WorldWideScience

Sample records for wearable body sensor

  1. Wearable computing from modeling to implementation of wearable systems based on body sensor networks

    CERN Document Server

    Fortino, Giancarlo; Galzarano, Stefano

    2018-01-01

    This book provides the most up-to-date research and development on wearable computing, wireless body sensor networks, wearable systems integrated with mobile computing, wireless networking and cloud computing. This book has a specific focus on advanced methods for programming Body Sensor Networks (BSNs) based on the reference SPINE project. It features an on-line website (http://spine.deis.unical.it) to support readers in developing their own BSN application/systems and covers new emerging topics on BSNs such as collaborative BSNs, BSN design methods, autonomic BSNs, integration of BSNs and pervasive environments, and integration of BSNs with cloud computing. The book provides a description of real BSN prototypes with the possibility to see on-line demos and download the software to test them on specific sensor platforms and includes case studies for more practical applications. * Provides a future roadmap by learning advanced technology and open research issues * Gathers the background knowledge to tackl...

  2. A triboelectric motion sensor in wearable body sensor network for human activity recognition.

    Science.gov (United States)

    Hui Huang; Xian Li; Ye Sun

    2016-08-01

    The goal of this study is to design a novel triboelectric motion sensor in wearable body sensor network for human activity recognition. Physical activity recognition is widely used in well-being management, medical diagnosis and rehabilitation. Other than traditional accelerometers, we design a novel wearable sensor system based on triboelectrification. The triboelectric motion sensor can be easily attached to human body and collect motion signals caused by physical activities. The experiments are conducted to collect five common activity data: sitting and standing, walking, climbing upstairs, downstairs, and running. The k-Nearest Neighbor (kNN) clustering algorithm is adopted to recognize these activities and validate the feasibility of this new approach. The results show that our system can perform physical activity recognition with a successful rate over 80% for walking, sitting and standing. The triboelectric structure can also be used as an energy harvester for motion harvesting due to its high output voltage in random low-frequency motion.

  3. Location verification algorithm of wearable sensors for wireless body area networks.

    Science.gov (United States)

    Wang, Hua; Wen, Yingyou; Zhao, Dazhe

    2018-01-01

    Knowledge of the location of sensor devices is crucial for many medical applications of wireless body area networks, as wearable sensors are designed to monitor vital signs of a patient while the wearer still has the freedom of movement. However, clinicians or patients can misplace the wearable sensors, thereby causing a mismatch between their physical locations and their correct target positions. An error of more than a few centimeters raises the risk of mistreating patients. The present study aims to develop a scheme to calculate and detect the position of wearable sensors without beacon nodes. A new scheme was proposed to verify the location of wearable sensors mounted on the patient's body by inferring differences in atmospheric air pressure and received signal strength indication measurements from wearable sensors. Extensive two-sample t tests were performed to validate the proposed scheme. The proposed scheme could easily recognize a 30-cm horizontal body range and a 65-cm vertical body range to correctly perform sensor localization and limb identification. All experiments indicate that the scheme is suitable for identifying wearable sensor positions in an indoor environment.

  4. High quantum efficiency annular backside silicon photodiodes for reflectance pulse oximetry in wearable wireless body sensors

    DEFF Research Database (Denmark)

    Duun, Sune Bro; Haahr, Rasmus Grønbek; Hansen, Ole

    2010-01-01

    The development of annular photodiodes for use in a reflectance pulse oximetry sensor is presented. Wearable and wireless body sensor systems for long-term monitoring require sensors that minimize power consumption. We have fabricated large area 2D ring-shaped silicon photodiodes optimized...

  5. A Wearable Respiratory Biofeedback System Based on Generalized Body Sensor Network

    Science.gov (United States)

    Liu, Guan-Zheng; Huang, Bang-Yu

    2011-01-01

    Abstract Wearable medical devices have enabled unobtrusive monitoring of vital signs and emerging biofeedback services in a pervasive manner. This article describes a wearable respiratory biofeedback system based on a generalized body sensor network (BSN) platform. The compact BSN platform was tailored for the strong requirements of overall system optimizations. A waist-worn biofeedback device was designed using the BSN. Extensive bench tests have shown that the generalized BSN worked as intended. In-situ experiments with 22 subjects indicated that the biofeedback device was discreet, easy to wear, and capable of offering wearable respiratory trainings. Pilot studies on wearable training patterns and resultant heart rate variability suggested that paced respirations at abdominal level and with identical inhaling/exhaling ratio were more appropriate for decreasing sympathetic arousal and increasing parasympathetic activities. PMID:21545293

  6. Wearable sensors for health monitoring

    Science.gov (United States)

    Suciu, George; Butca, Cristina; Ochian, Adelina; Halunga, Simona

    2015-02-01

    In this paper we describe several wearable sensors, designed for monitoring the health condition of the patients, based on an experimental model. Wearable sensors enable long-term continuous physiological monitoring, which is important for the treatment and management of many chronic illnesses, neurological disorders, and mental health issues. The system is based on a wearable sensors network, which is connected to a computer or smartphone. The wearable sensor network integrates several wearable sensors that can measure different parameters such as body temperature, heart rate and carbon monoxide quantity from the air. After the portable sensors measuring parameter values, they are transmitted by microprocessor through the Bluetooth to the application developed on computer or smartphone, to be interpreted.

  7. Wearable Optical Sensors

    KAUST Repository

    Ballard, Zachary S.; Ozcan, Aydogan

    2017-01-01

    The market for wearable sensors is predicted to grow to $5.5 billion by 2025, impacting global health in unprecedented ways. Optics and photonics will play a key role in the future of these wearable technologies, enabling highly sensitive

  8. Wearable sensors in intelligent clothing for measuring human body temperature based on optical fiber Bragg grating.

    Science.gov (United States)

    Li, Hongqiang; Yang, Haijing; Li, Enbang; Liu, Zhihui; Wei, Kejia

    2012-05-21

    Measuring body temperature is considerably important to physiological studies as well as clinical investigations. In recent years, numerous observations have been reported and various methods of measurement have been employed. The present paper introduces a novel wearable sensor in intelligent clothing for human body temperature measurement. The objective is the integration of optical fiber Bragg grating (FBG)-based sensors into functional textiles to extend the capabilities of wearable solutions for body temperature monitoring. In addition, the temperature sensitivity is 150 pm/°C, which is almost 15 times higher than that of a bare FBG. This study combines large and small pipes during fabrication to implant FBG sensors into the fabric. The law of energy conservation of the human body is considered in determining heat transfer between the body and its clothing. The mathematical model of heat transmission between the body and clothed FBG sensors is studied, and the steady-state thermal analysis is presented. The simulation results show the capability of the material to correct the actual body temperature. Based on the skin temperature obtained by the weighted average method, this paper presents the five points weighted coefficients model using both sides of the chest, armpits, and the upper back for the intelligent clothing. The weighted coefficients of 0.0826 for the left chest, 0.3706 for the left armpit, 0.3706 for the right armpit, 0.0936 for the upper back, and 0.0826 for the right chest were obtained using Cramer's Rule. Using the weighting coefficient, the deviation of the experimental result was ± 0.18 °C, which favors the use for clinical armpit temperature monitoring. Moreover, in special cases when several FBG sensors are broken, the weighted coefficients of the other sensors could be changed to obtain accurate body temperature.

  9. An Anonymous Mutual Authenticated Key Agreement Scheme for Wearable Sensors in Wireless Body Area Networks

    Directory of Open Access Journals (Sweden)

    Chien-Ming Chen

    2018-07-01

    Full Text Available The advancement of Wireless Body Area Networks (WBAN have led to significant progress in medical and health care systems. However, such networks still suffer from major security and privacy threats, especially for the data collected in medical or health care applications. Lack of security and existence of anonymous communication in WBAN brings about the operation failure of these networks. Recently, Li et al. proposed a lightweight protocol for wearable sensors in wireless body area networks. In their paper, the authors claimed that the protocol may provide anonymous mutual authentication and resist against various types of attacks. This study shows that such a protocol is still vulnerable to three types of attacks, i.e., the offline identity guessing attack, the sensor node impersonation attack and the hub node spoofing attack. We then present a secure scheme that addresses these problems, and retains similar efficiency in wireless sensors nodes and mobile phones.

  10. Wearable Flexible Sensors: A Review

    KAUST Repository

    Nag, Anindya

    2017-05-18

    The paper provides a review on some of the significant research work done on wearable flexible sensors (WFS). Sensors fabricated with flexible materials have been attached to a person along with the embedded system to monitor a parameter and transfer the significant data to the monitoring unit for further analyses. The use of wearable sensors has played a quite important role to monitor physiological parameters of a person to minimize any malfunctioning happening in the body. The paper categorizes the work according to the materials used for designing the system, the network protocols and different types of activities that were being monitored. The challenges faced by the current sensing systems and future opportunities for the wearable flexible sensors regarding its market values are also briefly explained in the paper.

  11. Wearable Flexible Sensors: A Review

    KAUST Repository

    Nag, Anindya; Mukhopadhyay, Subhas Chandra; Kosel, Jü rgen

    2017-01-01

    The paper provides a review on some of the significant research work done on wearable flexible sensors (WFS). Sensors fabricated with flexible materials have been attached to a person along with the embedded system to monitor a parameter and transfer the significant data to the monitoring unit for further analyses. The use of wearable sensors has played a quite important role to monitor physiological parameters of a person to minimize any malfunctioning happening in the body. The paper categorizes the work according to the materials used for designing the system, the network protocols and different types of activities that were being monitored. The challenges faced by the current sensing systems and future opportunities for the wearable flexible sensors regarding its market values are also briefly explained in the paper.

  12. Nanomaterial-Enabled Wearable Sensors for Healthcare.

    Science.gov (United States)

    Yao, Shanshan; Swetha, Puchakayala; Zhu, Yong

    2018-01-01

    Highly sensitive wearable sensors that can be conformably attached to human skin or integrated with textiles to monitor the physiological parameters of human body or the surrounding environment have garnered tremendous interest. Owing to the large surface area and outstanding material properties, nanomaterials are promising building blocks for wearable sensors. Recent advances in the nanomaterial-enabled wearable sensors including temperature, electrophysiological, strain, tactile, electrochemical, and environmental sensors are presented in this review. Integration of multiple sensors for multimodal sensing and integration with other components into wearable systems are summarized. Representative applications of nanomaterial-enabled wearable sensors for healthcare, including continuous health monitoring, daily and sports activity tracking, and multifunctional electronic skin are highlighted. Finally, challenges, opportunities, and future perspectives in the field of nanomaterial-enabled wearable sensors are discussed. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  13. Wearable Sensor Systems for Infants

    Directory of Open Access Journals (Sweden)

    Zhihua Zhu

    2015-02-01

    Full Text Available Continuous health status monitoring of infants is achieved with the development and fusion of wearable sensing technologies, wireless communication techniques and a low energy-consumption microprocessor with high performance data processing algorithms. As a clinical tool applied in the constant monitoring of physiological parameters of infants, wearable sensor systems for infants are able to transmit the information obtained inside an infant’s body to clinicians or parents. Moreover, such systems with integrated sensors can perceive external threats such as falling or drowning and warn parents immediately. Firstly, the paper reviews some available wearable sensor systems for infants; secondly, we introduce the different modules of the framework in the sensor systems; lastly, the methods and techniques applied in the wearable sensor systems are summarized and discussed. The latest research and achievements have been highlighted in this paper and the meaningful applications in healthcare and behavior analysis are also presented. Moreover, we give a lucid perspective of the development of wearable sensor systems for infants in the future.

  14. Synchronous wearable wireless body sensor network composed of autonomous textile nodes.

    Science.gov (United States)

    Vanveerdeghem, Peter; Van Torre, Patrick; Stevens, Christiaan; Knockaert, Jos; Rogier, Hendrik

    2014-10-09

    A novel, fully-autonomous, wearable, wireless sensor network is presented, where each flexible textile node performs cooperative synchronous acquisition and distributed event detection. Computationally efficient situational-awareness algorithms are implemented on the low-power microcontroller present on each flexible node. The detected events are wirelessly transmitted to a base station, directly, as well as forwarded by other on-body nodes. For each node, a dual-polarized textile patch antenna serves as a platform for the flexible electronic circuitry. Therefore, the system is particularly suitable for comfortable and unobtrusive integration into garments. In the meantime, polarization diversity can be exploited to improve the reliability and energy-efficiency of the wireless transmission. Extensive experiments in realistic conditions have demonstrated that this new autonomous, body-centric, textile-antenna, wireless sensor network is able to correctly detect different operating conditions of a firefighter during an intervention. By relying on four network nodes integrated into the protective garment, this functionality is implemented locally, on the body, and in real time. In addition, the received sensor data are reliably transferred to a central access point at the command post, for more detailed and more comprehensive real-time visualization. This information provides coordinators and commanders with situational awareness of the entire rescue operation. A statistical analysis of measured on-body node-to-node, as well as off-body person-to-person channels is included, confirming the reliability of the communication system.

  15. Wearable Optical Sensors

    KAUST Repository

    Ballard, Zachary S.

    2017-07-12

    The market for wearable sensors is predicted to grow to $5.5 billion by 2025, impacting global health in unprecedented ways. Optics and photonics will play a key role in the future of these wearable technologies, enabling highly sensitive measurements of otherwise invisible information and parameters about our health and surrounding environment. Through the implementation of optical wearable technologies, such as heart rate, blood pressure, and glucose monitors, among others, individuals are becoming more empowered to generate a wealth of rich, multifaceted physiological and environmental data, making personalized medicine a reality. Furthermore, these technologies can also be implemented in hospitals, clinics, point-of-care offices, assisted living facilities or even in patients’ homes for real-time, remote patient monitoring, creating more expeditious as well as resource-efficient systems. Several key optical technologies make such sensors possible, including e.g., optical fiber textiles, colorimetric, plasmonic, and fluorometric sensors, as well as Organic Light Emitting Diode (OLED) and Organic Photo-Diode (OPD) technologies. These emerging technologies and platforms show great promise as basic sensing elements in future wearable devices and will be reviewed in this chapter along-side currently existing fully integrated wearable optical sensors.

  16. Gait Analysis Using Wearable Sensors

    Science.gov (United States)

    Tao, Weijun; Liu, Tao; Zheng, Rencheng; Feng, Hutian

    2012-01-01

    Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications. PMID:22438763

  17. Gait Analysis Using Wearable Sensors

    Directory of Open Access Journals (Sweden)

    Hutian Feng

    2012-02-01

    Full Text Available Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications.

  18. High quantum efficiency annular backside silicon photodiodes for reflectance pulse oximetry in wearable wireless body sensors

    International Nuclear Information System (INIS)

    Duun, Sune; Haahr, Rasmus G; Hansen, Ole; Birkelund, Karen; Thomsen, Erik V

    2010-01-01

    The development of annular photodiodes for use in a reflectance pulse oximetry sensor is presented. Wearable and wireless body sensor systems for long-term monitoring require sensors that minimize power consumption. We have fabricated large area 2D ring-shaped silicon photodiodes optimized for minimizing the optical power needed in reflectance pulse oximetry. To simplify packaging, backside photodiodes are made which are compatible with assembly using surface mounting technology without pre-packaging. Quantum efficiencies up to 95% and area-specific noise equivalent powers down to 30 fW Hz -1/2 cm -1 are achieved. The photodiodes are incorporated into a wireless pulse oximetry sensor system embedded in an adhesive patch presented elsewhere as 'The Electronic Patch'. The annular photodiodes are fabricated using two masked diffusions of first boron and subsequently phosphor. The surface is passivated with a layer of silicon nitride also serving as an optical filter. As the final process, after metallization, a hole in the center of the photodiode is etched using deep reactive ion etch.

  19. Ambient Intelligence and Wearable Computing: Sensors on the Body, in the Home, and Beyond

    OpenAIRE

    Cook, Diane J.; Song, WenZhan

    2009-01-01

    Ambient intelligence has a history of focusing on technologies that are integrated into a person’s environment. However, ambient intelligence can be found on a person’s body as well. In this thematic issue we examine the role of wearable computing in the field of ambient intelligence. In this article we provide an overview of the field of wearable computing and discuss its relationship to the fields of smart environments and ambient intelligence. In addition, we introduce the papers presented...

  20. Wearable wireless photoplethysmography sensors

    Science.gov (United States)

    Spigulis, Janis; Erts, Renars; Nikiforovs, Vladimirs; Kviesis-Kipge, Edgars

    2008-04-01

    Wearable health monitoring sensors may support early detection of abnormal conditions and prevention of their consequences. Recent designs of three wireless photoplethysmography monitoring devices embedded in hat, glove and sock, and connected to PC or mobile phone by means of the Bluetooth technology, are described. First results of distant monitoring of heart rate and pulse wave transit time using the newly developed devices are presented.

  1. Wearable bio and chemical sensors

    OpenAIRE

    Coyle, Shirley; Curto, Vincenzo F.; Benito-Lopez, Fernando; Florea, Larisa; Diamond, Dermot

    2014-01-01

    Chemical and biochemical sensors have experienced tremendous growth in the past decade due to advances in material chemistry combined with the emergence of digital communication technologies and wireless sensor networks (WSNs) [1]. The emergence of wearable chemical and biochemical sensors is a relatively new concept that poses unique challenges to the field of wearable sensing. This is because chemical sensors have a more complex mode of operation, compared to physical transducers, in that t...

  2. An Analysis on Sensor Locations of the Human Body for Wearable Fall Detection Devices: Principles and Practice

    Directory of Open Access Journals (Sweden)

    Ahmet Turan Özdemir

    2016-07-01

    Full Text Available Wearable devices for fall detection have received attention in academia and industry, because falls are very dangerous, especially for elderly people, and if immediate aid is not provided, it may result in death. However, some predictive devices are not easily worn by elderly people. In this work, a huge dataset, including 2520 tests, is employed to determine the best sensor placement location on the body and to reduce the number of sensor nodes for device ergonomics. During the tests, the volunteer’s movements are recorded with six groups of sensors each with a triaxial (accelerometer, gyroscope and magnetometer sensor, which is placed tightly on different parts of the body with special straps: head, chest, waist, right-wrist, right-thigh and right-ankle. The accuracy of individual sensor groups with their location is investigated with six machine learning techniques, namely the k-nearest neighbor (k-NN classifier, Bayesian decision making (BDM, support vector machines (SVM, least squares method (LSM, dynamic time warping (DTW and artificial neural networks (ANNs. Each technique is applied to single, double, triple, quadruple, quintuple and sextuple sensor configurations. These configurations create 63 different combinations, and for six machine learning techniques, a total of 63 × 6 = 378 combinations is investigated. As a result, the waist region is found to be the most suitable location for sensor placement on the body with 99.96% fall detection sensitivity by using the k-NN classifier, whereas the best sensitivity achieved by the wrist sensor is 97.37%, despite this location being highly preferred for today’s wearable applications.

  3. An Analysis on Sensor Locations of the Human Body for Wearable Fall Detection Devices: Principles and Practice.

    Science.gov (United States)

    Özdemir, Ahmet Turan

    2016-07-25

    Wearable devices for fall detection have received attention in academia and industry, because falls are very dangerous, especially for elderly people, and if immediate aid is not provided, it may result in death. However, some predictive devices are not easily worn by elderly people. In this work, a huge dataset, including 2520 tests, is employed to determine the best sensor placement location on the body and to reduce the number of sensor nodes for device ergonomics. During the tests, the volunteer's movements are recorded with six groups of sensors each with a triaxial (accelerometer, gyroscope and magnetometer) sensor, which is placed tightly on different parts of the body with special straps: head, chest, waist, right-wrist, right-thigh and right-ankle. The accuracy of individual sensor groups with their location is investigated with six machine learning techniques, namely the k-nearest neighbor (k-NN) classifier, Bayesian decision making (BDM), support vector machines (SVM), least squares method (LSM), dynamic time warping (DTW) and artificial neural networks (ANNs). Each technique is applied to single, double, triple, quadruple, quintuple and sextuple sensor configurations. These configurations create 63 different combinations, and for six machine learning techniques, a total of 63 × 6 = 378 combinations is investigated. As a result, the waist region is found to be the most suitable location for sensor placement on the body with 99.96% fall detection sensitivity by using the k-NN classifier, whereas the best sensitivity achieved by the wrist sensor is 97.37%, despite this location being highly preferred for today's wearable applications.

  4. Wearable Sensor Networks for Motion Capture

    Directory of Open Access Journals (Sweden)

    Dennis Arsenault

    2015-08-01

    Full Text Available This work presents the development of a full body sensor-based motion tracking system that functions through wearable inertial sensors. The system is comprised of a total of ten wearable sensors and maps the player's motions to an on-screen character in real-time. A hierarchical skeletal model was implemented that allows players to navigate and interact with the virtual world without the need of a hand-held controller. To demonstrate the capabilities of the system, a simple virtual reality game was created. As a wearable system, the ability for the users to engage in activities while not being tied to a camera system, or being forced indoors presents a significant opportunity for mobile entertainment, augmented reality and interactive systems that use the body as a significant form of input. This paper outlines the key developments necessary to implement such a system.

  5. Wearable sensors: modalities, challenges, and prospects.

    Science.gov (United States)

    Heikenfeld, J; Jajack, A; Rogers, J; Gutruf, P; Tian, L; Pan, T; Li, R; Khine, M; Kim, J; Wang, J; Kim, J

    2018-01-16

    Wearable sensors have recently seen a large increase in both research and commercialization. However, success in wearable sensors has been a mix of both progress and setbacks. Most of commercial progress has been in smart adaptation of existing mechanical, electrical and optical methods of measuring the body. This adaptation has involved innovations in how to miniaturize sensing technologies, how to make them conformal and flexible, and in the development of companion software that increases the value of the measured data. However, chemical sensing modalities have experienced greater challenges in commercial adoption, especially for non-invasive chemical sensors. There have also been significant challenges in making significant fundamental improvements to existing mechanical, electrical, and optical sensing modalities, especially in improving their specificity of detection. Many of these challenges can be understood by appreciating the body's surface (skin) as more of an information barrier than as an information source. With a deeper understanding of the fundamental challenges faced for wearable sensors and of the state-of-the-art for wearable sensor technology, the roadmap becomes clearer for creating the next generation of innovations and breakthroughs.

  6. Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks

    Directory of Open Access Journals (Sweden)

    Chao Li

    2017-12-01

    Full Text Available In wireless sensor networks, sensor nodes collect plenty of data for each time period. If all of data are transmitted to a Fusion Center (FC, the power of sensor node would run out rapidly. On the other hand, the data also needs a filter to remove the noise. Therefore, an efficient fusion estimation model, which can save the energy of the sensor nodes while maintaining higher accuracy, is needed. This paper proposes a novel mixed H2/H∞-based energy-efficient fusion estimation model (MHEEFE for energy-limited Wearable Body Networks. In the proposed model, the communication cost is firstly reduced efficiently while keeping the estimation accuracy. Then, the parameters in quantization method are discussed, and we confirm them by an optimization method with some prior knowledge. Besides, some calculation methods of important parameters are researched which make the final estimates more stable. Finally, an iteration-based weight calculation algorithm is presented, which can improve the fault tolerance of the final estimate. In the simulation, the impacts of some pivotal parameters are discussed. Meanwhile, compared with the other related models, the MHEEFE shows a better performance in accuracy, energy-efficiency and fault tolerance.

  7. Wearable sensors fundamentals, implementation and applications

    CERN Document Server

    Sazonov, Edward

    2014-01-01

    Written by industry experts, this book aims to provide you with an understanding of how to design and work with wearable sensors. Together these insights provide the first single source of information on wearable sensors that would be a valuable addition to the library of any engineer interested in this field. Wearable Sensors covers a wide variety of topics associated with the development and application of various wearable sensors. It also provides an overview and coherent summary of many aspects of current wearable sensor technology. Both industry professionals and academic researcher

  8. The best body spot to detect the vital capacity from the respiratory movement data obtained by the wearable strain sensor.

    Science.gov (United States)

    Liu, Haijuan; Guo, Shaopeng; Liu, Huilin; Zhang, Hao; Chen, Sujie; Kuramoto-Ahuja, Tsugumi; Sato, Tamae; Kaneko, Junichiro; Onoda, Ko; Maruyama, Hitoshi

    2018-04-01

    [Purpose] The purpose of this study is to find the best body spots on the chest and abdomen wall to obtain the correlated indicators to the vital capacity. [Subjects and Methods] Thirty healthy male staff of the center served as the participants were advised to conduct a breathing movement using spirometer and a wearable strain sensor (WSS) respectively, which was the measured at four spots on chest and abdomen wall from maximal end of inspiration to maximal end of expiration. The Pearson's correlation analysis was conducted to find the correlation of the data obtained respectively by the WSS and spirometer. [Results] The correlation of the mobility data at the four body spots to the vital capacity data were calculated for each level by means of Pearson's correlation coefficient, which showed that the values at each body spot were positive significant correlations and the highest value was at the 10th rib. [Conclusion] There was a correlation between the mobility data of the chest and abdomen obtained by the WSS and the vital capacity data obtained by the spirometer, for which, the 10th rib is the best body spot to detect the positive significant correlation.

  9. Activity Recognition Invariant to Sensor Orientation with Wearable Motion Sensors.

    Science.gov (United States)

    Yurtman, Aras; Barshan, Billur

    2017-08-09

    Most activity recognition studies that employ wearable sensors assume that the sensors are attached at pre-determined positions and orientations that do not change over time. Since this is not the case in practice, it is of interest to develop wearable systems that operate invariantly to sensor position and orientation. We focus on invariance to sensor orientation and develop two alternative transformations to remove the effect of absolute sensor orientation from the raw sensor data. We test the proposed methodology in activity recognition with four state-of-the-art classifiers using five publicly available datasets containing various types of human activities acquired by different sensor configurations. While the ordinary activity recognition system cannot handle incorrectly oriented sensors, the proposed transformations allow the sensors to be worn at any orientation at a given position on the body, and achieve nearly the same activity recognition performance as the ordinary system for which the sensor units are not rotatable. The proposed techniques can be applied to existing wearable systems without much effort, by simply transforming the time-domain sensor data at the pre-processing stage.

  10. Assessment of Wearable Sensor Technologies for Biosurveillance

    Science.gov (United States)

    2014-11-01

    include: textile-based wearable sensors, epidermal tattoos, DNA and protein sensors, forensic detection of explosives, remote environmental sensing...Assessment of Wearable Sensor Technologies for Biosurveillance P a g e 4 3 David L. Hirschberg, PhD Assistant Professor, Clinical Pathology

  11. Wireless Fidelity Electromagnetic Field Exposure Monitoring With Wearable Body Sensor Networks.

    Science.gov (United States)

    Lecoutere, Jeroen; Thielens, Arno; Agneessens, Sam; Rogier, Hendrik; Joseph, Wout; Puers, Robert

    2016-06-01

    With the breakthrough of the Internet of Things and the steady increase of wireless applications in the daily environment, the assessment of radio frequency electromagnetic field (RF-EMF) exposure is key in determining possible health effects of exposure to certain levels of RF-EMF. This paper presents the first experimental validation of a novel personal exposimeter system based on a distributed measurement approach to achieve higher measurement quality and lower measurement variability than the commonly used single point measurement approach of existing exposimeters. An important feature of the system is the integration of inertial sensors in order to determine activity and posture during exposure measurements. The system is designed to assess exposure to frequencies within the 389 to 464, 779 to 928 and 2400 to 2483.5 MHz bands using only two transceivers per node. In this study, the 2400 to 2483.5 MHz band is validated. Every node provides antenna diversity for the different bands in order to achieve higher sensitivity at these frequencies. Two AAA batteries power each standalone node and as such determine the node hardware size of this proof of concept (53 mm×25 mm×15 mm) , making it smaller than any other commercially available exposimeter.

  12. Ergonomic analysis of construction worker's body postures using wearable mobile sensors.

    Science.gov (United States)

    Nath, Nipun D; Akhavian, Reza; Behzadan, Amir H

    2017-07-01

    Construction jobs are more labor-intensive compared to other industries. As such, construction workers are often required to exceed their natural physical capability to cope with the increasing complexity and challenges in this industry. Over long periods of time, this sustained physical labor causes bodily injuries to the workers which in turn, conveys huge losses to the industry in terms of money, time, and productivity. Various safety and health organizations have established rules and regulations that limit the amount and intensity of workers' physical movements to mitigate work-related bodily injuries. A precursor to enforcing and implementing such regulations and improving the ergonomics conditions on the jobsite is to identify physical risks associated with a particular task. Manually assessing a field activity to identify the ergonomic risks is not trivial and often requires extra effort which may render it to be challenging if not impossible. In this paper, a low-cost ubiquitous approach is presented and validated which deploys built-in smartphone sensors to unobtrusively monitor workers' bodily postures and autonomously identify potential work-related ergonomic risks. Results indicates that measurements of trunk and shoulder flexions of a worker by smartphone sensory data are very close to corresponding measurements by observation. The proposed method is applicable for workers in various occupations who are exposed to WMSDs due to awkward postures. Examples include, but are not limited to industry laborers, carpenters, welders, farmers, health assistants, teachers, and office workers. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Physical Human Activity Recognition Using Wearable Sensors.

    Science.gov (United States)

    Attal, Ferhat; Mohammed, Samer; Dedabrishvili, Mariam; Chamroukhi, Faicel; Oukhellou, Latifa; Amirat, Yacine

    2015-12-11

    This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors' placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.

  14. Wearable sensors for human health monitoring

    Science.gov (United States)

    Asada, H. Harry; Reisner, Andrew

    2006-03-01

    Wearable sensors for continuous monitoring of vital signs for extended periods of weeks or months are expected to revolutionize healthcare services in the home and workplace as well as in hospitals and nursing homes. This invited paper describes recent research progress in wearable health monitoring technology and its clinical applications, with emphasis on blood pressure and circulatory monitoring. First, a finger ring-type wearable blood pressure sensor based on photo plethysmogram is presented. Technical issues, including motion artifact reduction, power saving, and wearability enhancement, will be addressed. Second, sensor fusion and sensor networking for integrating multiple sensors with diverse modalities will be discussed for comprehensive monitoring and diagnosis of health status. Unlike traditional snap-shot measurements, continuous monitoring with wearable sensors opens up the possibility to treat the physiological system as a dynamical process. This allows us to apply powerful system dynamics and control methodologies, such as adaptive filtering, single- and multi-channel system identification, active noise cancellation, and adaptive control, to the monitoring and treatment of highly complex physiological systems. A few clinical trials illustrate the potentials of the wearable sensor technology for future heath care services.

  15. Physical Human Activity Recognition Using Wearable Sensors

    Directory of Open Access Journals (Sweden)

    Ferhat Attal

    2015-12-01

    Full Text Available This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle. Three main steps describe the activity recognition process: sensors’ placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN, Support Vector Machines (SVM, Gaussian Mixture Models (GMM, and Random Forest (RF as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM and Hidden Markov Model (HMM, are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.

  16. Wearable and Implantable Wireless Sensor Network Solutions for Healthcare Monitoring

    OpenAIRE

    Darwish, Ashraf; Hassanien, Aboul Ella

    2011-01-01

    Wireless sensor network (WSN) technologies are considered one of the key research areas in computer science and the healthcare application industries for improving the quality of life. The purpose of this paper is to provide a snapshot of current developments and future direction of research on wearable and implantable body area network systems for continuous monitoring of patients. This paper explains the important role of body sensor networks in medicine to minimize the need for caregivers ...

  17. Smart jacket design for neonatal monitoring with wearable sensors

    NARCIS (Netherlands)

    Bouwstra, S.; Chen, W.; Feijs, L.M.G.; Bambang Oetomo, S.

    2009-01-01

    Critically ill new born babies admitted at the Neonatal Intensive Care Unit (NICU) are extremely tiny and vulnerable to external disturbance. Smart Jacket proposed in this paper is the vision of a wearable unobtrusive continuous monitoring system realized by body sensor networks (BSN) and wireless

  18. Gait Recognition Using Wearable Motion Recording Sensors

    Directory of Open Access Journals (Sweden)

    Davrondzhon Gafurov

    2009-01-01

    Full Text Available This paper presents an alternative approach, where gait is collected by the sensors attached to the person's body. Such wearable sensors record motion (e.g. acceleration of the body parts during walking. The recorded motion signals are then investigated for person recognition purposes. We analyzed acceleration signals from the foot, hip, pocket and arm. Applying various methods, the best EER obtained for foot-, pocket-, arm- and hip- based user authentication were 5%, 7%, 10% and 13%, respectively. Furthermore, we present the results of our analysis on security assessment of gait. Studying gait-based user authentication (in case of hip motion under three attack scenarios, we revealed that a minimal effort mimicking does not help to improve the acceptance chances of impostors. However, impostors who know their closest person in the database or the genders of the users can be a threat to gait-based authentication. We also provide some new insights toward the uniqueness of gait in case of foot motion. In particular, we revealed the following: a sideway motion of the foot provides the most discrimination, compared to an up-down or forward-backward directions; and different segments of the gait cycle provide different level of discrimination.

  19. High Sensitivity, Wearable, Piezoresistive Pressure Sensors Based on Irregular Microhump Structures and Its Applications in Body Motion Sensing.

    Science.gov (United States)

    Wang, Zongrong; Wang, Shan; Zeng, Jifang; Ren, Xiaochen; Chee, Adrian J Y; Yiu, Billy Y S; Chung, Wai Choi; Yang, Yong; Yu, Alfred C H; Roberts, Robert C; Tsang, Anderson C O; Chow, Kwok Wing; Chan, Paddy K L

    2016-07-01

    A pressure sensor based on irregular microhump patterns has been proposed and developed. The devices show high sensitivity and broad operating pressure regime while comparing with regular micropattern devices. Finite element analysis (FEA) is utilized to confirm the sensing mechanism and predict the performance of the pressure sensor based on the microhump structures. Silicon carbide sandpaper is employed as the mold to develop polydimethylsiloxane (PDMS) microhump patterns with various sizes. The active layer of the piezoresistive pressure sensor is developed by spin coating PSS on top of the patterned PDMS. The devices show an averaged sensitivity as high as 851 kPa(-1) , broad operating pressure range (20 kPa), low operating power (100 nW), and fast response speed (6.7 kHz). Owing to their flexible properties, the devices are applied to human body motion sensing and radial artery pulse. These flexible high sensitivity devices show great potential in the next generation of smart sensors for robotics, real-time health monitoring, and biomedical applications. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  20. Wearable Fall Detector using Integrated Sensors and Energy Devices

    Science.gov (United States)

    Jung, Sungmook; Hong, Seungki; Kim, Jaemin; Lee, Sangkyu; Hyeon, Taeghwan; Lee, Minbaek; Kim, Dae-Hyeong

    2015-11-01

    Wearable devices have attracted great attentions as next-generation electronic devices. For the comfortable, portable, and easy-to-use system platform in wearable electronics, a key requirement is to replace conventional bulky and rigid energy devices into thin and deformable ones accompanying the capability of long-term energy supply. Here, we demonstrate a wearable fall detection system composed of a wristband-type deformable triboelectric generator and lithium ion battery in conjunction with integrated sensors, controllers, and wireless units. A stretchable conductive nylon is used as electrodes of the triboelectric generator and the interconnection between battery cells. Ethoxylated polyethylenimine, coated on the surface of the conductive nylon electrode, tunes the work function of a triboelectric generator and maximizes its performance. The electrical energy harvested from the triboelectric generator through human body motions continuously recharges the stretchable battery and prolongs hours of its use. The integrated energy supply system runs the 3-axis accelerometer and related electronics that record human body motions and send the data wirelessly. Upon the unexpected fall occurring, a custom-made software discriminates the fall signal and an emergency alert is immediately sent to an external mobile device. This wearable fall detection system would provide new opportunities in the mobile electronics and wearable healthcare.

  1. Wearable Sensors; Applications, design and implementation

    Science.gov (United States)

    Mukhopadhyay, Subhas Chandra; Islam, Tarikul

    2017-12-01

    With the ability to monitor a vast range of physiological parameters, combined with wireless technology, wireless sensor networks and the Internet of Things, wearable sensors are revolutionising the field of digital health monitoring. In addition to applications in health monitoring, such technology is being used to monitor the state of our living environment and even the quality of our foods and the wellbeing of livestock. Written for scientists, engineers and practitioners by an international collection of authors, this book reviews the fundamentals of wearable sensors, their function, design, fabrication and implementation. Their application and advanced aspects including interface electronics and signal processing for easy interpretation of data, data transmission, data networking, data security, and privacy are also included.

  2. Wearable PPG sensor based alertness scoring system.

    Science.gov (United States)

    Dey, Jishnu; Bhowmik, Tanmoy; Sahoo, Saswata; Tiwari, Vijay Narayan

    2017-07-01

    Quantifying mental alertness in today's world is important as it enables the person to adopt lifestyle changes for better work efficiency. Miniaturized sensors in wearable devices have facilitated detection/monitoring of mental alertness. Photoplethysmography (PPG) sensors through Heart Rate Variability (HRV) offer one such opportunity by providing information about one's daily alertness levels without requiring any manual interference from the user. In this paper, a smartwatch based alertness estimation system is proposed. Data collected from PPG sensor of smartwatch is processed and fed to machine learning based model to get a continuous alertness score. Utility functions are designed based on statistical analysis to give a quality score on different stages of alertness such as awake, long sleep and short duration power nap. An intelligent data collection approach is proposed in collaboration with the motion sensor in the smartwatch to reduce battery drainage. Overall, our proposed wearable based system provides a detailed analysis of alertness over a period in a systematic and optimized manner. We were able to achieve an accuracy of 80.1% for sleep/awake classification along with alertness score. This opens up the possibility for quantifying alertness levels using a single PPG sensor for better management of health related activities including sleep.

  3. A review of wearable sensors and systems with application in rehabilitation

    Directory of Open Access Journals (Sweden)

    Patel Shyamal

    2012-04-01

    Full Text Available Abstract The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community settings justifies the emphasis of this review paper on summarizing clinical applications of wearable technology currently undergoing assessment rather than describing the development of new wearable sensors and systems. A short description of key enabling technologies (i.e. sensor technology, communication technology, and data analysis techniques that have allowed researchers to implement wearable systems is followed by a detailed description of major areas of application of wearable technology. Applications described in this review paper include those that focus on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders. The integration of wearable and ambient sensors is discussed in the context of achieving home monitoring of older adults and subjects with chronic conditions. Future work required to advance the field toward clinical deployment of wearable sensors and systems is discussed.

  4. Flexible heartbeat sensor for wearable device.

    Science.gov (United States)

    Kwak, Yeon Hwa; Kim, Wonhyo; Park, Kwang Bum; Kim, Kunnyun; Seo, Sungkyu

    2017-08-15

    We demonstrate a flexible strain-gauge sensor and its use in a wearable application for heart rate detection. This polymer-based strain-gauge sensor was fabricated using a double-sided fabrication method with polymer and metal, i.e., polyimide and nickel-chrome. The fabrication process for this strain-gauge sensor is compatible with the conventional flexible printed circuit board (FPCB) processes facilitating its commercialization. The fabricated sensor showed a linear relation for an applied normal force of more than 930 kPa, with a minimum detectable force of 6.25Pa. This sensor can also linearly detect a bending radius from 5mm to 100mm. It is a thin, flexible, compact, and inexpensive (for mass production) heart rate detection sensor that is highly sensitive compared to the established optical photoplethysmography (PPG) sensors. It can detect not only the timing of heart pulsation, but also the amplitude or shape of the pulse signal. The proposed strain-gauge sensor can be applicable to various applications for smart devices requiring heartbeat detection. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. Wearable electronics sensors for safe and healthy living

    CERN Document Server

    2015-01-01

    This edited book contains invited papers from renowned experts working in the field of Wearable Electronics Sensors. It includes 14 chapters describing recent advancements in the area of Wearable Sensors, Wireless Sensors and Sensor Networks, Protocols, Topologies, Instrumentation architectures, Measurement techniques, Energy harvesting and scavenging, Signal processing, Design and Prototyping. The book will be useful for engineers, scientist and post-graduate students as a reference book for their research on wearable sensors, devices and technologies which is experiencing a period of rapid growth driven by new applications such as heart rate monitors, smart watches, tracking devices and smart glasses.  .

  6. Wearable and implantable wireless sensor network solutions for healthcare monitoring.

    Science.gov (United States)

    Darwish, Ashraf; Hassanien, Aboul Ella

    2011-01-01

    Wireless sensor network (WSN) technologies are considered one of the key research areas in computer science and the healthcare application industries for improving the quality of life. The purpose of this paper is to provide a snapshot of current developments and future direction of research on wearable and implantable body area network systems for continuous monitoring of patients. This paper explains the important role of body sensor networks in medicine to minimize the need for caregivers and help the chronically ill and elderly people live an independent life, besides providing people with quality care. The paper provides several examples of state of the art technology together with the design considerations like unobtrusiveness, scalability, energy efficiency, security and also provides a comprehensive analysis of the various benefits and drawbacks of these systems. Although offering significant benefits, the field of wearable and implantable body sensor networks still faces major challenges and open research problems which are investigated and covered, along with some proposed solutions, in this paper.

  7. Wearable and Implantable Wireless Sensor Network Solutions for Healthcare Monitoring

    Directory of Open Access Journals (Sweden)

    Ashraf Darwish

    2011-05-01

    Full Text Available Wireless sensor network (WSN technologies are considered one of the key research areas in computer science and the healthcare application industries for improving the quality of life. The purpose of this paper is to provide a snapshot of current developments and future direction of research on wearable and implantable body area network systems for continuous monitoring of patients. This paper explains the important role of body sensor networks in medicine to minimize the need for caregivers and help the chronically ill and elderly people live an independent life, besides providing people with quality care. The paper provides several examples of state of the art technology together with the design considerations like unobtrusiveness, scalability, energy efficiency, security and also provides a comprehensive analysis of the various benefits and drawbacks of these systems. Although offering significant benefits, the field of wearable and implantable body sensor networks still faces major challenges and open research problems which are investigated and covered, along with some proposed solutions, in this paper.

  8. Wearable and Implantable Wireless Sensor Network Solutions for Healthcare Monitoring

    Science.gov (United States)

    Darwish, Ashraf; Hassanien, Aboul Ella

    2011-01-01

    Wireless sensor network (WSN) technologies are considered one of the key research areas in computer science and the healthcare application industries for improving the quality of life. The purpose of this paper is to provide a snapshot of current developments and future direction of research on wearable and implantable body area network systems for continuous monitoring of patients. This paper explains the important role of body sensor networks in medicine to minimize the need for caregivers and help the chronically ill and elderly people live an independent life, besides providing people with quality care. The paper provides several examples of state of the art technology together with the design considerations like unobtrusiveness, scalability, energy efficiency, security and also provides a comprehensive analysis of the various benefits and drawbacks of these systems. Although offering significant benefits, the field of wearable and implantable body sensor networks still faces major challenges and open research problems which are investigated and covered, along with some proposed solutions, in this paper. PMID:22163914

  9. Wearable Sensors for Remote Health Monitoring.

    Science.gov (United States)

    Majumder, Sumit; Mondal, Tapas; Deen, M Jamal

    2017-01-12

    Life expectancy in most countries has been increasing continually over the several few decades thanks to significant improvements in medicine, public health, as well as personal and environmental hygiene. However, increased life expectancy combined with falling birth rates are expected to engender a large aging demographic in the near future that would impose significant  burdens on the socio-economic structure of these countries. Therefore, it is essential to develop cost-effective, easy-to-use systems for the sake of elderly healthcare and well-being. Remote health monitoring, based on non-invasive and wearable sensors, actuators and modern communication and information technologies offers an efficient and cost-effective solution that allows the elderly to continue to live in their comfortable home environment instead of expensive healthcare facilities. These systems will also allow healthcare personnel to monitor important physiological signs of their patients in real time, assess health conditions and provide feedback from distant facilities. In this paper, we have presented and compared several low-cost and non-invasive health and activity monitoring systems that were reported in recent years. A survey on textile-based sensors that can potentially be used in wearable systems is also presented. Finally, compatibility of several communication technologies as well as future perspectives and research challenges in remote monitoring systems will be discussed.

  10. Wearable Sensors for Remote Health Monitoring

    Directory of Open Access Journals (Sweden)

    Sumit Majumder

    2017-01-01

    Full Text Available Life expectancy in most countries has been increasing continually over the several few decades thanks to significant improvements in medicine, public health, as well as personal and environmental hygiene. However, increased life expectancy combined with falling birth rates are expected to engender a large aging demographic in the near future that would impose significant  burdens on the socio-economic structure of these countries. Therefore, it is essential to develop cost-effective, easy-to-use systems for the sake of elderly healthcare and well-being. Remote health monitoring, based on non-invasive and wearable sensors, actuators and modern communication and information technologies offers an efficient and cost-effective solution that allows the elderly to continue to live in their comfortable home environment instead of expensive healthcare facilities. These systems will also allow healthcare personnel to monitor important physiological signs of their patients in real time, assess health conditions and provide feedback from distant facilities. In this paper, we have presented and compared several low-cost and non-invasive health and activity monitoring systems that were reported in recent years. A survey on textile-based sensors that can potentially be used in wearable systems is also presented. Finally, compatibility of several communication technologies as well as future perspectives and research challenges in remote monitoring systems will be discussed.

  11. Flexible wearable sensor nodes with solar energy harvesting.

    Science.gov (United States)

    Taiyang Wu; Arefin, Md Shamsul; Redoute, Jean-Michel; Yuce, Mehmet Rasit

    2017-07-01

    Wearable sensor nodes have gained a lot of attention during the past few years as they can monitor and record people's physical parameters in real time. Wearable sensor nodes can promote healthy lifestyles and prevent the occurrence of potential illness or injuries. This paper presents a flexible wearable sensor system powered by an efficient solar energy harvesting technique. It can measure the subject's heartbeats using a photoplethysmography (PPG) sensor and perform activity monitoring using an accelerometer. The solar energy harvester adopts an output current based maximum power point tracking (MPPT) algorithm, which controls the solar panel to operate within its high output power range. The power consumption of the flexible sensor nodes has been investigated under different operation conditions. Experimental results demonstrate that wearable sensor nodes can work for more than 12 hours when they are powered by the solar energy harvester for 3 hours in the bright sunlight.

  12. Wearable and Implantable Sensors: The Patient’s Perspective

    Directory of Open Access Journals (Sweden)

    Alison McGregor

    2012-12-01

    Full Text Available There has been a rising interest in wearable and implantable biomedical sensors over the last decade. However, many technologies have not been integrated into clinical care, due to a limited understanding of user-centered design issues. Little information is available about these issues and there is a need to adopt more rigorous evidence standards for design features to allow important medical sensors to progress quicker into clinical care. Current trends in patient preferences need to be incorporated at an early stage into the design process of prospective clinical sensors. The first comprehensive patient data set, discussing mobile biomedical sensor technology, is presented in this paper. The study population mainly consisted of individuals suffering from arthritis. It was found that sensor systems needed to be small, discreet, unobtrusive and preferably incorporated into everyday objects. The upper extremity was seen as the favored position on the body for placement, while invasive placement yielded high levels of acceptance. Under these conditions most users were willing to wear the body-worn sensor for more than 20 h a day. This study is a first step to generate research based user-orientated design criteria’s for biomedical sensors.

  13. Ultra-low power and wearable CO2 sensors

    Data.gov (United States)

    National Aeronautics and Space Administration — IRIS architecture, nano chemical sensor, and e-textile antenna will be integrated/tested to make it wearable, mobile, peel-stick or fit where it is needed for...

  14. A Printed Organic Circuit System for Wearable Amperometric Electrochemical Sensors.

    Science.gov (United States)

    Shiwaku, Rei; Matsui, Hiroyuki; Nagamine, Kuniaki; Uematsu, Mayu; Mano, Taisei; Maruyama, Yuki; Nomura, Ayako; Tsuchiya, Kazuhiko; Hayasaka, Kazuma; Takeda, Yasunori; Fukuda, Takashi; Kumaki, Daisuke; Tokito, Shizuo

    2018-04-23

    Wearable sensor device technologies, which enable continuous monitoring of biological information from the human body, are promising in the fields of sports, healthcare, and medical applications. Further thinness, light weight, flexibility and low-cost are significant requirements for making the devices attachable onto human tissues or clothes like a patch. Here we demonstrate a flexible and printed circuit system consisting of an enzyme-based amperometric sensor, feedback control and amplification circuits based on organic thin-film transistors. The feedback control and amplification circuits based on pseudo-CMOS inverters were successfuly integrated by printing methods on a plastic film. This simple system worked very well like a potentiostat for electrochemical measurements, and enabled the quantitative and real-time measurement of lactate concentration with high sensitivity of 1 V/mM and a short response time of a hundred seconds.

  15. Wearable thermoelectric generators for body-powered devices

    NARCIS (Netherlands)

    Leonov, V.; Vullers, R.J.M.

    2009-01-01

    This paper presents a discussion on energy scavenging for wearable devices in conjunction with human body properties. Motivation, analysis of the relevant properties of the human body, and results of optimization of a thermopile and a thermoelectric generator for wearable and portable devices are

  16. Novel Flexible Wearable Sensor Materials and Signal Processing for Vital Sign and Human Activity Monitoring.

    Science.gov (United States)

    Servati, Amir; Zou, Liang; Wang, Z Jane; Ko, Frank; Servati, Peyman

    2017-07-13

    Advances in flexible electronic materials and smart textile, along with broad availability of smart phones, cloud and wireless systems have empowered the wearable technologies for significant impact on future of digital and personalized healthcare as well as consumer electronics. However, challenges related to lack of accuracy, reliability, high power consumption, rigid or bulky form factor and difficulty in interpretation of data have limited their wide-scale application in these potential areas. As an important solution to these challenges, we present latest advances in novel flexible electronic materials and sensors that enable comfortable and conformable body interaction and potential for invisible integration within daily apparel. Advances in novel flexible materials and sensors are described for wearable monitoring of human vital signs including, body temperature, respiratory rate and heart rate, muscle movements and activity. We then present advances in signal processing focusing on motion and noise artifact removal, data mining and aspects of sensor fusion relevant to future clinical applications of wearable technology.

  17. A Novel Wearable Electronic Nose for Healthcare Based on Flexible Printed Chemical Sensor Array

    Directory of Open Access Journals (Sweden)

    Panida Lorwongtragool

    2014-10-01

    Full Text Available A novel wearable electronic nose for armpit odor analysis is proposed by using a low-cost chemical sensor array integrated in a ZigBee wireless communication system. We report the development of a carbon nanotubes (CNTs/polymer sensor array based on inkjet printing technology. With this technique both composite-like layer and actual composite film of CNTs/polymer were prepared as sensing layers for the chemical sensor array. The sensor array can response to a variety of complex odors and is installed in a prototype of wearable e-nose for monitoring the axillary odor released from human body. The wearable e-nose allows the classification of different armpit odors and the amount of the volatiles released as a function of level of skin hygiene upon different activities.

  18. Multiobjective Design of Wearable Sensor Systems for Electrocardiogram Monitoring

    Directory of Open Access Journals (Sweden)

    F. J. Martinez-Tabares

    2016-01-01

    Full Text Available Wearable sensor systems will soon become part of the available medical tools for remote and long term physiological monitoring. However, the set of variables involved in the performance of these systems are usually antagonistic, and therefore the design of usable wearable systems in real clinical applications entails a number of challenges that have to be addressed first. This paper describes a method to optimise the design of these systems for the specific application of cardiac monitoring. The method proposed is based on the selection of a subset of 5 design variables, sensor contact, location, and rotation, signal correlation, and patient comfort, and 2 objective functions, functionality and wearability. These variables are optimised using linear and nonlinear models to maximise those objective functions simultaneously. The methodology described and the results achieved demonstrate that it is possible to find an optimal solution and therefore overcome most of the design barriers that prevent wearable sensor systems from being used in normal clinical practice.

  19. Unintended Consequences of Wearable Sensor Use in Healthcare

    Science.gov (United States)

    McCaldin, D.; Wang, K.; Schreier, G.; Lovell, N. H.; Marschollek, M.; Redmond, S. J.

    2016-01-01

    Summary Objectives As wearable sensors take the consumer market by storm, and medical device manufacturers move to make their devices wireless and appropriate for ambulatory use, this revolution brings with it some unintended consequences, which we aim to discuss in this paper. Methods We discuss some important unintended consequences, both beneficial and unwanted, which relate to: modifications of behavior; creation and use of big data sets; new security vulnerabilities; and unforeseen challenges faced by regulatory authorities, struggling to keep pace with recent innovations. Where possible, we proposed potential solutions to unwanted consequences. Results Intelligent and inclusive design processes may mitigate unintended modifications in behavior. For big data, legislating access to and use of these data will be a legal and political challenge in the years ahead, as we trade the health benefits of wearable sensors against the risk to our privacy. The wireless and personal nature of wearable sensors also exposes them to a number of unique security vulnerabilities. Regulation plays an important role in managing these security risks, but also has the dual responsibility of ensuring that wearable devices are fit for purpose. However, the burden of validating the function and security of medical devices is becoming infeasible for regulators, given the many software apps and wearable sensors entering the market each year, which are only a subset of an even larger ‘internet of things’. Conclusion Wearable sensors may serve to improve wellbeing, but we must be vigilant against the occurrence of unintended consequences. With collaboration between device manufacturers, regulators, and end-users, we balance the risk of unintended consequences occurring against the incredible benefit that wearable sensors promise to bring to the world. PMID:27830234

  20. A Method of Data Aggregation for Wearable Sensor Systems

    Directory of Open Access Journals (Sweden)

    Bo Shen

    2016-06-01

    Full Text Available Data aggregation has been considered as an effective way to decrease the data to be transferred in sensor networks. Particularly for wearable sensor systems, smaller battery has less energy, which makes energy conservation in data transmission more important. Nevertheless, wearable sensor systems usually have features like frequently dynamic changes of topologies and data over a large range, of which current aggregating methods can’t adapt to the demand. In this paper, we study the system composed of many wearable devices with sensors, such as the network of a tactical unit, and introduce an energy consumption-balanced method of data aggregation, named LDA-RT. In the proposed method, we develop a query algorithm based on the idea of ‘happened-before’ to construct a dynamic and energy-balancing routing tree. We also present a distributed data aggregating and sorting algorithm to execute top-k query and decrease the data that must be transferred among wearable devices. Combining these algorithms, LDA-RT tries to balance the energy consumptions for prolonging the lifetime of wearable sensor systems. Results of evaluation indicate that LDA-RT performs well in constructing routing trees and energy balances. It also outperforms the filter-based top-k monitoring approach in energy consumption, load balance, and the network’s lifetime, especially for highly dynamic data sources.

  1. Design, fabrication and metrological evaluation of wearable pressure sensors.

    Science.gov (United States)

    Goy, C B; Menichetti, V; Yanicelli, L M; Lucero, J B; López, M A Gómez; Parodi, N F; Herrera, M C

    2015-04-01

    Pressure sensors are valuable transducers that are necessary in a huge number of medical application. However, the state of the art of compact and lightweight pressure sensors with the capability of measuring the contact pressure between two surfaces (contact pressure sensors) is very poor. In this work, several types of wearable contact pressure sensors are fabricated using different conductive textile materials and piezo-resistive films. The fabricated sensors differ in size, the textile conductor used and/or the number of layers of the sandwiched piezo-resistive film. The intention is to study, through the obtaining of their calibration curves, their metrological properties (repeatability, sensitivity and range) and determine which physical characteristics improve their ability for measuring contact pressures. It has been found that it is possible to obtain wearable contact pressure sensors through the proposed fabrication process with satisfactory repeatability, range and sensitivity; and that some of these properties can be improved by the physical characteristics of the sensors.

  2. Unintended Consequences of Wearable Sensor Use in Healthcare. Contribution of the IMIA Wearable Sensors in Healthcare WG.

    Science.gov (United States)

    Schukat, M; McCaldin, D; Wang, K; Schreier, G; Lovell, N H; Marschollek, M; Redmond, S J

    2016-11-10

    As wearable sensors take the consumer market by storm, and medical device manufacturers move to make their devices wireless and appropriate for ambulatory use, this revolution brings with it some unintended consequences, which we aim to discuss in this paper. We discuss some important unintended consequences, both beneficial and unwanted, which relate to: modifications of behavior; creation and use of big data sets; new security vulnerabilities; and unforeseen challenges faced by regulatory authorities, struggling to keep pace with recent innovations. Where possible, we proposed potential solutions to unwanted consequences. Intelligent and inclusive design processes may mitigate unintended modifications in behavior. For big data, legislating access to and use of these data will be a legal and political challenge in the years ahead, as we trade the health benefits of wearable sensors against the risk to our privacy. The wireless and personal nature of wearable sensors also exposes them to a number of unique security vulnerabilities. Regulation plays an important role in managing these security risks, but also has the dual responsibility of ensuring that wearable devices are fit for purpose. However, the burden of validating the function and security of medical devices is becoming infeasible for regulators, given the many software apps and wearable sensors entering the market each year, which are only a subset of an even larger 'internet of things'. Wearable sensors may serve to improve wellbeing, but we must be vigilant against the occurrence of unintended consequences. With collaboration between device manufacturers, regulators, and end-users, we balance the risk of unintended consequences occurring against the incredible benefit that wearable sensors promise to bring to the world.

  3. Energy harvesting for human wearable and implantable bio-sensors.

    Science.gov (United States)

    Mitcheson, Paul D

    2010-01-01

    There are clear trade-offs between functionality, battery lifetime and battery volume for wearable and implantable wireless-biosensors which energy harvesting devices may be able to overcome. Reliable energy harvesting has now become a reality for machine condition monitoring and is finding applications in chemical process plants, refineries and water treatment works. However, practical miniature devices that can harvest sufficient energy from the human body to power a wireless bio-sensor are still in their infancy. This paper reviews the options for human energy harvesting in order to determine power availability for harvester-powered body sensor networks. The main competing technologies for energy harvesting from the human body are inertial kinetic energy harvesting devices and thermoelectric devices. These devices are advantageous to some other types as they can be hermetically sealed. In this paper the fundamental limit to the power output of these devices is compared as a function of generator volume when attached to a human whilst walking and running. It is shown that the kinetic energy devices have the highest fundamental power limits in both cases. However, when a comparison is made between the devices using device effectivenesses figures from previously demonstrated prototypes presented in the literature, the thermal device is competitive with the kinetic energy harvesting device when the subject is running and achieves the highest power density when the subject is walking.

  4. A Printed Organic Amplification System for Wearable Potentiometric Electrochemical Sensors.

    Science.gov (United States)

    Shiwaku, Rei; Matsui, Hiroyuki; Nagamine, Kuniaki; Uematsu, Mayu; Mano, Taisei; Maruyama, Yuki; Nomura, Ayako; Tsuchiya, Kazuhiko; Hayasaka, Kazuma; Takeda, Yasunori; Fukuda, Takashi; Kumaki, Daisuke; Tokito, Shizuo

    2018-03-02

    Electrochemical sensor systems with integrated amplifier circuits play an important role in measuring physiological signals via in situ human perspiration analysis. Signal processing circuitry based on organic thin-film transistors (OTFTs) have significant potential in realizing wearable sensor devices due to their superior mechanical flexibility and biocompatibility. Here, we demonstrate a novel potentiometric electrochemical sensing system comprised of a potassium ion (K + ) sensor and amplifier circuits employing OTFT-based pseudo-CMOS inverters, which have a highly controllable switching voltage and closed-loop gain. The ion concentration sensitivity of the fabricated K + sensor was 34 mV/dec, which was amplified to 160 mV/dec (by a factor of 4.6) with high linearity. The developed system is expected to help further the realization of ultra-thin and flexible wearable sensor devices for healthcare applications.

  5. Wearable health monitoring using capacitive voltage-mode Human Body Communication.

    Science.gov (United States)

    Maity, Shovan; Das, Debayan; Sen, Shreyas

    2017-07-01

    Rapid miniaturization and cost reduction of computing, along with the availability of wearable and implantable physiological sensors have led to the growth of human Body Area Network (BAN) formed by a network of such sensors and computing devices. One promising application of such a network is wearable health monitoring where the collected data from the sensors would be transmitted and analyzed to assess the health of a person. Typically, the devices in a BAN are connected through wireless (WBAN), which suffers from energy inefficiency due to the high-energy consumption of wireless transmission. Human Body Communication (HBC) uses the relatively low loss human body as the communication medium to connect these devices, promising order(s) of magnitude better energy-efficiency and built-in security compared to WBAN. In this paper, we demonstrate a health monitoring device and system built using Commercial-Off-The-Shelf (COTS) sensors and components, that can collect data from physiological sensors and transmit it through a) intra-body HBC to another device (hub) worn on the body or b) upload health data through HBC-based human-machine interaction to an HBC capable machine. The system design constraints and signal transfer characteristics for the implemented HBC-based wearable health monitoring system are measured and analyzed, showing reliable connectivity with >8× power savings compared to Bluetooth low-energy (BTLE).

  6. International Conference on Wearable Sensors and Robots 2015

    CERN Document Server

    Virk, G; Yang, Huayong

    2017-01-01

    These proceedings present the latest information on regulations and standards for medical and non-medical devices, including wearable robots for gait training and support, design of exoskeletons for the elderly, innovations in assistive robotics, and analysis of human–machine interactions taking into account ergonomic considerations. The rapid development of key mechatronics technologies in recent years has shown that human living standards have significantly improved, and the International Conference on Wearable Sensor and Robot was held in Hangzhou, China from October 16 to 18, 2015, to present research mainly focused on personal-care robots and medical devices. The aim of the conference was to bring together academics, researchers, engineers and students from across the world to discuss state-of-the-art technologies related to various aspects of wearable sensors and robots. .

  7. Flexible, Stretchable Sensors for Wearable Health Monitoring: Sensing Mechanisms, Materials, Fabrication Strategies and Features

    Science.gov (United States)

    Liu, Yan; Wang, Hai; Zhao, Wei; Qin, Hongbo; Xie, Yongqiang

    2018-01-01

    Wearable health monitoring systems have gained considerable interest in recent years owing to their tremendous promise for personal portable health watching and remote medical practices. The sensors with excellent flexibility and stretchability are crucial components that can provide health monitoring systems with the capability of continuously tracking physiological signals of human body without conspicuous uncomfortableness and invasiveness. The signals acquired by these sensors, such as body motion, heart rate, breath, skin temperature and metabolism parameter, are closely associated with personal health conditions. This review attempts to summarize the recent progress in flexible and stretchable sensors, concerning the detected health indicators, sensing mechanisms, functional materials, fabrication strategies, basic and desired features. The potential challenges and future perspectives of wearable health monitoring system are also briefly discussed. PMID:29470408

  8. Wearable sensor system for human localization and motion capture

    OpenAIRE

    Zihajehzadeh, Shaghayegh

    2017-01-01

    Recent advances in MEMS wearable inertial/magnetic sensors and mobile computing have fostered a dramatic growth of interest for ambulatory human motion capture (MoCap). Compared to traditional optical MoCap systems such as the optical systems, inertial (i.e. accelerometer and gyroscope) and magnetic sensors do not require external fixtures such as cameras. Hence, they do not have in-the-lab measurement limitations and thus are ideal for ambulatory applications. However, due to the manufacturi...

  9. A highly sensitive, low-cost, wearable pressure sensor based on conductive hydrogel spheres

    KAUST Repository

    Tai, Yanlong; Mulle, Matthieu; Ventura, Isaac Aguilar; Lubineau, Gilles

    2015-01-01

    Wearable pressure sensing solutions have promising future for practical applications in health monitoring and human/machine interfaces. Here, a highly sensitive, low-cost, wearable pressure sensor based on conductive single-walled carbon nanotube

  10. Gait Kinematic Analysis in Water Using Wearable Inertial Magnetic Sensors.

    Directory of Open Access Journals (Sweden)

    Silvia Fantozzi

    Full Text Available Walking is one of the fundamental motor tasks executed during aquatic therapy. Previous kinematics analyses conducted using waterproofed video cameras were limited to the sagittal plane and to only one or two consecutive steps. Furthermore, the set-up and post-processing are time-consuming and thus do not allow a prompt assessment of the correct execution of the movements during the aquatic session therapy. The aim of the present study was to estimate the 3D joint kinematics of the lower limbs and thorax-pelvis joints in sagittal and frontal planes during underwater walking using wearable inertial and magnetic sensors. Eleven healthy adults were measured during walking both in shallow water and in dry-land conditions. Eight wearable inertial and magnetic sensors were inserted in waterproofed boxes and fixed to the body segments by means of elastic modular bands. A validated protocol (Outwalk was used. Gait cycles were automatically segmented and selected if relevant intraclass correlation coefficients values were higher than 0.75. A total of 704 gait cycles for the lower limb joints were normalized in time and averaged to obtain the mean cycle of each joint, among participants. The mean speed in water was 40% lower than that of the dry-land condition. Longer stride duration and shorter stride distance were found in the underwater walking. In the sagittal plane, the knee was more flexed (≈ 23° and the ankle more dorsiflexed (≈ 9° at heel strike, and the hip was more flexed at toe-off (≈ 13° in water than on land. On the frontal plane in the underwater walking, smoother joint angle patterns were observed for thorax-pelvis and hip, and ankle was more inversed at toe-off (≈ 7° and showed a more inversed mean value (≈ 7°. The results were mainly explained by the effect of the speed in the water as supported by the linear mixed models analysis performed. Thus, it seemed that the combination of speed and environment triggered

  11. Gait Kinematic Analysis in Water Using Wearable Inertial Magnetic Sensors.

    Science.gov (United States)

    Fantozzi, Silvia; Giovanardi, Andrea; Borra, Davide; Gatta, Giorgio

    2015-01-01

    Walking is one of the fundamental motor tasks executed during aquatic therapy. Previous kinematics analyses conducted using waterproofed video cameras were limited to the sagittal plane and to only one or two consecutive steps. Furthermore, the set-up and post-processing are time-consuming and thus do not allow a prompt assessment of the correct execution of the movements during the aquatic session therapy. The aim of the present study was to estimate the 3D joint kinematics of the lower limbs and thorax-pelvis joints in sagittal and frontal planes during underwater walking using wearable inertial and magnetic sensors. Eleven healthy adults were measured during walking both in shallow water and in dry-land conditions. Eight wearable inertial and magnetic sensors were inserted in waterproofed boxes and fixed to the body segments by means of elastic modular bands. A validated protocol (Outwalk) was used. Gait cycles were automatically segmented and selected if relevant intraclass correlation coefficients values were higher than 0.75. A total of 704 gait cycles for the lower limb joints were normalized in time and averaged to obtain the mean cycle of each joint, among participants. The mean speed in water was 40% lower than that of the dry-land condition. Longer stride duration and shorter stride distance were found in the underwater walking. In the sagittal plane, the knee was more flexed (≈ 23°) and the ankle more dorsiflexed (≈ 9°) at heel strike, and the hip was more flexed at toe-off (≈ 13°) in water than on land. On the frontal plane in the underwater walking, smoother joint angle patterns were observed for thorax-pelvis and hip, and ankle was more inversed at toe-off (≈ 7°) and showed a more inversed mean value (≈ 7°). The results were mainly explained by the effect of the speed in the water as supported by the linear mixed models analysis performed. Thus, it seemed that the combination of speed and environment triggered modifications in the

  12. Wearable Wireless Tyrosinase Bandage and Microneedle Sensors: Toward Melanoma Screening.

    Science.gov (United States)

    Ciui, Bianca; Martin, Aida; Mishra, Rupesh K; Brunetti, Barbara; Nakagawa, Tatsuo; Dawkins, Thomas J; Lyu, Mengjia; Cristea, Cecilia; Sandulescu, Robert; Wang, Joseph

    2018-04-01

    Wearable bendable bandage-based sensor and a minimally invasive microneedle biosensor are described toward rapid screening of skin melanoma. These wearable electrochemical sensors are capable of detecting the presence of the tyrosinase (TYR) enzyme cancer biomarker in the presence of its catechol substrate, immobilized on the transducer surface. In the presence of the surface TYR biomarker, the immobilized catechol is rapidly converted to benzoquinone that is detected amperometrically, with a current signal proportional to the TYR level. The flexible epidermal bandage sensor relies on printing stress-enduring inks which display good resiliency against mechanical deformations, whereas the hollow microneedle device is filled with catechol-coated carbon paste for assessing tissue TYR levels. The bandage sensor can thus be used directly on the skin whereas microneedle device can reach melanoma tissues under the skin. Both wearable sensors are interfaced to an ultralight flexible electronic board, which transmits data wirelessly to a mobile device. The analytical performance of the resulting bandage and microneedle sensing systems are evaluated using TYR-containing agarose phantom gel and porcine skin. The new integrated conformal portable sensing platforms hold considerable promise for decentralized melanoma screening, and can be extended to the screening of other key biomarkers in skin moles. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  13. Flexible Graphene-Based Wearable Gas and Chemical Sensors.

    Science.gov (United States)

    Singh, Eric; Meyyappan, M; Nalwa, Hari Singh

    2017-10-11

    Wearable electronics is expected to be one of the most active research areas in the next decade; therefore, nanomaterials possessing high carrier mobility, optical transparency, mechanical robustness and flexibility, lightweight, and environmental stability will be in immense demand. Graphene is one of the nanomaterials that fulfill all these requirements, along with other inherently unique properties and convenience to fabricate into different morphological nanostructures, from atomically thin single layers to nanoribbons. Graphene-based materials have also been investigated in sensor technologies, from chemical sensing to detection of cancer biomarkers. The progress of graphene-based flexible gas and chemical sensors in terms of material preparation, sensor fabrication, and their performance are reviewed here. The article provides a brief introduction to graphene-based materials and their potential applications in flexible and stretchable wearable electronic devices. The role of graphene in fabricating flexible gas sensors for the detection of various hazardous gases, including nitrogen dioxide (NO 2 ), ammonia (NH 3 ), hydrogen (H 2 ), hydrogen sulfide (H 2 S), carbon dioxide (CO 2 ), sulfur dioxide (SO 2 ), and humidity in wearable technology, is discussed. In addition, applications of graphene-based materials are also summarized in detecting toxic heavy metal ions (Cd, Hg, Pb, Cr, Fe, Ni, Co, Cu, Ag), and volatile organic compounds (VOCs) including nitrobenzene, toluene, acetone, formaldehyde, amines, phenols, bisphenol A (BPA), explosives, chemical warfare agents, and environmental pollutants. The sensitivity, selectivity and strategies for excluding interferents are also discussed for graphene-based gas and chemical sensors. The challenges for developing future generation of flexible and stretchable sensors for wearable technology that would be usable for the Internet of Things (IoT) are also highlighted.

  14. A flexible wearable sensor for knee flexion assessment during gait.

    Science.gov (United States)

    Papi, Enrica; Bo, Yen Nee; McGregor, Alison H

    2018-05-01

    Gait analysis plays an important role in the diagnosis and management of patients with movement disorders but it is usually performed within a laboratory. Recently interest has shifted towards the possibility of conducting gait assessments in everyday environments thus facilitating long-term monitoring. This is possible by using wearable technologies rather than laboratory based equipment. This study aims to validate a novel wearable sensor system's ability to measure peak knee sagittal angles during gait. The proposed system comprises a flexible conductive polymer unit interfaced with a wireless acquisition node attached over the knee on a pair of leggings. Sixteen healthy volunteers participated to two gait assessments on separate occasions. Data was simultaneously collected from the novel sensor and a gold standard 10 camera motion capture system. The relationship between sensor signal and reference knee flexion angles was defined for each subject to allow the transformation of sensor voltage outputs to angular measures (degrees). The knee peak flexion angle from the sensor and reference system were compared by means of root mean square error (RMSE), absolute error, Bland-Altman plots and intra-class correlation coefficients (ICCs) to assess test-retest reliability. Comparisons of knee peak flexion angles calculated from the sensor and gold standard yielded an absolute error of 0.35(±2.9°) and RMSE of 1.2(±0.4)°. Good agreement was found between the two systems with the majority of data lying within the limits of agreement. The sensor demonstrated high test-retest reliability (ICCs>0.8). These results show the ability of the sensor to monitor knee peak sagittal angles with small margins of error and in agreement with the gold standard system. The sensor has potential to be used in clinical settings as a discreet, unobtrusive wearable device allowing for long-term gait analysis. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

  15. Wearable Sensor-Based Rehabilitation Exercise Assessment for Knee Osteoarthritis

    Directory of Open Access Journals (Sweden)

    Kun-Hui Chen

    2015-02-01

    Full Text Available Since the knee joint bears the full weight load of the human body and the highest pressure loads while providing flexible movement, it is the body part most vulnerable and susceptible to osteoarthritis. In exercise therapy, the early rehabilitation stages last for approximately six weeks, during which the patient works with the physical therapist several times each week. The patient is afterwards given instructions for continuing rehabilitation exercise by him/herself at home. This study develops a rehabilitation exercise assessment mechanism using three wearable sensors mounted on the chest, thigh and shank of the working leg in order to enable the patients with knee osteoarthritis to manage their own rehabilitation progress. In this work, time-domain, frequency-domain features and angle information of the motion sensor signals are used to classify the exercise type and identify whether their postures are proper or not. Three types of rehabilitation exercise commonly prescribed to knee osteoarthritis patients are: Short-Arc Exercise, Straight Leg Raise, and Quadriceps Strengthening Mini-squats. After ten subjects performed the three kinds of rehabilitation activities, three validation techniques including 10-fold cross-validation, within subject cross validation, and leave-one-subject cross validation are utilized to confirm the proposed mechanism. The overall recognition accuracy for exercise type classification is 97.29% and for exercise posture identification it is 88.26%. The experimental results demonstrate the feasibility of the proposed mechanism which can help patients perform rehabilitation movements and progress effectively. Moreover, the proposed mechanism is able to detect multiple errors at once, fulfilling the requirements for rehabilitation assessment.

  16. Development of gait segmentation methods for wearable foot pressure sensors.

    Science.gov (United States)

    Crea, S; De Rossi, S M M; Donati, M; Reberšek, P; Novak, D; Vitiello, N; Lenzi, T; Podobnik, J; Munih, M; Carrozza, M C

    2012-01-01

    We present an automated segmentation method based on the analysis of plantar pressure signals recorded from two synchronized wireless foot insoles. Given the strict limits on computational power and power consumption typical of wearable electronic components, our aim is to investigate the capability of a Hidden Markov Model machine-learning method, to detect gait phases with different levels of complexity in the processing of the wearable pressure sensors signals. Therefore three different datasets are developed: raw voltage values, calibrated sensor signals and a calibrated estimation of total ground reaction force and position of the plantar center of pressure. The method is tested on a pool of 5 healthy subjects, through a leave-one-out cross validation. The results show high classification performances achieved using estimated biomechanical variables, being on average the 96%. Calibrated signals and raw voltage values show higher delays and dispersions in phase transition detection, suggesting a lower reliability for online applications.

  17. Channel Model on Various Frequency Bands for Wearable Body Area Network

    Science.gov (United States)

    Katayama, Norihiko; Takizawa, Kenichi; Aoyagi, Takahiro; Takada, Jun-Ichi; Li, Huan-Bang; Kohno, Ryuji

    Body Area Network (BAN) is considered as a promising technology in supporting medical and healthcare services by combining with various biological sensors. In this paper, we look at wearable BAN, which provides communication links among sensors on body surface. In order to design a BAN that manages biological information with high efficiency and high reliability, the propagation characteristics of BAN must be thoroughly investigated. As a preliminary effort, we measured the propagation characteristics of BAN at frequency bands of 400MHz, 600MHz, 900MHz and 2400MHz respectively. Channel models for wearable BAN based on the measurement were derived. Our results show that the channel model can be described by using a path loss model for all frequency bands investigated.

  18. Emotion and Cognitive Reappraisal Based on GSR Wearable Sensor

    Institute of Scientific and Technical Information of China (English)

    LI Minjia; XIE Lun; WANG Zhiliang; REN Fuji

    2017-01-01

    Various wearable equipment enables us to measure people behavior by physiological signals. In our research, we present one gal-vanic skin reaction (GSR) wearable sensor which can analyze human emotions based on cognition reappraisal. First, We research the factors of emotional state transition in Arousal-Valence-Stance(AVS) emotional space. Second, the influence of the cognition on emotional state transition is considered, and the reappraisal factor based on Gross regulation theory is established to correct the effectiveness from cognitive reappraisal ability to emotional state transition. Third, based on the previous work, we establish a GSR emotion sensing system for predicting emotional state transition and considering the correlation between GSR signals and emotions. Finally, an overall wearable sensor layout is built. In the experiment part, we invited 30 college students to wear our GSR sensors and watch 14 kinds of affective videos. We recorded their GSR signals while asking them to record their emotional states synchronously. The experiment results show different reappraisal factors can predict subjects'emotional state transition well and indirectly confirm the feasibility of the Gross regulation theory.

  19. Influence on Calculated Blood Pressure of Measurement Posture for the Development of Wearable Vital Sign Sensors

    Directory of Open Access Journals (Sweden)

    Shouhei Koyama

    2017-01-01

    Full Text Available We studied a wearable blood pressure sensor using a fiber Bragg grating (FBG sensor, which is a highly accurate strain sensor. This sensor is installed at the pulsation point of the human body to measure the pulse wave signal. A calibration curve is built that calculates the blood pressure by multivariate analysis using the pulse wave signal and a reference blood pressure measurement. However, if the measurement height of the FBG sensor is different from the reference measurement height, an error is included in the reference blood pressure. We verified the accuracy of the blood pressure calculation with respect to the measurement height difference and the posture of the subject. As the difference between the measurement height of the FBG sensor and the reference blood pressure measurement increased, the accuracy of the blood pressure calculation decreased. When the measurement height was identical and only posture was changed, good accuracy was achieved. In addition, when calibration curves were built using data measured in multiple postures, the blood pressure of each posture could be calculated from a single calibration curve. This will allow miniaturization of the necessary electronics of the sensor system, which is important for a wearable sensor.

  20. Wearable Wide-Range Strain Sensors Based on Ionic Liquids and Monitoring of Human Activities

    Directory of Open Access Journals (Sweden)

    Shao-Hui Zhang

    2017-11-01

    Full Text Available Wearable sensors for detection of human activities have encouraged the development of highly elastic sensors. In particular, to capture subtle and large-scale body motion, stretchable and wide-range strain sensors are highly desired, but still a challenge. Herein, a highly stretchable and transparent stain sensor based on ionic liquids and elastic polymer has been developed. The as-obtained sensor exhibits impressive stretchability with wide-range strain (from 0.1% to 400%, good bending properties and high sensitivity, whose gauge factor can reach 7.9. Importantly, the sensors show excellent biological compatibility and succeed in monitoring the diverse human activities ranging from the complex large-scale multidimensional motions to subtle signals, including wrist, finger and elbow joint bending, finger touch, breath, speech, swallow behavior and pulse wave.

  1. Online Phase Detection Using Wearable Sensors for Walking with a Robotic Prosthesis

    Directory of Open Access Journals (Sweden)

    Maja Goršič

    2014-02-01

    Full Text Available This paper presents a gait phase detection algorithm for providing feedback in walking with a robotic prosthesis. The algorithm utilizes the output signals of a wearable wireless sensory system incorporating sensorized shoe insoles and inertial measurement units attached to body segments. The principle of detecting transitions between gait phases is based on heuristic threshold rules, dividing a steady-state walking stride into four phases. For the evaluation of the algorithm, experiments with three amputees, walking with the robotic prosthesis and wearable sensors, were performed. Results show a high rate of successful detection for all four phases (the average success rate across all subjects >90%. A comparison of the proposed method to an off-line trained algorithm using hidden Markov models reveals a similar performance achieved without the need for learning dataset acquisition and previous model training.

  2. Development and evaluation of an ambulatory stress monitor based on wearable sensors.

    Science.gov (United States)

    Choi, Jongyoon; Ahmed, Beena; Gutierrez-Osuna, Ricardo

    2012-03-01

    Chronic stress is endemic to modern society. However, as it is unfeasible for physicians to continuously monitor stress levels, its diagnosis is nontrivial. Wireless body sensor networks offer opportunities to ubiquitously detect and monitor mental stress levels, enabling improved diagnosis, and early treatment. This article describes the development of a wearable sensor platform to monitor a number of physiological correlates of mental stress. We discuss tradeoffs in both system design and sensor selection to balance information content and wearability. Using experimental signals collected from the wearable sensor, we describe a selected number of physiological features that show good correlation with mental stress. In particular, we propose a new spectral feature that estimates the balance of the autonomic nervous system by combining information from the power spectral density of respiration and heart rate variability. We validate the effectiveness of our approach on a binary discrimination problem when subjects are placed under two psychophysiological conditions: mental stress and relaxation. When used in a logistic regression model, our feature set is able to discriminate between these two mental states with a success rate of 81% across subjects. © 2012 IEEE

  3. Carbon Nanotube-Based Ion Selective Sensors for Wearable Applications.

    Science.gov (United States)

    Roy, Soumyendu; David-Pur, Moshe; Hanein, Yael

    2017-10-11

    Wearable electronics offer new opportunities in a wide range of applications, especially sweat analysis using skin sensors. A fundamental challenge in these applications is the formation of sensitive and stable electrodes. In this article we report the development of a wearable sensor based on carbon nanotube (CNT) electrode arrays for sweat sensing. Solid-state ion selective electrodes (ISEs), sensitive to Na + ions, were prepared by drop coating plasticized poly(vinyl chloride) (PVC) doped with ionophore and ion exchanger on CNT electrodes. The ion selective membrane (ISM) filled the intertubular spaces of the highly porous CNT film and formed an attachment that was stronger than that achieved with flat Au, Pt, or carbon electrodes. Concentration of the ISM solution used influenced the attachment to the CNT film, the ISM surface morphology, and the overall performance of the sensor. Sensitivity of 56 ± 3 mV/decade to Na + ions was achieved. Optimized solid-state reference electrodes (REs), suitable for wearable applications, were prepared by coating CNT electrodes with colloidal dispersion of Ag/AgCl, agarose hydrogel with 0.5 M NaCl, and a passivation layer of PVC doped with NaCl. The CNT-based REs had low sensitivity (-1.7 ± 1.2 mV/decade) toward the NaCl solution and high repeatability and were superior to bare Ag/AgCl, metals, carbon, and CNT films, reported previously as REs. CNT-based ISEs were calibrated against CNT-based REs, and the short-term stability of the system was tested. We demonstrate that CNT-based devices implemented on a flexible support are a very attractive platform for future wearable technology devices.

  4. Wearable technologies for sweat rate and conductivity sensors

    CERN Document Server

    Salvo, Pietro

    2012-01-01

    Hauptbeschreibung Wearable sensors present a new frontier in the development of monitoring techniques. They are of great importance in sectors such as sports and healthcare, as they permit the continuous monitoring of physiological and biological elements, such as ECG and human sweat. Until recently, this could only be carried out in specialized laboratories in the presence of cumbersome, and usually, expensive devices. Sweat monitoring sensors integrated onto textile substrates are not only part of a new field of work but, they also represent the first attempt to implement such an

  5. Noninvasive wearable sensor for indirect glucometry.

    Science.gov (United States)

    Zilberstein, Gleb; Zilberstein, Roman; Maor, Uriel; Righetti, Pier Giorgio

    2018-04-02

    A noninvasive mini-sensor for blood glucose concentration assessment has been developed. The monitoring is performed by gently pressing a wrist or fingertip onto the chemochromic mixture coating a thin glass or polymer film positioned on the back panel of a smart watch with PPG/HRM (photoplethysmographic/heart rate monitoring sensor). The various chemochromic components measure the absolute values of the following metabolites present in the sweat: acetone, acetone beta-hydroxybutirate, aceto acetate, water, carbon dioxide, lactate anion, pyruvic acid, Na and K salts. Taken together, all these parameters give information about blood glucose concentration, calculated via multivariate analysis based on neural network algorithms built into the sensor. The Clarke Error Grid shows an excellent correlation between data measured by the standard invasive glucose analyser and the present noninvasive sensor, with all points aligned along a 45-degree diagonal and contained almost exclusively in sector A. Graphs measuring glucose levels five times a day (prior, during and after breakfast and prior, during and after lunch), for different individuals (males and females) show a good correlation between the two curves of conventional, invasive meters vs. the noninvasive sensor, with an error of ±15%. This novel, noninvasive sensor for indirect glucometry is fully miniaturized, easy to use and operate and could represent a valid alternative in clinical settings and for individual, personal users, to current, invasive tools. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  6. Development of a wearable wireless body area network for health monitoring of the elderly and disabled

    Science.gov (United States)

    Rushambwa, Munyaradzi C.; Gezimati, Mavis; Jeeva, J. B.

    2017-11-01

    Novel advancements in systems miniaturization, electronics in health care and communication technologies are enabling the integration of both patients and doctors involvement in health care system. A Wearable Wireless Body Area Network (WWBAN) provides continuous, unobtrusive ambulatory, ubiquitous health monitoring, and provide real time patient’s status to the physician without any constraint on their normal daily life activities. In this project we developed a wearable wireless body area network system that continuously monitor the health of the elderly and the disabled and provide them with independent, safe and secure living. The WWBAN system monitors the following parameters; blood oxygen saturation using a pulse oximeter sensor (SpO2), heart rate (HR) pulse sensor, Temperature, hydration, glucose level and fall detection. When the wearable system is put on, the sensor values are processed and analysed. If any of the monitored parameter values falls below or exceeds the normal range, there is trigger of remote alert by which an SMS is send to a doctor or physician via GSM module and network. The developed system offers flexibility and mobility to the user; it is a real time system and has significance in revolutionizing health care system by enabling non-invasive, inexpensive, continuous health monitoring.

  7. Wearable Sweat Rate Sensors for Human Thermal Comfort Monitoring.

    Science.gov (United States)

    Sim, Jai Kyoung; Yoon, Sunghyun; Cho, Young-Ho

    2018-01-19

    We propose watch-type sweat rate sensors capable of automatic natural ventilation by integrating miniaturized thermo-pneumatic actuators, and experimentally verify their performances and applicability. Previous sensors using natural ventilation require manual ventilation process or high-power bulky thermo-pneumatic actuators to lift sweat rate detection chambers above skin for continuous measurement. The proposed watch-type sweat rate sensors reduce operation power by minimizing expansion fluid volume to 0.4 ml through heat circuit modeling. The proposed sensors reduce operation power to 12.8% and weight to 47.6% compared to previous portable sensors, operating for 4 hours at 6 V batteries. Human experiment for thermal comfort monitoring is performed by using the proposed sensors having sensitivity of 0.039 (pF/s)/(g/m 2 h) and linearity of 97.9% in human sweat rate range. Average sweat rate difference for each thermal status measured in three subjects shows (32.06 ± 27.19) g/m 2 h in thermal statuses including 'comfortable', 'slightly warm', 'warm', and 'hot'. The proposed sensors thereby can discriminate and compare four stages of thermal status. Sweat rate measurement error of the proposed sensors is less than 10% under air velocity of 1.5 m/s corresponding to human walking speed. The proposed sensors are applicable for wearable and portable use, having potentials for daily thermal comfort monitoring applications.

  8. Extraction and Analysis of Respiratory Motion Using Wearable Inertial Sensor System during Trunk Motion

    Directory of Open Access Journals (Sweden)

    Apoorva Gaidhani

    2017-12-01

    Full Text Available Respiratory activity is an essential vital sign of life that can indicate changes in typical breathing patterns and irregular body functions such as asthma and panic attacks. Many times, there is a need to monitor breathing activity while performing day-to-day functions such as standing, bending, trunk stretching or during yoga exercises. A single IMU (inertial measurement unit can be used in measuring respiratory motion; however, breathing motion data may be influenced by a body trunk movement that occurs while recording respiratory activity. This research employs a pair of wireless, wearable IMU sensors custom-made by the Department of Electrical Engineering at San Diego State University. After appropriate sensor placement for data collection, this research applies principles of robotics, using the Denavit-Hartenberg convention, to extract relative angular motion between the two sensors. One of the obtained relative joint angles in the “Sagittal” plane predominantly yields respiratory activity. An improvised version of the proposed method and wearable, wireless sensors can be suitable to extract respiratory information while performing sports or exercises, as they do not restrict body motion or the choice of location to gather data.

  9. 3D Printing Technologies for Flexible Tactile Sensors toward Wearable Electronics and Electronic Skin

    Directory of Open Access Journals (Sweden)

    Changyong Liu

    2018-06-01

    Full Text Available 3D printing has attracted a lot of attention in recent years. Over the past three decades, various 3D printing technologies have been developed including photopolymerization-based, materials extrusion-based, sheet lamination-based, binder jetting-based, power bed fusion-based and direct energy deposition-based processes. 3D printing offers unparalleled flexibility and simplicity in the fabrication of highly complex 3D objects. Tactile sensors that emulate human tactile perceptions are used to translate mechanical signals such as force, pressure, strain, shear, torsion, bend, vibration, etc. into electrical signals and play a crucial role toward the realization of wearable electronics and electronic skin. To date, many types of 3D printing technologies have been applied in the manufacturing of various types of tactile sensors including piezoresistive, capacitive and piezoelectric sensors. This review attempts to summarize the current state-of-the-art 3D printing technologies and their applications in tactile sensors for wearable electronics and electronic skin. The applications are categorized into five aspects: 3D-printed molds for microstructuring substrate, electrodes and sensing element; 3D-printed flexible sensor substrate and sensor body for tactile sensors; 3D-printed sensing element; 3D-printed flexible and stretchable electrodes for tactile sensors; and fully 3D-printed tactile sensors. Latest advances in the fabrication of tactile sensors by 3D printing are reviewed and the advantages and limitations of various 3D printing technologies and printable materials are discussed. Finally, future development of 3D-printed tactile sensors is discussed.

  10. Development of fabric-based chemical gas sensors for use as wearable electronic noses.

    Science.gov (United States)

    Seesaard, Thara; Lorwongtragool, Panida; Kerdcharoen, Teerakiat

    2015-01-16

    Novel gas sensors embroidered into fabric substrates based on polymers/ SWNT-COOH nanocomposites were proposed in this paper, aiming for their use as a wearable electronic nose (e-nose). The fabric-based chemical gas sensors were fabricated by two main processes: drop coating and embroidery. Four potential polymers (PVC, cumene-PSMA, PSE and PVP)/functionalized-SWCNT sensing materials were deposited onto interdigitated electrodes previously prepared by embroidering conductive thread on a fabric substrate to make an optimal set of sensors. After preliminary trials of the obtained sensors, it was found that the sensors yielded a electrical resistance in the region of a few kilo-Ohms. The sensors were tested with various volatile compounds such as ammonium hydroxide, ethanol, pyridine, triethylamine, methanol and acetone, which are commonly found in the wastes released from the human body. These sensors were used to detect and discriminate between the body odors of different regions and exist in various forms such as the urine, armpit and exhaled breath odor. Based on a simple pattern recognition technique, we have shown that the proposed fabric-based chemical gas sensors can discriminate the human body odor from two persons.

  11. Development of Fabric-Based Chemical Gas Sensors for Use as Wearable Electronic Noses

    Directory of Open Access Journals (Sweden)

    Thara Seesaard

    2015-01-01

    Full Text Available Novel gas sensors embroidered into fabric substrates based on polymers/ SWNT-COOH nanocomposites were proposed in this paper, aiming for their use as a wearable electronic nose (e-nose. The fabric-based chemical gas sensors were fabricated by two main processes: drop coating and embroidery. Four potential polymers (PVC, cumene-PSMA, PSE and PVP/functionalized-SWCNT sensing materials were deposited onto interdigitated electrodes previously prepared by embroidering conductive thread on a fabric substrate to make an optimal set of sensors. After preliminary trials of the obtained sensors, it was found that the sensors yielded a electrical resistance in the region of a few kilo-Ohms. The sensors were tested with various volatile compounds such as ammonium hydroxide, ethanol, pyridine, triethylamine, methanol and acetone, which are commonly found in the wastes released from the human body. These sensors were used to detect and discriminate between the body odors of different regions and exist in various forms such as the urine, armpit and exhaled breath odor. Based on a simple pattern recognition technique, we have shown that the proposed fabric-based chemical gas sensors can discriminate the human body odor from two persons.

  12. Wearable Stretch Sensors for Motion Measurement of the Wrist Joint Based on Dielectric Elastomers.

    Science.gov (United States)

    Huang, Bo; Li, Mingyu; Mei, Tao; McCoul, David; Qin, Shihao; Zhao, Zhanfeng; Zhao, Jianwen

    2017-11-23

    Motion capture of the human body potentially holds great significance for exoskeleton robots, human-computer interaction, sports analysis, rehabilitation research, and many other areas. Dielectric elastomer sensors (DESs) are excellent candidates for wearable human motion capture systems because of their intrinsic characteristics of softness, light weight, and compliance. In this paper, DESs were applied to measure all component motions of the wrist joints. Five sensors were mounted to different positions on the wrist, and each one is for one component motion. To find the best position to mount the sensors, the distribution of the muscles is analyzed. Even so, the component motions and the deformation of the sensors are coupled; therefore, a decoupling method was developed. By the decoupling algorithm, all component motions can be measured with a precision of 5°, which meets the requirements of general motion capture systems.

  13. Force Sensing Resistor and Evaluation of Technology for Wearable Body Pressure Sensing

    Directory of Open Access Journals (Sweden)

    Davide Giovanelli

    2016-01-01

    Full Text Available Wearable technologies are gaining momentum and widespread diffusion. Thanks to devices such as activity trackers, in form of bracelets, watches, or anklets, the end-users are becoming more and more aware of their daily activity routine, posture, and training and can modify their motor-behavior. Activity trackers are prevalently based on inertial sensors such as accelerometers and gyroscopes. Loads we bear with us and the interface pressure they put on our body also affect posture. A contact interface pressure sensing wearable would be beneficial to complement inertial activity trackers. What is precluding force sensing resistors (FSR to be the next best seller wearable? In this paper, we provide elements to answer this question. We build an FSR based on resistive material (Velostat and printed conductive ink electrodes on polyethylene terephthalate (PET substrate; we test its response to pressure in the range 0–2.7 kPa. We present a state-of-the-art review, filtered by the need to identify technologies adequate for wearables. We conclude that the repeatability is the major issue yet unsolved.

  14. Wearable inertial sensors in swimming motion analysis: a systematic review.

    Science.gov (United States)

    de Magalhaes, Fabricio Anicio; Vannozzi, Giuseppe; Gatta, Giorgio; Fantozzi, Silvia

    2015-01-01

    The use of contemporary technology is widely recognised as a key tool for enhancing competitive performance in swimming. Video analysis is traditionally used by coaches to acquire reliable biomechanical data about swimming performance; however, this approach requires a huge computational effort, thus introducing a delay in providing quantitative information. Inertial and magnetic sensors, including accelerometers, gyroscopes and magnetometers, have been recently introduced to assess the biomechanics of swimming performance. Research in this field has attracted a great deal of interest in the last decade due to the gradual improvement of the performance of sensors and the decreasing cost of miniaturised wearable devices. With the aim of describing the state of the art of current developments in this area, a systematic review of the existing methods was performed using the following databases: PubMed, ISI Web of Knowledge, IEEE Xplore, Google Scholar, Scopus and Science Direct. Twenty-seven articles published in indexed journals and conference proceedings, focusing on the biomechanical analysis of swimming by means of inertial sensors were reviewed. The articles were categorised according to sensor's specification, anatomical sites where the sensors were attached, experimental design and applications for the analysis of swimming performance. Results indicate that inertial sensors are reliable tools for swimming biomechanical analyses.

  15. A Shoe-Embedded Piezoelectric Energy Harvester for Wearable Sensors

    Directory of Open Access Journals (Sweden)

    Jingjing Zhao

    2014-07-01

    Full Text Available Harvesting mechanical energy from human motion is an attractive approach for obtaining clean and sustainable electric energy to power wearable sensors, which are widely used for health monitoring, activity recognition, gait analysis and so on. This paper studies a piezoelectric energy harvester for the parasitic mechanical energy in shoes originated from human motion. The harvester is based on a specially designed sandwich structure with a thin thickness, which makes it readily compatible with a shoe. Besides, consideration is given to both high performance and excellent durability. The harvester provides an average output power of 1 mW during a walk at a frequency of roughly 1 Hz. Furthermore, a direct current (DC power supply is built through integrating the harvester with a power management circuit. The DC power supply is tested by driving a simulated wireless transmitter, which can be activated once every 2–3 steps with an active period lasting 5 ms and a mean power of 50 mW. This work demonstrates the feasibility of applying piezoelectric energy harvesters to power wearable sensors.

  16. Human Activity Recognition from Body Sensor Data using Deep Learning.

    Science.gov (United States)

    Hassan, Mohammad Mehedi; Huda, Shamsul; Uddin, Md Zia; Almogren, Ahmad; Alrubaian, Majed

    2018-04-16

    In recent years, human activity recognition from body sensor data or wearable sensor data has become a considerable research attention from academia and health industry. This research can be useful for various e-health applications such as monitoring elderly and physical impaired people at Smart home to improve their rehabilitation processes. However, it is not easy to accurately and automatically recognize physical human activity through wearable sensors due to the complexity and variety of body activities. In this paper, we address the human activity recognition problem as a classification problem using wearable body sensor data. In particular, we propose to utilize a Deep Belief Network (DBN) model for successful human activity recognition. First, we extract the important initial features from the raw body sensor data. Then, a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) are performed to further process the features and make them more robust to be useful for fast activity recognition. Finally, the DBN is trained by these features. Various experiments were performed on a real-world wearable sensor dataset to verify the effectiveness of the deep learning algorithm. The results show that the proposed DBN outperformed other algorithms and achieves satisfactory activity recognition performance.

  17. A wearable biofeedback control system based body area network for freestyle swimming.

    Science.gov (United States)

    Rui Li; Zibo Cai; WeeSit Lee; Lai, Daniel T H

    2016-08-01

    Wearable posture measurement units are capable of enabling real-time performance evaluation and providing feedback to end users. This paper presents a wearable feedback prototype designed for freestyle swimming with focus on trunk rotation measurement. The system consists of a nine-degree-of-freedom inertial sensor, which is built in a central data collection and processing unit, and two vibration motors for delivering real-time feedback. Theses devices form a fundamental body area network (BAN). In the experiment setup, four recreational swimmers were asked to do two sets of 4 x 25m freestyle swimming without and with feedback provided respectively. Results showed that real-time biofeedback mechanism improves swimmers kinematic performance by an average of 4.5% reduction in session time. Swimmers can gradually adapt to feedback signals, and the biofeedback control system can be employed in swimmers daily training for fitness maintenance.

  18. Tissue viability monitoring: a multi-sensor wearable platform approach

    Science.gov (United States)

    Mathur, Neha; Davidson, Alan; Buis, Arjan; Glesk, Ivan

    2016-12-01

    Health services worldwide are seeking ways to improve patient care for amputees suffering from diabetes, and at the same time reduce costs. The monitoring of residual limb temperature, interface pressure and gait can be a useful indicator of tissue viability in lower limb amputees especially to predict the occurrence of pressure ulcers. This is further exacerbated by elevated temperatures and humid micro environment within the prosthesis which encourages the growth of bacteria and skin breakdown. Wearable systems for prosthetic users have to be designed such that the sensors are minimally obtrusive and reliable enough to faithfully record movement and physiological signals. A mobile sensor platform has been developed for use with the lower limb prosthetic users. This system uses an Arduino board that includes sensors for temperature, gait, orientation and pressure measurements. The platform transmits sensor data to a central health authority database server infrastructure through the Bluetooth protocol at a suitable sampling rate. The data-sets recorded using these systems are then processed using machine learning algorithms to extract clinically relevant information from the data. Where a sensor threshold is reached a warning signal can be sent wirelessly together with the relevant data to the patient and appropriate medical personnel. This knowledge is also useful in establishing biomarkers related to a possible deterioration in a patient's health or for assessing the impact of clinical interventions.

  19. Human body heat for powering wearable devices: From thermal energy to application

    International Nuclear Information System (INIS)

    Thielen, Moritz; Sigrist, Lukas; Magno, Michele; Hierold, Christofer; Benini, Luca

    2017-01-01

    Highlights: • A complete system optimization for wearable thermal harvesting from body heat to the application is proposed. • State-of-the-art thermal harvesters and DC-DC converters are compared and classified. • Extensive simulation and experiments are carried out to characterize the harvesting performance. • A case study demonstrates the feasibility to supply a multi-sensor wearables only from body heat. - Abstract: Energy harvesting is the key technology to enable self-sustained wearable devices for the Internet of Things and medical applications. Among various types of harvesting sources such as light, vibration and radio frequency, thermoelectric generators (TEG) are a promising option due to their independence of light conditions or the activity of the wearer. This work investigates scavenging of human body heat and the optimization of the power conversion efficiency from body core to the application. We focus on the critical interaction between thermal harvester and power conditioning circuitry and compare two approaches: (1) a high output voltage, low thermal resistance μTEG combined with a high efficiency actively controlled single inductor DC-DC converter, and (2) a high thermal resistance, low electric resistance mTEG in combination with a low-input voltage coupled inductors based DC-DC converter. The mTEG approach delivers up to 65% higher output power per area in a lab setup and 1–15% in a real-world experiment on the human body depending on physical activity and environmental conditions. Using off-the-shelf and low-cost components, we achieve an average power of 260 μW (μTEG) to 280 μW (mTEG) and power densities of 13 μW cm"−"2 (μTEG) to 14 μW cm"−"2 (mTEG) for systems worn on the human wrist. With the small and lightweight harvesters optimized for wearability, 16% (mTEG) to 24% (μTEG) of the theoretical maximum efficiency is achieved in a worst-case scenario. This efficiency highly depends on the application specific conditions

  20. Wearable carbon nanotube based dry-electrodes for electrophysiological sensors

    Science.gov (United States)

    Kang, Byeong-Cheol; Ha, Tae-Jun

    2018-05-01

    In this paper, we demonstrate all-solution-processed carbon nanotube (CNT) dry-electrodes for the detection of electrophysiological signals such as electrocardiograms (ECG) and electromyograms (EMG). The key parameters of P, Q, R, S, and T peaks are successfully extracted by such CNT based dry-electrodes, which is comparable with conventional silver/chloride (Ag/AgCl) wet-electrodes with a conducting gel film for the ECG recording. Furthermore, the sensing performance of CNT based dry-electrodes is secured during the bending test of 200 cycles, which is essential for wearable electrophysiological sensors in a non-invasive method on human skin. We also investigate the application of wearable CNT based dry-electrodes directly attached to the human skins such as forearm for sensing the electrophysiological signals. The accurate and rapid sensing response can be achieved by CNT based dry-electrodes to supervise the health condition affected by excessive physical movements during the real-time measurements.

  1. Feasibility of large-scale deployment of multiple wearable sensors in Parkinson's disease

    NARCIS (Netherlands)

    Silva de Lima, A.L.; Hahn, T.; Evers, L.J.W.; Vries, N.M. de; Cohen, E.; Afek, M.; Bataille, L.; Daeschler, M.; Claes, K.; Boroojerdi, B.; Terricabras, D.; Little, M.A.; Baldus, H.; Bloem, B.R.; Faber, M.J.

    2017-01-01

    Wearable devices can capture objective day-to-day data about Parkinson's Disease (PD). This study aims to assess the feasibility of implementing wearable technology to collect data from multiple sensors during the daily lives of PD patients. The Parkinson@home study is an observational, two-cohort

  2. Freezing of gait and fall detection in Parkinson's disease using wearable sensors: a systematic review

    NARCIS (Netherlands)

    Silva de Lima, A.L.; Evers, L.J.W.; Hahn, T.; Bataille, L.; Hamilton, J.L.; Little, M.A.; Okuma, Y.; Bloem, B.R.; Faber, M.J.

    2017-01-01

    Despite the large number of studies that have investigated the use of wearable sensors to detect gait disturbances such as Freezing of gait (FOG) and falls, there is little consensus regarding appropriate methodologies for how to optimally apply such devices. Here, an overview of the use of wearable

  3. Wireless Power Transfer for Autonomous Wearable Neurotransmitter Sensors.

    Science.gov (United States)

    Nguyen, Cuong M; Kota, Pavan Kumar; Nguyen, Minh Q; Dubey, Souvik; Rao, Smitha; Mays, Jeffrey; Chiao, J-C

    2015-09-23

    In this paper, we report a power management system for autonomous and real-time monitoring of the neurotransmitter L-glutamate (L-Glu). A low-power, low-noise, and high-gain recording module was designed to acquire signal from an implantable flexible L-Glu sensor fabricated by micro-electro-mechanical system (MEMS)-based processes. The wearable recording module was wirelessly powered through inductive coupling transmitter antennas. Lateral and angular misalignments of the receiver antennas were resolved by using a multi-transmitter antenna configuration. The effective coverage, over which the recording module functioned properly, was improved with the use of in-phase transmitter antennas. Experimental results showed that the recording system was capable of operating continuously at distances of 4 cm, 7 cm and 10 cm. The wireless power management system reduced the weight of the recording module, eliminated human intervention and enabled animal experimentation for extended durations.

  4. Wireless Power Transfer for Autonomous Wearable Neurotransmitter Sensors

    Directory of Open Access Journals (Sweden)

    Cuong M. Nguyen

    2015-09-01

    Full Text Available In this paper, we report a power management system for autonomous and real-time monitoring of the neurotransmitter L-glutamate (L-Glu. A low-power, low-noise, and high-gain recording module was designed to acquire signal from an implantable flexible L-Glu sensor fabricated by micro-electro-mechanical system (MEMS-based processes. The wearable recording module was wirelessly powered through inductive coupling transmitter antennas. Lateral and angular misalignments of the receiver antennas were resolved by using a multi-transmitter antenna configuration. The effective coverage, over which the recording module functioned properly, was improved with the use of in-phase transmitter antennas. Experimental results showed that the recording system was capable of operating continuously at distances of 4 cm, 7 cm and 10 cm. The wireless power management system reduced the weight of the recording module, eliminated human intervention and enabled animal experimentation for extended durations.

  5. Medication Adherence using Non-intrusive Wearable Sensors

    Directory of Open Access Journals (Sweden)

    T. H. Lim

    2017-12-01

    Full Text Available Activity recognition approaches have been applied in home ambient systems to monitor the status and well- being of occupant especially for home care systems. With the advancement of embedded wireless sensing devices, various applications have been proposed to monitor user´s activities and maintain a healthy lifestyle. In this paper, we propose and evaluate a Smart Medication Alert and Treatment Electronic Systems (SmartMATES using a non-intrusive wearable activity recognition sensing system to monitor and alert an user for missing medication prescription. Two sensors are used to collect data from the accelerometer and radio transceiver. Based on the data collected, SmartMATES processes the data and generate a model for the various actions including taking medication. We have evaluated the SmartMATES on 9 participants. The results show that the SmartMATES can identify and prevent missing dosage in a less intrusive way than existing mobile application and traditional approaches.

  6. Wearable flex sensor system for multiple badminton player grip identification

    Science.gov (United States)

    Jacob, Alvin; Zakaria, Wan Nurshazwani Wan; Tomari, Mohd Razali Bin Md; Sek, Tee Kian; Suberi, Anis Azwani Muhd

    2017-09-01

    This paper focuses on the development of a wearable sensor system to identify the different types of badminton grip that is used by a player during training. Badminton movements and strokes are fast and dynamic, where most of the involved movement are difficult to identify with the naked eye. Also, the usage of high processing optometric motion capture system is expensive and causes computational burden. Therefore, this paper suggests the development of a sensorized glove using flex sensor to measure a badminton player's finger flexion angle. The proposed Hand Monitoring Module (HMM) is connected to a personal computer through Bluetooth to enable wireless data transmission. The usability and feasibility of the HMM to identify different grip types were examined through a series of experiments, where the system exhibited 70% detection ability for the five different grip type. The outcome plays a major role in training players to use the proper grips for a badminton stroke to achieve a more powerful and accurate stroke execution.

  7. Wearable sensors network for health monitoring using e-Health platform

    Directory of Open Access Journals (Sweden)

    I. Orha

    2014-06-01

    Full Text Available In this paper we have proposed to present a wearable system for automatic recording of the main physiological parameters of the human body: body temperature, galvanic skin response, respiration rate, blood pressure, pulse, blood oxygen content, blood glucose content, electrocardiogram (ECG, electromyography(EMG, and patient position. To realize this system, we have developed a program that can read and automatically save in a file, the data from specialized sensors. The results can be later interpreted, by comparing them with known normal values and thus offering the possibility for a primary health status diagnosis by specialized personnel. The data received from the wearable sensors is taken by an interface circuit, provided with signal conditioning (filtering, amplification, etc. A microcontroller controls the data acquisition. In this applications we used an Arduino Uno standard development platform. The data are transferred to a PC, using serial communication port of Arduino platform and a communications shield. The whole process of health assessment is commissioned by a program developed by us in the Python programming language. The program provides automatic recording of the aforementioned parameters in a predetermined sequence, or only certain parameters are registered.

  8. Coaxial Thermoplastic Elastomer-Wrapped Carbon Nanotube Fibers for Deformable and Wearable Strain Sensors

    KAUST Repository

    Zhou, Jian

    2018-01-22

    Highly conductive and stretchable fibers are crucial components of wearable electronics systems. Excellent electrical conductivity, stretchability, and wearability are required from such fibers. Existing technologies still display limited performances in these design requirements. Here, achieving highly stretchable and sensitive strain sensors by using a coaxial structure, prepared via coaxial wet spinning of thermoplastic elastomer-wrapped carbon nanotube fibers, is proposed. The sensors attain high sensitivity (with a gauge factor of 425 at 100% strain), high stretchability, and high linearity. They are also reproducible and durable. Their use as safe sensing components on deformable cable, expandable surfaces, and wearable textiles is demonstrated.

  9. Freezing of gait and fall detection in Parkinson's disease using wearable sensors: a systematic review.

    Science.gov (United States)

    Silva de Lima, Ana Lígia; Evers, Luc J W; Hahn, Tim; Bataille, Lauren; Hamilton, Jamie L; Little, Max A; Okuma, Yasuyuki; Bloem, Bastiaan R; Faber, Marjan J

    2017-08-01

    Despite the large number of studies that have investigated the use of wearable sensors to detect gait disturbances such as Freezing of gait (FOG) and falls, there is little consensus regarding appropriate methodologies for how to optimally apply such devices. Here, an overview of the use of wearable systems to assess FOG and falls in Parkinson's disease (PD) and validation performance is presented. A systematic search in the PubMed and Web of Science databases was performed using a group of concept key words. The final search was performed in January 2017, and articles were selected based upon a set of eligibility criteria. In total, 27 articles were selected. Of those, 23 related to FOG and 4 to falls. FOG studies were performed in either laboratory or home settings, with sample sizes ranging from 1 PD up to 48 PD presenting Hoehn and Yahr stage from 2 to 4. The shin was the most common sensor location and accelerometer was the most frequently used sensor type. Validity measures ranged from 73-100% for sensitivity and 67-100% for specificity. Falls and fall risk studies were all home-based, including samples sizes of 1 PD up to 107 PD, mostly using one sensor containing accelerometers, worn at various body locations. Despite the promising validation initiatives reported in these studies, they were all performed in relatively small sample sizes, and there was a significant variability in outcomes measured and results reported. Given these limitations, the validation of sensor-derived assessments of PD features would benefit from more focused research efforts, increased collaboration among researchers, aligning data collection protocols, and sharing data sets.

  10. Wearable Fall Detector using Integrated Sensors and Energy Devices

    OpenAIRE

    Sungmook Jung; Seungki Hong; Jaemin Kim; Sangkyu Lee; Taeghwan Hyeon; Minbaek Lee; Dae-Hyeong Kim

    2015-01-01

    Wearable devices have attracted great attentions as next-generation electronic devices. For the comfortable, portable, and easy-to-use system platform in wearable electronics, a key requirement is to replace conventional bulky and rigid energy devices into thin and deformable ones accompanying the capability of long-term energy supply. Here, we demonstrate a wearable fall detection system composed of a wristband-type deformable triboelectric generator and lithium ion battery in conjunction wi...

  11. Wearable Eating Habit Sensing System Using Internal Body Sound

    Science.gov (United States)

    Shuzo, Masaki; Komori, Shintaro; Takashima, Tomoko; Lopez, Guillaume; Tatsuta, Seiji; Yanagimoto, Shintaro; Warisawa, Shin'ichi; Delaunay, Jean-Jacques; Yamada, Ichiro

    Continuous monitoring of eating habits could be useful in preventing lifestyle diseases such as metabolic syndrome. Conventional methods consist of self-reporting and calculating mastication frequency based on the myoelectric potential of the masseter muscle. Both these methods are significant burdens for the user. We developed a non-invasive, wearable sensing system that can record eating habits over a long period of time in daily life. Our sensing system is composed of two bone conduction microphones placed in the ears that send internal body sound data to a portable IC recorder. Applying frequency spectrum analysis on the collected sound data, we could not only count the number of mastications during eating, but also accurately differentiate between eating, drinking, and speaking activities. This information can be used to evaluate the regularity of meals. Moreover, we were able to analyze sound features to classify the types of foods eaten by food texture.

  12. A Study of Wearable Bio-Sensor Technologies and Applications in Healthcare

    Directory of Open Access Journals (Sweden)

    Amir Mehmood

    2017-06-01

    Full Text Available In today’s world the rapid advancements in Micro-Electromechanical Systems (MEMS and Nano technology have improved almost all the aspects of daily life routine with the help of different smart devices such as smart phones, compact electronic devices etc. The prime example of these emerging developments is the development of wireless sensors for healthcare procedures. One kind of these sensors is wearable bio-sensors. In this paper, the technologies of two types of bio-sensors (ECG, EMG are investigated and also compared with traditional ECG, EMG equipment. We have taken SHIMMERTM wireless sensor platform as an example of wearable biosensors technology. We have investigated the systems developed for analysis techniques with SHIMMERTM ECG and EMG wearable bio-sensors and these biosensors are used in continuous remote monitoring. For example, applications in continuous health monitoring of elderly people, critical chronic patients and Fitness & Fatigue observations. Nevertheless, early fall detection in older adults and weak patients, treatment efficacy assessment. This study not only provides the basic concepts of wearable wireless bio-sensors networks (WBSN, but also provides basic knowledge of different sensor platforms available for patient’s remote monitoring. Also various healthcare applications by using bio-sensors are discussed and in last comparison with traditional ECG and EMG is presented.

  13. Identifying balance impairments in people with Parkinson's disease using video and wearable sensors.

    Science.gov (United States)

    Stack, Emma; Agarwal, Veena; King, Rachel; Burnett, Malcolm; Tahavori, Fatemeh; Janko, Balazs; Harwin, William; Ashburn, Ann; Kunkel, Dorit

    2018-05-01

    Falls and near falls are common among people with Parkinson's (PwP). To date, most wearable sensor research focussed on fall detection, few studies explored if wearable sensors can detect instability. Can instability (caution or near-falls) be detected using wearable sensors in comparison to video analysis? Twenty-four people (aged 60-86) with and without Parkinson's were recruited from community groups. Movements (e.g. walking, turning, transfers and reaching) were observed in the gait laboratory and/or at home; recorded using clinical measures, video and five wearable sensors (attached on the waist, ankles and wrists). After defining 'caution' and 'instability', two researchers evaluated video data and a third the raw wearable sensor data; blinded to each other's evaluations. Agreement between video and sensor data was calculated on stability, timing, step count and strategy. Data was available for 117 performances: 82 (70%) appeared stable on video. Ratings agreed in 86/117 cases (74%). Highest agreement was noted for chair transfer, timed up and go test and 3 m walks. Video analysts noted caution (slow, contained movements, safety-enhancing postures and concentration) and/or instability (saving reactions, stopping after stumbling or veering) in 40/134 performances (30%): raw wearable sensor data identified 16/35 performances rated cautious or unstable (sensitivity 46%) and 70/82 rated stable (specificity 85%). There was a 54% chance that a performance identified from wearable sensors as cautious/unstable was so; rising to 80% for stable movements. Agreement between wearable sensor and video data suggested that wearable sensors can detect subtle instability and near-falls. Caution and instability were observed in nearly a third of performances, suggesting that simple, mildly challenging actions, with clearly defined start- and end-points, may be most amenable to monitoring during free-living at home. Using the genuine near-falls recorded, work continues to

  14. Analysis of Indoor Rowing Motion using Wearable Inertial Sensors

    NARCIS (Netherlands)

    Bosch, S.; Shoaib, M.; Geerlings, Stephen; Buit, Lennart; Meratnia, Nirvana; Havinga, Paul J.M.

    2015-01-01

    In this exploratory work the motion of rowers is analyzed while rowing on a rowing machine. This is performed using inertial sensors that measure the orientation at several positions on the body. Using these measurements, this work provides a preliminary analysis of the differences between

  15. Sensitivity-Enhanced Wearable Active Voiceprint Sensor Based on Cellular Polypropylene Piezoelectret.

    Science.gov (United States)

    Li, Wenbo; Zhao, Sheng; Wu, Nan; Zhong, Junwen; Wang, Bo; Lin, Shizhe; Chen, Shuwen; Yuan, Fang; Jiang, Hulin; Xiao, Yongjun; Hu, Bin; Zhou, Jun

    2017-07-19

    Wearable active sensors have extensive applications in mobile biosensing and human-machine interaction but require good flexibility, high sensitivity, excellent stability, and self-powered feature. In this work, cellular polypropylene (PP) piezoelectret was chosen as the core material of a sensitivity-enhanced wearable active voiceprint sensor (SWAVS) to realize voiceprint recognition. By virtue of the dipole orientation control method, the air layers in the piezoelectret were efficiently utilized, and the current sensitivity was enhanced (from 1.98 pA/Hz to 5.81 pA/Hz at 115 dB). The SWAVS exhibited the superiorities of high sensitivity, accurate frequency response, and excellent stability. The voiceprint recognition system could make correct reactions to human voices by judging both the password and speaker. This study presented a voiceprint sensor with potential applications in noncontact biometric recognition and safety guarantee systems, promoting the progress of wearable sensor networks.

  16. Recognizing Multi-user Activities using Wearable Sensors in a Smart Home

    DEFF Research Database (Denmark)

    Wang, Liang; Gu, Tao; Tao, Xianping

    2010-01-01

    The advances of wearable sensors and wireless networks oer many opportunities to recognize human activities from sensor readings in pervasive computing. Existing work so far focuses mainly on recognizing activities of a single user in a home environment. However, there are typically multiple inha...

  17. How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review

    Directory of Open Access Journals (Sweden)

    Erika Rovini

    2017-10-01

    Full Text Available Background: Parkinson's disease (PD is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology. Objectives: This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON–OFF phases, and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring. Data sources: The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016. Study eligibility criteria: Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers. Results: Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease.

  18. How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review.

    Science.gov (United States)

    Rovini, Erika; Maremmani, Carlo; Cavallo, Filippo

    2017-01-01

    Background: Parkinson's disease (PD) is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology. This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON-OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring). The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016. Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers. Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease.

  19. Nondestructive Estimation of Muscle Contributions to STS Training with Different Loadings Based on Wearable Sensor System.

    Science.gov (United States)

    Liu, Kun; Liu, Yong; Yan, Jianchao; Sun, Zhenyuan

    2018-03-25

    Partial body weight support or loading sit-to-stand (STS) rehabilitation can be useful for persons with lower limb dysfunction to achieve movement again based on the internal residual muscle force and external assistance. To explicate how the muscles contribute to the kinetics and kinematics of STS performance by non-invasive in vitro detection and to nondestructively estimate the muscle contributions to STS training with different loadings, a wearable sensor system was developed with ground reaction force (GRF) platforms, motion capture inertial sensors and electromyography (EMG) sensors. To estimate the internal moments of hip, knee and ankle joints and quantify the contributions of individual muscle and gravity to STS movement, the inverse dynamics analysis on a simplified STS biomechanical model with external loading is proposed. The functional roles of the lower limb individual muscles (rectus femoris (RF), gluteus maximus (GM), vastus lateralis (VL), tibialis anterior (TA) and gastrocnemius (GAST)) during STS motion and the mechanism of the muscles' synergies to perform STS-specific subtasks were analyzed. The muscle contributions to the biomechanical STS subtasks of vertical propulsion, anteroposterior (AP) braking and propulsion for body balance in the sagittal plane were quantified by experimental studies with EMG, kinematic and kinetic data.

  20. Physiological Informatics: Collection and Analyses of Data from Wearable Sensors and Smartphone for Healthcare.

    Science.gov (United States)

    Bai, Jinwei; Shen, Li; Sun, Huimin; Shen, Bairong

    2017-01-01

    Physiological data from wearable sensors and smartphone are accumulating rapidly, and this provides us the chance to collect dynamic and personalized information as phenotype to be integrated to genotype for the holistic understanding of complex diseases. This integration can be applied to early prediction and prevention of disease, therefore promoting the shifting of disease care tradition to the healthcare paradigm. In this chapter, we summarize the physiological signals which can be detected by wearable sensors, the sharing of the physiological big data, and the mining methods for the discovery of disease-associated patterns for personalized diagnosis and treatment. We discuss the challenges of physiological informatics about the storage, the standardization, the analyses, and the applications of the physiological data from the wearable sensors and smartphone. At last, we present our perspectives on the models for disentangling the complex relationship between early disease prediction and the mining of physiological phenotype data.

  1. Sensors and wearable technologies in sport technologies, trends and approaches for implementation

    CERN Document Server

    James, Daniel A

    2016-01-01

    This book explores emerging trends in wearable sensors for sport and highlights the developments taking place. Drawing on the literature both the approaches and principals for the use of sensors in sport are outlined, and together with references to key works the reader finds this useful in considering such endeavours. The development of wearable technologies is fast paced and accompanying that is an exponential growth in the use and development of computing resources, thus while the review is comprehensive on content not all works can be included and given publication times will inevitably be somewhat dated. The illumination through trends, examples and principles are an aid for anyone considering the use of sensors and wearables in sports.

  2. Wearable E-Textile Technologies: A Review on Sensors, Actuators and Control Elements

    Directory of Open Access Journals (Sweden)

    Carlos Gonçalves

    2018-03-01

    Full Text Available Wearable e-textiles are able to perform electronic functions and are perceived as a way to add features into common wearable textiles, building competitive market advantages. The e-textile production has become not only a research effort but also an industrial production challenge. It is important to know how to use existing industrial processes or to develop new ones that are able to scale up production, ensuring the behavior and performance of prototypes. Despite the technical challenges, there are already some examples of wearable e-textiles where sensors, actuators, and production techniques were used to seamlessly embed electronic features into traditional wearable textiles, which allow for daily use without a bionic stigma.

  3. Design and Voluntary Motion Intention Estimation of a Novel Wearable Full-Body Flexible Exoskeleton Robot

    Directory of Open Access Journals (Sweden)

    Chunjie Chen

    2017-01-01

    Full Text Available The wearable full-body exoskeleton robot developed in this study is one application of mobile cyberphysical system (CPS, which is a complex mobile system integrating mechanics, electronics, computer science, and artificial intelligence. Steel wire was used as the flexible transmission medium and a group of special wire-locking structures was designed. Additionally, we designed passive joints for partial joints of the exoskeleton. Finally, we proposed a novel gait phase recognition method for full-body exoskeletons using only joint angular sensors, plantar pressure sensors, and inclination sensors. The method consists of four procedures. Firstly, we classified the three types of main motion patterns: normal walking on the ground, stair-climbing and stair-descending, and sit-to-stand movement. Secondly, we segregated the experimental data into one gait cycle. Thirdly, we divided one gait cycle into eight gait phases. Finally, we built a gait phase recognition model based on k-Nearest Neighbor perception and trained it with the phase-labeled gait data. The experimental result shows that the model has a 98.52% average correct rate of classification of the main motion patterns on the testing set and a 95.32% average correct rate of phase recognition on the testing set. So the exoskeleton robot can achieve human motion intention in real time and coordinate its movement with the wearer.

  4. A Low Power, Parallel Wearable Multi-Sensor System for Human Activity Evaluation.

    Science.gov (United States)

    Li, Yuecheng; Jia, Wenyan; Yu, Tianjian; Luan, Bo; Mao, Zhi-Hong; Zhang, Hong; Sun, Mingui

    2015-04-01

    In this paper, the design of a low power heterogeneous wearable multi-sensor system, built with Zynq System-on-Chip (SoC), for human activity evaluation is presented. The powerful data processing capability and flexibility of this SoC represent significant improvements over our previous ARM based system designs. The new system captures and compresses multiple color images and sensor data simultaneously. Several strategies are adopted to minimize power consumption. Our wearable system provides a new tool for the evaluation of human activity, including diet, physical activity and lifestyle.

  5. Laboratory Evaluation of Low-Cost Wearable Sensors for Measuring Head Impacts in Sports.

    Science.gov (United States)

    Tyson, Abigail M; Duma, Stefan M; Rowson, Steven

    2018-04-03

    Advances in low-cost wearable head impact sensor technology provide potential benefits regarding sports safety for both consumers and researchers. However, previous laboratory evaluations are not directly comparable and don't incorporate test conditions representative of unhelmeted impacts. This study addresses those limitations. The xPatch by X2 Biosystems and the SIM-G by Triax Technologies were placed on a NOCSAE headform with a Hybrid III neck which underwent impacts tests using a pendulum. Impact conditions included helmeted, padded impactor to bare head, and rigid impactor to bare head to represent long and short-duration impacts seen in helmeted and unhelmeted sports. The wearable sensors were evaluated on their kinematic accuracy by comparing results to reference sensors located at the headform center of gravity. Statistical tests for equivalence were performed on the slope of the linear regression between wearable sensors and reference. The xPatch gave equivalent measurements to the reference in select longer-duration impacts whereas the SIM-G had large variance leading to no equivalence. For the short-duration impacts, both wearable sensors underpredicted the reference. This error can be improved with increases in sampling rate from 1 to 1.5 kHz. Follow-up evaluations should be performed on the field to identify error in vivo. (197/200).

  6. Wearable sensor glove based on conducting fabric using electrodermal activity and pulse-wave sensors for e-health application.

    Science.gov (United States)

    Lee, Youngbum; Lee, Byungwoo; Lee, Myoungho

    2010-03-01

    Improvement of the quality and efficiency of health in medicine, both at home and the hospital, calls for improved sensors that might be included in a common carrier such as a wearable sensor device to measure various biosignals and provide healthcare services that use e-health technology. Designed to be user-friendly, smart clothes and gloves respond well to the end users for health monitoring. This study describes a wearable sensor glove that is equipped with an electrodermal activity (EDA) sensor, pulse-wave sensor, conducting fabric, and an embedded system. The EDA sensor utilizes the relationship between drowsiness and the EDA signal. The EDA sensors were made using a conducting fabric instead of silver chloride electrodes, as a more practical and practically wearable device. The pulse-wave sensor measurement system, which is widely applied in oriental medicinal practices, is also a strong element in e-health monitoring systems. The EDA and pulse-wave signal acquisition module was constructed by connecting the sensor to the glove via a conductive fabric. The signal acquisition module is then connected to a personal computer that displays the results of the EDA and pulse-wave signal processing analysis and gives accurate feedback to the user. This system is designed for a number of applications for the e-health services, including drowsiness detection and oriental medicine.

  7. Variables influencing wearable sensor outcome estimates in individuals with stroke and incomplete spinal cord injury: a pilot investigation validating two research grade sensors.

    Science.gov (United States)

    Jayaraman, Chandrasekaran; Mummidisetty, Chaithanya Krishna; Mannix-Slobig, Alannah; McGee Koch, Lori; Jayaraman, Arun

    2018-03-13

    Monitoring physical activity and leveraging wearable sensor technologies to facilitate active living in individuals with neurological impairment has been shown to yield benefits in terms of health and quality of living. In this context, accurate measurement of physical activity estimates from these sensors are vital. However, wearable sensor manufacturers generally only provide standard proprietary algorithms based off of healthy individuals to estimate physical activity metrics which may lead to inaccurate estimates in population with neurological impairment like stroke and incomplete spinal cord injury (iSCI). The main objective of this cross-sectional investigation was to evaluate the validity of physical activity estimates provided by standard proprietary algorithms for individuals with stroke and iSCI. Two research grade wearable sensors used in clinical settings were chosen and the outcome metrics estimated using standard proprietary algorithms were validated against designated golden standard measures (Cosmed K4B2 for energy expenditure and metabolic equivalent and manual tallying for step counts). The influence of sensor location, sensor type and activity characteristics were also studied. 28 participants (Healthy (n = 10); incomplete SCI (n = 8); stroke (n = 10)) performed a spectrum of activities in a laboratory setting using two wearable sensors (ActiGraph and Metria-IH1) at different body locations. Manufacturer provided standard proprietary algorithms estimated the step count, energy expenditure (EE) and metabolic equivalent (MET). These estimates were compared with the estimates from gold standard measures. For verifying validity, a series of Kruskal Wallis ANOVA tests (Games-Howell multiple comparison for post-hoc analyses) were conducted to compare the mean rank and absolute agreement of outcome metrics estimated by each of the devices in comparison with the designated gold standard measurements. The sensor type, sensor location

  8. A Wearable Wireless Sensor Network for Indoor Smart Environment Monitoring in Safety Applications.

    Science.gov (United States)

    Antolín, Diego; Medrano, Nicolás; Calvo, Belén; Pérez, Francisco

    2017-02-14

    This paper presents the implementation of a wearable wireless sensor network aimed at monitoring harmful gases in industrial environments. The proposed solution is based on a customized wearable sensor node using a low-power low-rate wireless personal area network (LR-WPAN) communications protocol, which as a first approach measures CO₂ concentration, and employs different low power strategies for appropriate energy handling which is essential to achieving long battery life. These wearables nodes are connected to a deployed static network and a web-based application allows data storage, remote control and monitoring of the complete network. Therefore, a complete and versatile remote web application with a locally implemented decision-making system is accomplished, which allows early detection of hazardous situations for exposed workers.

  9. A Wearable Wireless Sensor Network for Indoor Smart Environment Monitoring in Safety Applications

    Directory of Open Access Journals (Sweden)

    Diego Antolín

    2017-02-01

    Full Text Available This paper presents the implementation of a wearable wireless sensor network aimed at monitoring harmful gases in industrial environments. The proposed solution is based on a customized wearable sensor node using a low-power low-rate wireless personal area network (LR-WPAN communications protocol, which as a first approach measures CO2 concentration, and employs different low power strategies for appropriate energy handling which is essential to achieving long battery life. These wearables nodes are connected to a deployed static network and a web-based application allows data storage, remote control and monitoring of the complete network. Therefore, a complete and versatile remote web application with a locally implemented decision-making system is accomplished, which allows early detection of hazardous situations for exposed workers.

  10. Wearable multifunctional sensors using printed stretchable conductors made of silver nanowires

    Science.gov (United States)

    Yao, Shanshan; Zhu, Yong

    2014-01-01

    Considerable efforts have been made to achieve highly sensitive and wearable sensors that can simultaneously detect multiple stimuli such as stretch, pressure, temperature or touch. Here we develop highly stretchable multifunctional sensors that can detect strain (up to 50%), pressure (up to ~1.2 MPa) and finger touch with high sensitivity, fast response time (~40 ms) and good pressure mapping function. The reported sensors utilize the capacitive sensing mechanism, where silver nanowires are used as electrodes (conductors) and Ecoflex is used as a dielectric. The silver nanowire electrodes are screen printed. Our sensors have been demonstrated for several wearable applications including monitoring thumb movement, sensing the strain of the knee joint in patellar reflex (knee-jerk) and other human motions such as walking, running and jumping from squatting, illustrating the potential utilities of such sensors in robotic systems, prosthetics, healthcare and flexible touch panels.Considerable efforts have been made to achieve highly sensitive and wearable sensors that can simultaneously detect multiple stimuli such as stretch, pressure, temperature or touch. Here we develop highly stretchable multifunctional sensors that can detect strain (up to 50%), pressure (up to ~1.2 MPa) and finger touch with high sensitivity, fast response time (~40 ms) and good pressure mapping function. The reported sensors utilize the capacitive sensing mechanism, where silver nanowires are used as electrodes (conductors) and Ecoflex is used as a dielectric. The silver nanowire electrodes are screen printed. Our sensors have been demonstrated for several wearable applications including monitoring thumb movement, sensing the strain of the knee joint in patellar reflex (knee-jerk) and other human motions such as walking, running and jumping from squatting, illustrating the potential utilities of such sensors in robotic systems, prosthetics, healthcare and flexible touch panels. Electronic

  11. Flexible Nanowire Cluster as a Wearable Colorimetric Humidity Sensor.

    Science.gov (United States)

    Wei, Zhiqiang; Zhou, Zhang-Kai; Li, Qiuyu; Xue, Jiancai; Di Falco, Andrea; Yang, Zhongjian; Zhou, Jianhua; Wang, Xuehua

    2017-07-01

    Wearable plasmonic devices combine the advantages of high flexibility, ultrathinness, light weight, and excellent integration with the optical benefits mediated by plasmon-enhanced electric fields. However, two obstacles severely hinder further developments and applications of a wearable plasmonic device. One is the lack of efficient approach to obtaining devices with robust antimotion-interference property, i.e., the devices can work independently on the morphology changes of their working structures caused by arbitrary wearing conditions. The other issue is to seek a facile and high-throughput fabrication method to satisfy the financial requirement of industrialization. In order to overcome these two challenges, a functional flexible film of nanowire cluster is developed, which can be easily fabricated by taking the advantages of both conventional electrochemical and sputtering methods. Such flexible plasmonic films can be made into wearable devices that work independently on shape changes induced by various wearing conditions (such as bending, twisting and stretching). Furthermore, due to plasmonic advantages of color controlling and high sensitivity to environment changes, the flexible film of nanowire cluster can be used to fabricate wearable items (such as bracelet, clothes, bag, or even commercial markers), with the ability of wireless visualization for humidity sensing. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Identifying compensatory movement patterns in the upper extremity using a wearable sensor system.

    Science.gov (United States)

    Ranganathan, Rajiv; Wang, Rui; Dong, Bo; Biswas, Subir

    2017-11-30

    Movement impairments such as those due to stroke often result in the nervous system adopting atypical movements to compensate for movement deficits. Monitoring these compensatory patterns is critical for improving functional outcomes during rehabilitation. The purpose of this study was to test the feasibility and validity of a wearable sensor system for detecting compensatory trunk kinematics during activities of daily living. Participants with no history of neurological impairments performed reaching and manipulation tasks with their upper extremity, and their movements were recorded by a wearable sensor system and validated using a motion capture system. Compensatory movements of the trunk were induced using a brace that limited range of motion at the elbow. Our results showed that the elbow brace elicited compensatory movements of the trunk during reaching tasks but not manipulation tasks, and that a wearable sensor system with two sensors could reliably classify compensatory movements (~90% accuracy). These results show the potential of the wearable system to assess and monitor compensatory movements outside of a lab setting.

  13. Multimodal Teaching Analytics: Automated Extraction of Orchestration Graphs from Wearable Sensor Data

    Science.gov (United States)

    Prieto, L. P.; Sharma, K.; Kidzinski, L.; Rodríguez-Triana, M. J.; Dillenbourg, P.

    2018-01-01

    The pedagogical modelling of everyday classroom practice is an interesting kind of evidence, both for educational research and teachers' own professional development. This paper explores the usage of wearable sensors and machine learning techniques to automatically extract orchestration graphs (teaching activities and their social plane over time)…

  14. Can the effectiveness of an online stress management program be augmented by wearable sensor technology?

    Directory of Open Access Journals (Sweden)

    Abigail Millings

    2015-09-01

    Conclusions: The newly developed stress management program could be an effective way to improve student mental health. Wearable sensor technology, particularly biofeedback exercises, may be a useful contribution for the next generation of e-therapies, but further development of the prototypes is needed and their reliability and usability will likely affect user responses to them.

  15. Polydimethylsiloxane (PDMS-Based Flexible Resistive Strain Sensors for Wearable Applications

    Directory of Open Access Journals (Sweden)

    Jing Chen

    2018-02-01

    Full Text Available There is growing attention and rapid development on flexible electronic devices with electronic materials and sensing technology innovations. In particular, strain sensors with high elasticity and stretchability are needed for several potential applications including human entertainment technology, human–machine interface, personal healthcare, and sports performance monitoring, etc. This article presents recent advancements in the development of polydimethylsiloxane (PDMS-based flexible resistive strain sensors for wearable applications. First of all, the article shows that PDMS-based stretchable resistive strain sensors are successfully fabricated by different methods, such as the filtration method, printing technology, micromolding method, coating techniques, and liquid phase mixing. Next, strain sensing performances including stretchability, gauge factor, linearity, and durability are comprehensively demonstrated and compared. Finally, potential applications of PDMS-based flexible resistive strain sensors are also discussed. This review indicates that the era of wearable intelligent electronic systems has arrived.

  16. A Perspective on Wearable Sensor Measurements and Data Science for Parkinson's Disease.

    Science.gov (United States)

    Matias, Ricardo; Paixão, Vitor; Bouça, Raquel; Ferreira, Joaquim J

    2017-01-01

    Miniaturized and wearable sensor-based measurements enable the assessment of Parkinson's disease (PD) motor-related features like never before and hold great promise as non-invasive biomarkers for early and accurate diagnosis, and monitoring the progression of PD. High-fidelity human movement reconstruction and simulation can already be conducted in a clinical setting with increasingly precise and affordable motion technology enabling access to high-quality labeled data on patients' subcomponents of movement (kinematics and kinetics). At the same time, body-worn sensors now allow us to extend some quantitative movement-related measurements to patients' daily living activities. This era of patient movement "cognification" is bringing us previously inaccessible variables that encode patients' movement, and that, together with measures from clinical examinations, poses new challenges in data analysis. We present herein examples of the application of an unsupervised methodology to classify movement behavior in healthy individuals and patients with PD where no specific knowledge on the type of behaviors recorded is needed. We are most certainly leaving the early stage of the exponential curve that describes the current technological evolution and soon will be entering its steep ascent. But there is already a benefit to be derived from current motion technology and sophisticated data science methods to objectively measure parkinsonian impairments.

  17. Estimation of Individual Muscular Forces of the Lower Limb during Walking Using a Wearable Sensor System

    OpenAIRE

    Suin Kim; Kyongkwan Ro; Joonbum Bae

    2017-01-01

    Although various kinds of methodologies have been suggested to estimate individual muscular forces, many of them require a costly measurement system accompanied by complex preprocessing and postprocessing procedures. In this research, a simple wearable sensor system was developed, combined with the inverse dynamics-based static optimization method. The suggested method can be set up easily and can immediately convert motion information into muscular forces. The proposed sensor system consiste...

  18. Machine learning classification of medication adherence in patients with movement disorders using non-wearable sensors.

    Science.gov (United States)

    Tucker, Conrad S; Behoora, Ishan; Nembhard, Harriet Black; Lewis, Mechelle; Sterling, Nicholas W; Huang, Xuemei

    2015-11-01

    Medication non-adherence is a major concern in the healthcare industry and has led to increases in health risks and medical costs. For many neurological diseases, adherence to medication regimens can be assessed by observing movement patterns. However, physician observations are typically assessed based on visual inspection of movement and are limited to clinical testing procedures. Consequently, medication adherence is difficult to measure when patients are away from the clinical setting. The authors propose a data mining driven methodology that uses low cost, non-wearable multimodal sensors to model and predict patients' adherence to medication protocols, based on variations in their gait. The authors conduct a study involving Parkinson's disease patients that are "on" and "off" their medication in order to determine the statistical validity of the methodology. The data acquired can then be used to quantify patients' adherence while away from the clinic. Accordingly, this data-driven system may allow for early warnings regarding patient safety. Using whole-body movement data readings from the patients, the authors were able to discriminate between PD patients on and off medication, with accuracies greater than 97% for some patients using an individually customized model and accuracies of 78% for a generalized model containing multiple patient gait data. The proposed methodology and study demonstrate the potential and effectiveness of using low cost, non-wearable hardware and data mining models to monitor medication adherence outside of the traditional healthcare facility. These innovations may allow for cost effective, remote monitoring of treatment of neurological diseases. Copyright © 2015 Elsevier Ltd. All rights reserved.

  19. Effective Low-Power Wearable Wireless Surface EMG Sensor Design Based on Analog-Compressed Sensing

    Directory of Open Access Journals (Sweden)

    Mohammadreza Balouchestani

    2014-12-01

    Full Text Available Surface Electromyography (sEMG is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The main drawbacks of existing sEMG systems are: (1 they are not able to provide real-time monitoring; (2 they suffer from long processing time and low speed; (3 they are not effective for wireless healthcare systems because they consume huge power. In this work, we present an analog-based Compressed Sensing (CS architecture, which consists of three novel algorithms for design and implementation of wearable wireless sEMG bio-sensor. At the transmitter side, two new algorithms are presented in order to apply the analog-CS theory before Analog to Digital Converter (ADC. At the receiver side, a robust reconstruction algorithm based on a combination of ℓ1-ℓ1-optimization and Block Sparse Bayesian Learning (BSBL framework is presented to reconstruct the original bio-signals from the compressed bio-signals. The proposed architecture allows reducing the sampling rate to 25% of Nyquist Rate (NR. In addition, the proposed architecture reduces the power consumption to 40%, Percentage Residual Difference (PRD to 24%, Root Mean Squared Error (RMSE to 2%, and the computation time from 22 s to 9.01 s, which provide good background for establishing wearable wireless healthcare systems. The proposed architecture achieves robust performance in low Signal-to-Noise Ratio (SNR for the reconstruction process.

  20. Initial development and testing of a novel foam-based pressure sensor for wearable sensing

    Directory of Open Access Journals (Sweden)

    Smyth Barry

    2005-03-01

    Full Text Available Abstract Background This paper provides an overview of initial research conducted in the development of pressure-sensitive foam and its application in wearable sensing. The foam sensor is composed of polypyrrole-coated polyurethane foam, which exhibits a piezo-resistive reaction when exposed to electrical current. The use of this polymer-coated foam is attractive for wearable sensing due to the sensor's retention of desirable mechanical properties similar to those exhibited by textile structures. Methods The development of the foam sensor is described, as well as the development of a prototype sensing garment with sensors in several areas on the torso to measure breathing, shoulder movement, neck movement, and scapula pressure. Sensor properties were characterized, and data from pilot tests was examined visually. Results The foam exhibits a positive linear conductance response to increased pressure. Torso tests show that it responds in a predictable and measurable manner to breathing, shoulder movement, neck movement, and scapula pressure. Conclusion The polypyrrole foam shows considerable promise as a sensor for medical, wearable, and ubiquitous computing applications. Further investigation of the foam's consistency of response, durability over time, and specificity of response is necessary.

  1. A remote assessment system with a vision robot and wearable sensors.

    Science.gov (United States)

    Zhang, Tong; Wang, Jue; Ren, Yumiao; Li, Jianjun

    2004-01-01

    This paper describes an ongoing researched remote rehabilitation assessment system that has a 6-freedom double-eyes vision robot to catch vision information, and a group of wearable sensors to acquire biomechanical signals. A server computer is fixed on the robot, to provide services to the robot's controller and all the sensors. The robot is connected to Internet by wireless channel, and so do the sensors to the robot. Rehabilitation professionals can semi-automatically practise an assessment program via Internet. The preliminary results show that the smart device, including the robot and the sensors, can improve the quality of remote assessment, and reduce the complexity of operation at a distance.

  2. Barriers to the Adoption of Wearable Sensors in the Workplace: A Survey of Occupational Safety and Health Professionals.

    Science.gov (United States)

    Schall, Mark C; Sesek, Richard F; Cavuoto, Lora A

    2018-05-01

    To gather information on the (a) types of wearable sensors, particularly personal activity monitors, currently used by occupational safety and health (OSH) professionals; (b) potential benefits of using such technologies in the workplace; and (c) perceived barriers preventing the widespread adoption of wearable sensors in industry. Wearable sensors are increasingly being promoted as a means to improve employee health and well-being, and there is mounting evidence supporting their use as exposure assessment and personal health tools. Despite this, many workplaces have been hesitant to adopt these technologies. An electronic survey was emailed to 28,428 registered members of the American Society of Safety Engineers (ASSE) and 1,302 professionals certified by the Board of Certification in Professional Ergonomics (BCPE). A total of 952 valid responses were returned. Over half of respondents described being in favor of using wearable sensors to track OSH-related risk factors and relevant exposure metrics at their respective workplaces. However, barriers including concerns regarding employee privacy/confidentiality of collected data, employee compliance, sensor durability, the cost/benefit ratio of using wearables, and good manufacturing practice requirements were described as challenges precluding adoption. The broad adoption of wearable technologies appears to depend largely on the scientific community's ability to successfully address the identified barriers. Investigators may use the information provided to develop research studies that better address OSH practitioner concerns and help technology developers operationalize wearable sensors to improve employee health and well-being.

  3. Video Analysis Verification of Head Impact Events Measured by Wearable Sensors.

    Science.gov (United States)

    Cortes, Nelson; Lincoln, Andrew E; Myer, Gregory D; Hepburn, Lisa; Higgins, Michael; Putukian, Margot; Caswell, Shane V

    2017-08-01

    Wearable sensors are increasingly used to quantify the frequency and magnitude of head impact events in multiple sports. There is a paucity of evidence that verifies head impact events recorded by wearable sensors. To utilize video analysis to verify head impact events recorded by wearable sensors and describe the respective frequency and magnitude. Cohort study (diagnosis); Level of evidence, 2. Thirty male (mean age, 16.6 ± 1.2 years; mean height, 1.77 ± 0.06 m; mean weight, 73.4 ± 12.2 kg) and 35 female (mean age, 16.2 ± 1.3 years; mean height, 1.66 ± 0.05 m; mean weight, 61.2 ± 6.4 kg) players volunteered to participate in this study during the 2014 and 2015 lacrosse seasons. Participants were instrumented with GForceTracker (GFT; boys) and X-Patch sensors (girls). Simultaneous game video was recorded by a trained videographer using a single camera located at the highest midfield location. One-third of the field was framed and panned to follow the ball during games. Videographic and accelerometer data were time synchronized. Head impact counts were compared with video recordings and were deemed valid if (1) the linear acceleration was ≥20 g, (2) the player was identified on the field, (3) the player was in camera view, and (4) the head impact mechanism could be clearly identified. Descriptive statistics of peak linear acceleration (PLA) and peak rotational velocity (PRV) for all verified head impacts ≥20 g were calculated. For the boys, a total recorded 1063 impacts (2014: n = 545; 2015: n = 518) were logged by the GFT between game start and end times (mean PLA, 46 ± 31 g; mean PRV, 1093 ± 661 deg/s) during 368 player-games. Of these impacts, 690 were verified via video analysis (65%; mean PLA, 48 ± 34 g; mean PRV, 1242 ± 617 deg/s). The X-Patch sensors, worn by the girls, recorded a total 180 impacts during the course of the games, and 58 (2014: n = 33; 2015: n = 25) were verified via video analysis (32%; mean PLA, 39 ± 21 g; mean PRV, 1664

  4. PhysioDroid: combining wearable health sensors and mobile devices for a ubiquitous, continuous, and personal monitoring.

    Science.gov (United States)

    Banos, Oresti; Villalonga, Claudia; Damas, Miguel; Gloesekoetter, Peter; Pomares, Hector; Rojas, Ignacio

    2014-01-01

    Technological advances on the development of mobile devices, medical sensors, and wireless communication systems support a new generation of unobtrusive, portable, and ubiquitous health monitoring systems for continuous patient assessment and more personalized health care. There exist a growing number of mobile apps in the health domain; however, little contribution has been specifically provided, so far, to operate this kind of apps with wearable physiological sensors. The PhysioDroid, presented in this paper, provides a personalized means to remotely monitor and evaluate users' conditions. The PhysioDroid system provides ubiquitous and continuous vital signs analysis, such as electrocardiogram, heart rate, respiration rate, skin temperature, and body motion, intended to help empower patients and improve clinical understanding. The PhysioDroid is composed of a wearable monitoring device and an Android app providing gathering, storage, and processing features for the physiological sensor data. The versatility of the developed app allows its use for both average users and specialists, and the reduced cost of the PhysioDroid puts it at the reach of most people. Two exemplary use cases for health assessment and sports training are presented to illustrate the capabilities of the PhysioDroid. Next technical steps include generalization to other mobile platforms and health monitoring devices.

  5. Robotic Art for Wearable

    DEFF Research Database (Denmark)

    Lund, Henrik Hautop; Pagliarini, Luigi

    2010-01-01

    on “simple” plug-and-play circuits, ranging from pure sensors-actuators schemes to artefacts with a smaller level of elaboration complexity. Indeed, modular robotic wearable focuses on enhancing the body perception and proprioperception by trying to substitute all of the traditional exoskeletons perceptive...

  6. Modular Bayesian Networks with Low-Power Wearable Sensors for Recognizing Eating Activities.

    Science.gov (United States)

    Kim, Kee-Hoon; Cho, Sung-Bae

    2017-12-11

    Recently, recognizing a user's daily activity using a smartphone and wearable sensors has become a popular issue. However, in contrast with the ideal definition of an experiment, there could be numerous complex activities in real life with respect to its various background and contexts: time, space, age, culture, and so on. Recognizing these complex activities with limited low-power sensors, considering the power and memory constraints of the wearable environment and the user's obtrusiveness at once is not an easy problem, although it is very crucial for the activity recognizer to be practically useful. In this paper, we recognize activity of eating, which is one of the most typical examples of a complex activity, using only daily low-power mobile and wearable sensors. To organize the related contexts systemically, we have constructed the context model based on activity theory and the "Five W's", and propose a Bayesian network with 88 nodes to predict uncertain contexts probabilistically. The structure of the proposed Bayesian network is designed by a modular and tree-structured approach to reduce the time complexity and increase the scalability. To evaluate the proposed method, we collected the data with 10 different activities from 25 volunteers of various ages, occupations, and jobs, and have obtained 79.71% accuracy, which outperforms other conventional classifiers by 7.54-14.4%. Analyses of the results showed that our probabilistic approach could also give approximate results even when one of contexts or sensor values has a very heterogeneous pattern or is missing.

  7. A compact perspiration meter system with capacitive humidity sensor for wearable health-care applications

    Science.gov (United States)

    Mitani, Yusuke; Miyaji, Kousuke; Kaneko, Satoshi; Uekura, Takaharu; Momose, Hideya; Johguchi, Koh

    2018-04-01

    This paper presents a compact wearable perspiration meter system using a 180-nm CMOS technology. With custom chip and board design, the proposed perspiration meter, which can measure a qualitative sweating rate, is integrated into 15 × 20 mm2. From the experimental results, the capacitances of the humidity sensors with analog-to-digital converter and band-gap reference circuits can operate accurately without hysteresis. In addition, a demonstration with simulated human skin is carried out to investigate the sensor’s performance under real environments. The proposed perspiration meter can output values equivalent to a conventional meter. As a result, it is verified that the proposed system can be used as a human sweat sensor for wearable application.

  8. Fall risks assessment among community dwelling elderly using wearable wireless sensors

    Science.gov (United States)

    Lockhart, Thurmon E.; Soangra, Rahul; Frames, Chris

    2014-06-01

    Postural stability characteristics are considered to be important in maintaining functional independence free of falls and healthy life style especially for the growing elderly population. This study focuses on developing tools of clinical value in fall prevention: 1) Implementation of sensors that are minimally obtrusive and reliably record movement data. 2) Unobtrusively gather data from wearable sensors from four community centers 3) developed and implemented linear and non-linear signal analysis algorithms to extract clinically relevant information using wearable technology. In all a total of 100 community dwelling elderly individuals (66 non-fallers and 34 fallers) participated in the experiment. All participants were asked to stand-still in eyes open (EO) and eyes closed (EC) condition on forceplate with one wireless inertial sensor affixed at sternum level. Participants' history of falls had been recorded for last 2 years, with emphasis on frequency and characteristics of falls. Any participant with at least one fall in the prior year were classified as faller and the others as non-faller. The results indicated several key factors/features of postural characteristics relevant to balance control and stability during quite stance and, showed good predictive capability of fall risks among older adults. Wearable technology allowed us to gather data where it matters the most to answer fall related questions, i.e. the community setting environments. This study opens new prospects of clinical testing using postural variables with a wearable sensor that may be relevant for assessing fall risks at home and patient environment in near future.

  9. Estimation of Temporal Gait Parameters Using a Wearable Microphone-Sensor-Based System

    Directory of Open Access Journals (Sweden)

    Cheng Wang

    2016-12-01

    Full Text Available Most existing wearable gait analysis methods focus on the analysis of data obtained from inertial sensors. This paper proposes a novel, low-cost, wireless and wearable gait analysis system which uses microphone sensors to collect footstep sound signals during walking. This is the first time a microphone sensor is used as a wearable gait analysis device as far as we know. Based on this system, a gait analysis algorithm for estimating the temporal parameters of gait is presented. The algorithm fully uses the fusion of two feet footstep sound signals and includes three stages: footstep detection, heel-strike event and toe-on event detection, and calculation of gait temporal parameters. Experimental results show that with a total of 240 data sequences and 1732 steps collected using three different gait data collection strategies from 15 healthy subjects, the proposed system achieves an average 0.955 F1-measure for footstep detection, an average 94.52% accuracy rate for heel-strike detection and 94.25% accuracy rate for toe-on detection. Using these detection results, nine temporal related gait parameters are calculated and these parameters are consistent with their corresponding normal gait temporal parameters and labeled data calculation results. The results verify the effectiveness of our proposed system and algorithm for temporal gait parameter estimation.

  10. A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients

    Directory of Open Access Journals (Sweden)

    Lei Yu

    2016-02-01

    Full Text Available Clinical rehabilitation assessment is an important part of the therapy process because it is the premise for prescribing suitable rehabilitation interventions. However, the commonly used assessment scales have the following two drawbacks: (1 they are susceptible to subjective factors; (2 they only have several rating levels and are influenced by a ceiling effect, making it impossible to exactly detect any further improvement in the movement. Meanwhile, energy constraints are a primary design consideration in wearable sensor network systems since they are often battery-operated. Traditionally, for wearable sensor network systems that follow the Shannon/Nyquist sampling theorem, there are many data that need to be sampled and transmitted. This paper proposes a novel wearable sensor network system to monitor and quantitatively assess the upper limb motion function, based on compressed sensing technology. With the sparse representation model, less data is transmitted to the computer than with traditional systems. The experimental results show that the accelerometer signals of Bobath handshake and shoulder touch exercises can be compressed, and the length of the compressed signal is less than 1/3 of the raw signal length. More importantly, the reconstruction errors have no influence on the predictive accuracy of the Brunnstrom stage classification model. It also indicated that the proposed system can not only reduce the amount of data during the sampling and transmission processes, but also, the reconstructed accelerometer signals can be used for quantitative assessment without any loss of useful information.

  11. A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients

    Science.gov (United States)

    Yu, Lei; Xiong, Daxi; Guo, Liquan; Wang, Jiping

    2016-01-01

    Clinical rehabilitation assessment is an important part of the therapy process because it is the premise for prescribing suitable rehabilitation interventions. However, the commonly used assessment scales have the following two drawbacks: (1) they are susceptible to subjective factors; (2) they only have several rating levels and are influenced by a ceiling effect, making it impossible to exactly detect any further improvement in the movement. Meanwhile, energy constraints are a primary design consideration in wearable sensor network systems since they are often battery-operated. Traditionally, for wearable sensor network systems that follow the Shannon/Nyquist sampling theorem, there are many data that need to be sampled and transmitted. This paper proposes a novel wearable sensor network system to monitor and quantitatively assess the upper limb motion function, based on compressed sensing technology. With the sparse representation model, less data is transmitted to the computer than with traditional systems. The experimental results show that the accelerometer signals of Bobath handshake and shoulder touch exercises can be compressed, and the length of the compressed signal is less than 1/3 of the raw signal length. More importantly, the reconstruction errors have no influence on the predictive accuracy of the Brunnstrom stage classification model. It also indicated that the proposed system can not only reduce the amount of data during the sampling and transmission processes, but also, the reconstructed accelerometer signals can be used for quantitative assessment without any loss of useful information. PMID:26861337

  12. Wearable Wireless Sensor for Multi-Scale Physiological Monitoring

    Science.gov (United States)

    2013-10-01

    Davies, E. Williams, P. Rutherford, and L. Gemmell. Effect of introducing the Modified Early Warning score on clinical outcomes, cardio -pulmonary...reduced problems with adherence and almost eliminated skin complications from sensor heat or reaction to adhesive materials. Improvements in sensor

  13. Wireless body sensor networks for health-monitoring applications

    International Nuclear Information System (INIS)

    Hao, Yang; Foster, Robert

    2008-01-01

    Current wireless technologies, such as wireless body area networks and wireless personal area networks, provide promising applications in medical monitoring systems to measure specified physiological data and also provide location-based information, if required. With the increasing sophistication of wearable and implantable medical devices and their integration with wireless sensors, an ever-expanding range of therapeutic and diagnostic applications is being pursued by research and commercial organizations. This paper aims to provide a comprehensive review of recent developments in wireless sensor technology for monitoring behaviour related to human physiological responses. It presents background information on the use of wireless technology and sensors to develop a wireless physiological measurement system. A generic miniature platform and other available technologies for wireless sensors have been studied in terms of hardware and software structural requirements for a low-cost, low-power, non-invasive and unobtrusive system. (topical review)

  14. Wearable dry sensors with bluetooth connection for use in remote patient monitoring systems.

    Science.gov (United States)

    Gargiulo, Gaetano; Bifulco, Paolo; Cesarelli, Mario; Jin, Craig; McEwan, Alistair; van Schaik, Andre

    2010-01-01

    Cost reduction has become the primary theme of healthcare reforms globally. More providers are moving towards remote patient monitoring, which reduces the length of hospital stays and frees up their physicians and nurses for acute cases and helps them to tackle staff shortages. Physiological sensors are commonly used in many human specialties e.g. electrocardiogram (ECG) electrodes, for monitoring heart signals, and electroencephalogram (EEG) electrodes, for sensing the electrical activity of the brain, are the most well-known applications. Consequently there is a substantial unmet need for physiological sensors that can be simply and easily applied by the patient or primary carer, are comfortable to wear, can accurately sense parameters over long periods of time and can be connected to data recording systems using Bluetooth technology. We have developed a small, battery powered, user customizable portable monitor. This prototype is capable of recording three-axial body acceleration, skin temperature, and has up to four bio analogical front ends. Moreover, it is also able of continuous wireless transmission to any Bluetooth device including a PDA or a cellular phone. The bio-front end can use long-lasting dry electrodes or novel textile electrodes that can be embedded in clothes. The device can be powered by a standard mobile phone which has a Ni-MH 3.6 V battery, to sustain more than seven days continuous functioning when using the Bluetooth Sniff mode to reduce TX power. In this paper, we present some of the evaluation experiments of our wearable personal monitor device with a focus on ECG applications.

  15. Dealing with the Effects of Sensor Displacement in Wearable Activity Recognition

    Directory of Open Access Journals (Sweden)

    Oresti Banos

    2014-06-01

    Full Text Available Most wearable activity recognition systems assume a predefined sensor deployment that remains unchanged during runtime. However, this assumption does not reflect real-life conditions. During the normal use of such systems, users may place the sensors in a position different from the predefined sensor placement. Also, sensors may move from their original location to a different one, due to a loose attachment. Activity recognition systems trained on activity patterns characteristic of a given sensor deployment may likely fail due to sensor displacements. In this work, we innovatively explore the effects of sensor displacement induced by both the intentional misplacement of sensors and self-placement by the user. The effects of sensor displacement are analyzed for standard activity recognition techniques, as well as for an alternate robust sensor fusion method proposed in a previous work. While classical recognition models show little tolerance to sensor displacement, the proposed method is proven to have notable capabilities to assimilate the changes introduced in the sensor position due to self-placement and provides considerable improvements for large misplacements.

  16. The Use of Wearable Inertial Motion Sensors in Human Lower Limb Biomechanics Studies: A Systematic Review

    Directory of Open Access Journals (Sweden)

    Yue-Yan Chan

    2010-12-01

    Full Text Available Wearable motion sensors consisting of accelerometers, gyroscopes and magnetic sensors are readily available nowadays. The small size and low production costs of motion sensors make them a very good tool for human motions analysis. However, data processing and accuracy of the collected data are important issues for research purposes. In this paper, we aim to review the literature related to usage of inertial sensors in human lower limb biomechanics studies. A systematic search was done in the following search engines: ISI Web of Knowledge, Medline, SportDiscus and IEEE Xplore. Thirty nine full papers and conference abstracts with related topics were included in this review. The type of sensor involved, data collection methods, study design, validation methods and its applications were reviewed.

  17. The use of wearable inertial motion sensors in human lower limb biomechanics studies: a systematic review.

    Science.gov (United States)

    Fong, Daniel Tik-Pui; Chan, Yue-Yan

    2010-01-01

    Wearable motion sensors consisting of accelerometers, gyroscopes and magnetic sensors are readily available nowadays. The small size and low production costs of motion sensors make them a very good tool for human motions analysis. However, data processing and accuracy of the collected data are important issues for research purposes. In this paper, we aim to review the literature related to usage of inertial sensors in human lower limb biomechanics studies. A systematic search was done in the following search engines: ISI Web of Knowledge, Medline, SportDiscus and IEEE Xplore. Thirty nine full papers and conference abstracts with related topics were included in this review. The type of sensor involved, data collection methods, study design, validation methods and its applications were reviewed.

  18. Efficient Skin Temperature Sensor and Stable Gel-Less Sticky ECG Sensor for a Wearable Flexible Healthcare Patch.

    Science.gov (United States)

    Yamamoto, Yuki; Yamamoto, Daisuke; Takada, Makoto; Naito, Hiroyoshi; Arie, Takayuki; Akita, Seiji; Takei, Kuniharu

    2017-09-01

    Wearable, flexible healthcare devices, which can monitor health data to predict and diagnose disease in advance, benefit society. Toward this future, various flexible and stretchable sensors as well as other components are demonstrated by arranging materials, structures, and processes. Although there are many sensor demonstrations, the fundamental characteristics such as the dependence of a temperature sensor on film thickness and the impact of adhesive for an electrocardiogram (ECG) sensor are yet to be explored in detail. In this study, the effect of film thickness for skin temperature measurements, adhesive force, and reliability of gel-less ECG sensors as well as an integrated real-time demonstration is reported. Depending on the ambient conditions, film thickness strongly affects the precision of skin temperature measurements, resulting in a thin flexible film suitable for a temperature sensor in wearable device applications. Furthermore, by arranging the material composition, stable gel-less sticky ECG electrodes are realized. Finally, real-time simultaneous skin temperature and ECG signal recordings are demonstrated by attaching an optimized device onto a volunteer's chest. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  19. A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices.

    Science.gov (United States)

    Ravi, Daniele; Wong, Charence; Lo, Benny; Yang, Guang-Zhong

    2017-01-01

    The increasing popularity of wearable devices in recent years means that a diverse range of physiological and functional data can now be captured continuously for applications in sports, wellbeing, and healthcare. This wealth of information requires efficient methods of classification and analysis where deep learning is a promising technique for large-scale data analytics. While deep learning has been successful in implementations that utilize high-performance computing platforms, its use on low-power wearable devices is limited by resource constraints. In this paper, we propose a deep learning methodology, which combines features learned from inertial sensor data together with complementary information from a set of shallow features to enable accurate and real-time activity classification. The design of this combined method aims to overcome some of the limitations present in a typical deep learning framework where on-node computation is required. To optimize the proposed method for real-time on-node computation, spectral domain preprocessing is used before the data are passed onto the deep learning framework. The classification accuracy of our proposed deep learning approach is evaluated against state-of-the-art methods using both laboratory and real world activity datasets. Our results show the validity of the approach on different human activity datasets, outperforming other methods, including the two methods used within our combined pipeline. We also demonstrate that the computation times for the proposed method are consistent with the constraints of real-time on-node processing on smartphones and a wearable sensor platform.

  20. Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models.

    Science.gov (United States)

    Beltrame, Thomas; Amelard, Robert; Wong, Alexander; Hughson, Richard L

    2018-02-01

    Physical activity levels are related through algorithms to the energetic demand, with no information regarding the integrity of the multiple physiological systems involved in the energetic supply. Longitudinal analysis of the oxygen uptake (V̇o 2 ) by wearable sensors in realistic settings might permit development of a practical tool for the study of the longitudinal aerobic system dynamics (i.e., V̇o 2 kinetics). This study evaluated aerobic system dynamics based on predicted V̇o 2 data obtained from wearable sensors during unsupervised activities of daily living (μADL). Thirteen healthy men performed a laboratory-controlled moderate exercise protocol and were monitored for ≈6 h/day for 4 days (μADL data). Variables derived from hip accelerometer (ACC HIP ), heart rate monitor, and respiratory bands during μADL were extracted and processed by a validated random forest regression model to predict V̇o 2 . The aerobic system analysis was based on the frequency-domain analysis of ACC HIP and predicted V̇o 2 data obtained during μADL. Optimal samples for frequency domain analysis (constrained to ≤0.01 Hz) were selected when ACC HIP was higher than 0.05 g at a given frequency (i.e., participants were active). The temporal characteristics of predicted V̇o 2 data during μADL correlated with the temporal characteristics of measured V̇o 2 data during laboratory-controlled protocol ([Formula: see text] = 0.82, P system dynamics can be investigated during unsupervised activities of daily living by wearable sensors. Although speculative, these algorithms have the potential to be incorporated into wearable systems for early detection of changes in health status in realistic environments by detecting changes in aerobic response dynamics. NEW & NOTEWORTHY The early detection of subclinical aerobic system impairments might be indicative of impaired physiological reserves that impact the capacity for physical activity. This study is the first to use wearable

  1. 3D printing of wearable fractal-based sensor systems for neurocardiology and healthcare

    Science.gov (United States)

    Ramasamy, Mouli; Varadan, Vijay K.

    2017-04-01

    Neurocardiology is the pathophysiological interplay of nervous and cardiovascular systems. The communication between the heart and brain has revealed various methodologies in healthcare that could be investigated to study the heart-brain interactions and other cardiovascular and neurological diseases. A textile based wearable nanosensor system in the form of e-bra, e-shirt, e-headband, e-brief, underwear etc, was presented in this SPIE conferences earlier for noninvasive recording of EEG and EKG, and showing the correlation between the brain and heart signals. In this paper, the technology is expanded further using fractal based geometries using 3D printing system for low cost and flexible wearable sensor system for healthcare.

  2. Fabrication of a Textile-Based Wearable Blood Leakage Sensor Using Screen-Offset Printing

    Directory of Open Access Journals (Sweden)

    Ken-ichi Nomura

    2018-01-01

    Full Text Available We fabricate a wearable blood leakage sensor on a cotton textile by combining two newly developed techniques. First, we employ a screen-offset printing technique that avoids blurring, short circuiting between adjacent conductive patterns, and electrode fracturing to form an interdigitated electrode structure for the sensor on a textile. Furthermore, we develop a scheme to distinguish blood from other substances by utilizing the specific dielectric dispersion of blood observed in the sub-megahertz frequency range. The sensor can detect blood volumes as low as 15 μL, which is significantly lower than those of commercially available products (which can detect approximately 1 mL of blood and comparable to a recently reported value of approximately 10 μL. In this study, we merge two technologies to develop a more practical skin-friendly sensor that can be applied for safe, stress-free blood leakage monitoring during hemodialysis.

  3. Textile-Based, Interdigital, Capacitive, Soft-Strain Sensor for Wearable Applications

    Directory of Open Access Journals (Sweden)

    Ozgur Atalay

    2018-05-01

    Full Text Available The electronic textile area has gained considerable attention due to its implementation of wearable devices, and soft sensors are the main components of these systems. In this paper, a new sensor design is presented to create stretchable, capacitance-based strain sensors for human motion tracking. This involves the use of stretchable, conductive-knit fabric within the silicone elastomer matrix, as interdigitated electrodes. While conductive fabric creates a secure conductive network for electrodes, a silicone-based matrix provides encapsulation and dimensional-stability to the structure. During the benchtop characterization, sensors show linear output, i.e., R2 = 0.997, with high response time, i.e., 50 ms, and high resolution, i.e., 1.36%. Finally, movement of the knee joint during the different scenarios was successfully recorded.

  4. Communication Challenges in on-Body and Body-to-Body Wearable Wireless Networks—A Connectivity Perspective

    Directory of Open Access Journals (Sweden)

    Dhafer Ben Arbia

    2017-07-01

    Full Text Available Wearable wireless networks (WWNs offer innovative ways to connect humans and/or objects anywhere, anytime, within an infinite variety of applications. WWNs include three levels of communications: on-body, body-to-body and off-body communication. Successful communication in on-body and body-to-body networks is often challenging due to ultra-low power consumption, processing and storage capabilities, which have a significant impact on the achievable throughput and packet reception ratio as well as latency. Consequently, all these factors make it difficult to opt for an appropriate technology to optimize communication performance, which predominantly depends on the given application. In particular, this work emphasizes the impact of coarse-grain factors (such as dynamic and diverse mobility, radio-link and signal propagation, interference management, data dissemination schemes, and routing approaches directly affecting the communication performance in WWNs. Experiments have been performed on a real testbed to investigate the connectivity behavior on two wireless communication levels: on-body and body-to-body. It is concluded that by considering the impact of above-mentioned factors, the general perception of using specific technologies may not be correct. Indeed, for on-body communication, by using the IEEE 802.15.6 standard (which is specifically designed for on-body communication, it is observed that while operating at low transmission power under realistic conditions, the connectivity can be significantly low, thus, the transmission power has to be tuned carefully. Similarly, for body-to-body communication in an indoor environment, WiFi IEEE 802.11n also has a high threshold of end-to-end disconnections beyond two hops (approximatively 25 m. Therefore, these facts promote the use of novel technologies such as 802.11ac, NarrowBand-IoT (NB-IoT etc. as possible candidates for body-to-body communications as a part of the Internet of humans concept.

  5. RGO-coated elastic fibres as wearable strain sensors for full-scale detection of human motions

    Science.gov (United States)

    Mi, Qing; Wang, Qi; Zang, Siyao; Mao, Guoming; Zhang, Jinnan; Ren, Xiaomin

    2018-01-01

    In this study, we chose highly-elastic fabric fibres as the functional carrier and then simply coated the fibres with reduced graphene oxide (rGO) using plasma treatment, dip coating and hydrothermal reduction steps, finally making a wearable strain sensor. As a result, the full-scale detection of human motions, ranging from bending joints to the pulse beat, has been achieved by these sensors. Moreover, high sensitivity, good stability and excellent repeatability were realized. The good sensing performances and economical fabrication process of this wearable strain sensor have strengthened our confidence in practical applications in smart clothing, smart fabrics, healthcare, and entertainment fields.

  6. A highly sensitive, low-cost, wearable pressure sensor based on conductive hydrogel spheres

    KAUST Repository

    Tai, Yanlong

    2015-01-01

    Wearable pressure sensing solutions have promising future for practical applications in health monitoring and human/machine interfaces. Here, a highly sensitive, low-cost, wearable pressure sensor based on conductive single-walled carbon nanotube (SWCNT)/alginate hydrogel spheres is reported. Conductive and piezoresistive spheres are embedded between conductive electrodes (indium tin oxide-coated polyethylene terephthalate films) and subjected to environmental pressure. The detection mechanism is based on the piezoresistivity of the SWCNT/alginate conductive spheres and on the sphere-electrode contact. Step-by-step, we optimized the design parameters to maximize the sensitivity of the sensor. The optimized hydrogel sensor exhibited a satisfactory sensitivity (0.176 ΔR/R0/kPa-1) and a low detectable limit (10 Pa). Moreover, a brief response time (a few milliseconds) and successful repeatability were also demonstrated. Finally, the efficiency of this strategy was verified through a series of practical tests such as monitoring human wrist pulse, detecting throat muscle motion or identifying the location and the distribution of an external pressure using an array sensor (4 × 4). © 2015 The Royal Society of Chemistry.

  7. Modular Bayesian Networks with Low-Power Wearable Sensors for Recognizing Eating Activities

    Directory of Open Access Journals (Sweden)

    Kee-Hoon Kim

    2017-12-01

    Full Text Available Recently, recognizing a user’s daily activity using a smartphone and wearable sensors has become a popular issue. However, in contrast with the ideal definition of an experiment, there could be numerous complex activities in real life with respect to its various background and contexts: time, space, age, culture, and so on. Recognizing these complex activities with limited low-power sensors, considering the power and memory constraints of the wearable environment and the user’s obtrusiveness at once is not an easy problem, although it is very crucial for the activity recognizer to be practically useful. In this paper, we recognize activity of eating, which is one of the most typical examples of a complex activity, using only daily low-power mobile and wearable sensors. To organize the related contexts systemically, we have constructed the context model based on activity theory and the “Five W’s”, and propose a Bayesian network with 88 nodes to predict uncertain contexts probabilistically. The structure of the proposed Bayesian network is designed by a modular and tree-structured approach to reduce the time complexity and increase the scalability. To evaluate the proposed method, we collected the data with 10 different activities from 25 volunteers of various ages, occupations, and jobs, and have obtained 79.71% accuracy, which outperforms other conventional classifiers by 7.54–14.4%. Analyses of the results showed that our probabilistic approach could also give approximate results even when one of contexts or sensor values has a very heterogeneous pattern or is missing.

  8. Modular Bayesian Networks with Low-Power Wearable Sensors for Recognizing Eating Activities

    Science.gov (United States)

    Kim, Kee-Hoon

    2017-01-01

    Recently, recognizing a user’s daily activity using a smartphone and wearable sensors has become a popular issue. However, in contrast with the ideal definition of an experiment, there could be numerous complex activities in real life with respect to its various background and contexts: time, space, age, culture, and so on. Recognizing these complex activities with limited low-power sensors, considering the power and memory constraints of the wearable environment and the user’s obtrusiveness at once is not an easy problem, although it is very crucial for the activity recognizer to be practically useful. In this paper, we recognize activity of eating, which is one of the most typical examples of a complex activity, using only daily low-power mobile and wearable sensors. To organize the related contexts systemically, we have constructed the context model based on activity theory and the “Five W’s”, and propose a Bayesian network with 88 nodes to predict uncertain contexts probabilistically. The structure of the proposed Bayesian network is designed by a modular and tree-structured approach to reduce the time complexity and increase the scalability. To evaluate the proposed method, we collected the data with 10 different activities from 25 volunteers of various ages, occupations, and jobs, and have obtained 79.71% accuracy, which outperforms other conventional classifiers by 7.54–14.4%. Analyses of the results showed that our probabilistic approach could also give approximate results even when one of contexts or sensor values has a very heterogeneous pattern or is missing. PMID:29232937

  9. Performance of human body communication-based wearable ECG with capacitive coupling electrodes.

    Science.gov (United States)

    Sakuma, Jun; Anzai, Daisuke; Wang, Jianqing

    2016-09-01

    Wearable electrocardiogram (ECG) is attracting much attention in daily healthcare applications, and human body communication (HBC) technology provides an evident advantage in making the sensing electrodes of ECG also working for transmission through the human body. In view of actual usage in daily life, however, non-contact electrodes to the human body are desirable. In this Letter, the authors discussed the ECG circuit structure in the HBC-based wearable ECG for removing the common mode noise when employing non-contact capacitive coupling electrodes. Through the comparison of experimental results, they have shown that the authors' proposed circuit structure with the third electrode directly connected to signal ground can provide an effect on common mode noise reduction similar to the usual drive-right-leg circuit, and a sufficiently good acquisition performance of ECG signals.

  10. Light-controlling, flexible and transparent ethanol gas sensor based on ZnO nanoparticles for wearable devices.

    Science.gov (United States)

    Zheng, Z Q; Yao, J D; Wang, B; Yang, G W

    2015-06-16

    In recent years, owing to the significant applications of health monitoring, wearable electronic devices such as smart watches, smart glass and wearable cameras have been growing rapidly. Gas sensor is an important part of wearable electronic devices for detecting pollutant, toxic, and combustible gases. However, in order to apply to wearable electronic devices, the gas sensor needs flexible, transparent, and working at room temperature, which are not available for traditional gas sensors. Here, we for the first time fabricate a light-controlling, flexible, transparent, and working at room-temperature ethanol gas sensor by using commercial ZnO nanoparticles. The fabricated sensor not only exhibits fast and excellent photoresponse, but also shows high sensing response to ethanol under UV irradiation. Meanwhile, its transmittance exceeds 62% in the visible spectral range, and the sensing performance keeps the same even bent it at a curvature angle of 90(o). Additionally, using commercial ZnO nanoparticles provides a facile and low-cost route to fabricate wearable electronic devices.

  11. Wearable Wireless Sensor for Multi-Scale Physiological Monitoring

    Science.gov (United States)

    2014-10-01

    0.22 for the head and finger data, respectively. Finger PPG waveforms for some people contain prominent traces of dicrotic notch , which results in...frequency components introduced by the dicrotic notch . It can also be speculated that PPG recorded from peripheral part (finger) is modulated at a...articles (3 articles in Annals of BME rated as one of the top journals in BME with an impact factor of 3.2, and 1 article in the journal Sensors

  12. Wearable, Flexible, and Multifunctional Healthcare Device with an ISFET Chemical Sensor for Simultaneous Sweat pH and Skin Temperature Monitoring.

    Science.gov (United States)

    Nakata, Shogo; Arie, Takayuki; Akita, Seiji; Takei, Kuniharu

    2017-03-24

    Real-time daily healthcare monitoring may increase the chances of predicting and diagnosing diseases in their early stages which, currently, occurs most frequently during medical check-ups. Next-generation noninvasive healthcare devices, such as flexible multifunctional sensor sheets designed to be worn on skin, are considered to be highly suitable candidates for continuous real-time health monitoring. For healthcare applications, acquiring data on the chemical state of the body, alongside physical characteristics such as body temperature and activity, are extremely important for predicting and identifying potential health conditions. To record these data, in this study, we developed a wearable, flexible sweat chemical sensor sheet for pH measurement, consisting of an ion-sensitive field-effect transistor (ISFET) integrated with a flexible temperature sensor: we intend to use this device as the foundation of a fully integrated, wearable healthcare patch in the future. After characterizing the performance, mechanical flexibility, and stability of the sensor, real-time measurements of sweat pH and skin temperature are successfully conducted through skin contact. This flexible integrated device has the potential to be developed into a chemical sensor for sweat for applications in healthcare and sports.

  13. Wearable-Sensor-Based Classification Models of Faller Status in Older Adults.

    Directory of Open Access Journals (Sweden)

    Jennifer Howcroft

    Full Text Available Wearable sensors have potential for quantitative, gait-based, point-of-care fall risk assessment that can be easily and quickly implemented in clinical-care and older-adult living environments. This investigation generated models for wearable-sensor based fall-risk classification in older adults and identified the optimal sensor type, location, combination, and modelling method; for walking with and without a cognitive load task. A convenience sample of 100 older individuals (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Participants also completed the Activities-specific Balance Confidence scale, Community Health Activities Model Program for Seniors questionnaire, six minute walk test, and ranked their fear of falling. Fall risk classification models were assessed for all sensor combinations and three model types: multi-layer perceptron neural network, naïve Bayesian, and support vector machine. The best performing model was a multi-layer perceptron neural network with input parameters from pressure-sensing insoles and head, pelvis, and left shank accelerometers (accuracy = 84%, F1 score = 0.600, MCC score = 0.521. Head sensor-based models had the best performance of the single-sensor models for single-task gait assessment. Single-task gait assessment models outperformed models based on dual-task walking or clinical assessment data. Support vector machines and neural networks were the best modelling technique for fall risk classification. Fall risk classification models developed for point-of-care environments should be developed using support vector machines and neural networks, with a multi-sensor single-task gait assessment.

  14. Development of a Respiratory Inductive Plethysmography Module Supporting Multiple Sensors for Wearable Systems

    Directory of Open Access Journals (Sweden)

    Zhengbo Zhang

    2012-09-01

    Full Text Available In this paper, we present an RIP module with the features of supporting multiple inductive sensors, no variable frequency LC oscillator, low power consumption, and automatic gain adjustment for each channel. Based on the method of inductance measurement without using a variable frequency LC oscillator, we further integrate pulse amplitude modulation and time division multiplexing scheme into a module to support multiple RIP sensors. All inductive sensors are excited by a high-frequency electric current periodically and momentarily, and the inductance of each sensor is measured during the time when the electric current is fed to it. To improve the amplitude response of the RIP sensors, we optimize the sensing unit with a matching capacitor parallel with each RIP sensor forming a frequency selection filter. Performance tests on the linearity of the output with cross-sectional area and the accuracy of respiratory volume estimation demonstrate good linearity and accurate lung volume estimation. Power consumption of this new RIP module with two sensors is very low. The performance of respiration measurement during movement is also evaluated. This RIP module is especially desirable for wearable systems with multiple RIP sensors for long-term respiration monitoring.

  15. Characterizing Dynamic Walking Patterns and Detecting Falls with Wearable Sensors Using Gaussian Process Methods

    Directory of Open Access Journals (Sweden)

    Taehwan Kim

    2017-05-01

    Full Text Available By incorporating a growing number of sensors and adopting machine learning technologies, wearable devices have recently become a prominent health care application domain. Among the related research topics in this field, one of the most important issues is detecting falls while walking. Since such falls may lead to serious injuries, automatically and promptly detecting them during daily use of smartphones and/or smart watches is a particular need. In this paper, we investigate the use of Gaussian process (GP methods for characterizing dynamic walking patterns and detecting falls while walking with built-in wearable sensors in smartphones and/or smartwatches. For the task of characterizing dynamic walking patterns in a low-dimensional latent feature space, we propose a novel approach called auto-encoded Gaussian process dynamical model, in which we combine a GP-based state space modeling method with a nonlinear dimensionality reduction method in a unique manner. The Gaussian process methods are fit for this task because one of the most import strengths of the Gaussian process methods is its capability of handling uncertainty in the model parameters. Also for detecting falls while walking, we propose to recycle the latent samples generated in training the auto-encoded Gaussian process dynamical model for GP-based novelty detection, which can lead to an efficient and seamless solution to the detection task. Experimental results show that the combined use of these GP-based methods can yield promising results for characterizing dynamic walking patterns and detecting falls while walking with the wearable sensors.

  16. Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems.

    Science.gov (United States)

    Wan, Liangtian; Han, Guangjie; Wang, Hao; Shu, Lei; Feng, Nanxing; Peng, Bao

    2016-03-12

    In health monitoring systems, the base station (BS) and the wearable sensors communicate with each other to construct a virtual multiple input and multiple output (VMIMO) system. In real applications, the signal that the BS received is a distributed source because of the scattering, reflection, diffraction and refraction in the propagation path. In this paper, a 2D direction-of-arrival (DOA) estimation algorithm for incoherently-distributed (ID) and coherently-distributed (CD) sources is proposed based on multiple VMIMO systems. ID and CD sources are separated through the second-order blind identification (SOBI) algorithm. The traditional estimating signal parameters via the rotational invariance technique (ESPRIT)-based algorithm is valid only for one-dimensional (1D) DOA estimation for the ID source. By constructing the signal subspace, two rotational invariant relationships are constructed. Then, we extend the ESPRIT to estimate 2D DOAs for ID sources. For DOA estimation of CD sources, two rational invariance relationships are constructed based on the application of generalized steering vectors (GSVs). Then, the ESPRIT-based algorithm is used for estimating the eigenvalues of two rational invariance matrices, which contain the angular parameters. The expressions of azimuth and elevation for ID and CD sources have closed forms, which means that the spectrum peak searching is avoided. Therefore, compared to the traditional 2D DOA estimation algorithms, the proposed algorithm imposes significantly low computational complexity. The intersecting point of two rays, which come from two different directions measured by two uniform rectangle arrays (URA), can be regarded as the location of the biosensor (wearable sensor). Three BSs adopting the smart antenna (SA) technique cooperate with each other to locate the wearable sensors using the angulation positioning method. Simulation results demonstrate the effectiveness of the proposed algorithm.

  17. A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks.

    Science.gov (United States)

    Ponce, Hiram; Martínez-Villaseñor, María de Lourdes; Miralles-Pechuán, Luis

    2016-07-05

    Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods.

  18. A wearable and highly sensitive pressure sensor with ultrathin gold nanowires

    Science.gov (United States)

    Gong, Shu; Schwalb, Willem; Wang, Yongwei; Chen, Yi; Tang, Yue; Si, Jye; Shirinzadeh, Bijan; Cheng, Wenlong

    2014-02-01

    Ultrathin gold nanowires are mechanically flexible yet robust, which are novel building blocks with potential applications in future wearable optoelectronic devices. Here we report an efficient, low-cost fabrication strategy to construct a highly sensitive, flexible pressure sensor by sandwiching ultrathin gold nanowire-impregnated tissue paper between two thin polydimethylsiloxane sheets. The entire device fabrication process is scalable, enabling facile large-area integration and patterning for mapping spatial pressure distribution. Our gold nanowires-based pressure sensors can be operated at a battery voltage of 1.5 V with low energy consumption (1.14 kPa-1) and high stability (>50,000 loading-unloading cycles). In addition, our sensor can resolve pressing, bending, torsional forces and acoustic vibrations. The superior sensing properties in conjunction with mechanical flexibility and robustness enabled real-time monitoring of blood pulses as well as detection of small vibration forces from music.

  19. Recent progress of flexible and wearable strain sensors for human-motion monitoring

    Science.gov (United States)

    Ge, Gang; Huang, Wei; Shao, Jinjun; Dong, Xiaochen

    2018-01-01

    With the rapid development of human artificial intelligence and the inevitably expanding markets, the past two decades have witnessed an urgent demand for the flexible and wearable devices, especially the flexible strain sensors. Flexible strain sensors, incorporated the merits of stretchability, high sensitivity and skin-mountable, are emerging as an extremely charming domain in virtue of their promising applications in artificial intelligent realms, human-machine systems and health-care devices. In this review, we concentrate on the transduction mechanisms, building blocks of flexible physical sensors, subsequently property optimization in terms of device structures and sensing materials in the direction of practical applications. Perspectives on the existing challenges are also highlighted in the end. Project supported by the NNSF of China (Nos. 61525402, 61604071), the Key University Science Research Project of Jiangsu Province (No. 15KJA430006), and the Natural Science Foundation of Jiangsu Province (No. BK20161012).

  20. A sneak peek into digital innovations and wearable sensors for cardiac monitoring.

    Science.gov (United States)

    Michard, Frederic

    2017-04-01

    Many mobile phone or tablet applications have been designed to control cardiovascular risk factors (obesity, smoking, sedentary lifestyle, diabetes and hypertension) or to optimize treatment adherence. Some have been shown to be useful but the long-term benefits remain to be demonstrated. Digital stethoscopes make easier the interpretation of abnormal heart sounds, and the development of pocket-sized echo machines may quickly and significantly expand the use of ultrasounds. Daily home monitoring of pulmonary artery pressures with wireless implantable sensors has been shown to be associated with a significant decrease in hospital readmissions for heart failure. There are more and more non-invasive, wireless, and wearable sensors designed to monitor heart rate, heart rate variability, respiratory rate, arterial oxygen saturation, and thoracic fluid content. They have the potential to change the way we monitor and treat patients with cardiovascular diseases in the hospital and beyond. Some may have the ability to improve quality of care, decrease the number of medical visits and hospitalization, and ultimately health care costs. Validation and outcome studies are needed to clarify, among the growing number of digital innovations and wearable sensors, which tools have real clinical value.

  1. Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

    Directory of Open Access Journals (Sweden)

    Hadi Banaee

    2013-12-01

    Full Text Available The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.

  2. Motor Ingredients Derived from a Wearable Sensor-Based Virtual Reality System for Frozen Shoulder Rehabilitation

    Directory of Open Access Journals (Sweden)

    Si-Huei Lee

    2016-01-01

    Full Text Available Objective. This study aims to extract motor ingredients through data mining from wearable sensors in a virtual reality goal-directed shoulder rehabilitation (GDSR system and to examine their effects toward clinical assessment. Design. A single-group before/after comparison. Setting. Outpatient research hospital. Subjects. 16 patients with frozen shoulder. Interventions. The rehabilitation treatment involved GDSR exercises, hot pack, and interferential therapy. All patients first received hot pack and interferential therapy on the shoulder joints before engaging in the exercises. The GDSR exercise sessions were 40 minutes twice a week for 4 weeks. Main Measures. Clinical assessments included Constant and Murley score, range of motion of the shoulder, and muscle strength of upper arm as main measures. Motor indices from sensor data and task performance were measured as secondary measures. Results. The pre- and posttest results for task performance, motor indices, and the clinical assessments indicated significant improvement for the majority of the assessed items. Correlation analysis between the task performance and clinical assessments revealed significant correlations among a number of items. Stepwise regression analysis showed that task performance effectively predicted the results of several clinical assessment items. Conclusions. The motor ingredients derived from the wearable sensor and task performance are applicable and adequate to examine and predict clinical improvement after GDSR training.

  3. Wearable motion sensors to continuously measure real-world physical activities.

    Science.gov (United States)

    Dobkin, Bruce H

    2013-12-01

    Rehabilitation for sensorimotor impairments aims to improve daily activities, walking, exercise, and motor skills. Monitoring of practice and measuring outcomes, however, is usually restricted to laboratory-based procedures and self-reports. Mobile health devices may reverse these confounders of daily care and research trials. Wearable, wireless motion sensor data, analyzed by activity pattern-recognition algorithms, can describe the type, quantity, and quality of mobility-related activities in the community. Data transmission from the sensors to a cell phone and the Internet enable continuous monitoring. Remote access to laboratory quality data about walking speed, duration and distance, gait asymmetry and smoothness of movements, as well as cycling, exercise, and skills practice, opens new opportunities to engage patients in progressive, personalized therapies with feedback about the performance. Clinical trial designs will be able to include remote verification of the integrity of complex physical interventions and compliance with practice, as well as capture repeated, ecologically sound, ratio scale outcome measures. Given the progressively falling cost of miniaturized wearable gyroscopes, accelerometers, and other physiologic sensors, as well as inexpensive data transmission, sensing systems may become as ubiquitous as cell phones for healthcare. Neurorehabilitation can develop these mobile health platforms for daily care and clinical trials to improve exercise and fitness, skills learning, and physical functioning.

  4. Sweat test for cystic fibrosis: Wearable sweat sensor vs. standard laboratory test.

    Science.gov (United States)

    Choi, Dong-Hoon; Thaxton, Abigail; Jeong, In Cheol; Kim, Kain; Sosnay, Patrick R; Cutting, Garry R; Searson, Peter C

    2018-03-23

    Sweat chloride testing for diagnosis of cystic fibrosis (CF) involves sweat induction, collection and handling, and measurement in an analytical lab. We have developed a wearable sensor with an integrated salt bridge for real-time measurement of sweat chloride concentration. Here, in a proof-of-concept study, we compare the performance of the sensor to current clinical practice in CF patients and healthy subjects. Sweat was induced on both forearms of 10 individuals with CF and 10 healthy subjects using pilocarpine iontophoresis. A Macroduct sweat collection device was attached to one arm and sweat was collected for 30 min and then sent for laboratory analysis. A sensor was attached to the other arm and the chloride ion concentration monitored in real time for 30 min using a Bluetooth transceiver and smart phone app. Stable sweat chloride measurements were obtained within 15 min following sweat induction using the wearable sensor. We define the detection time as the time at which the standard deviation of the real-time chloride ion concentration remained below 2 mEq/L for 5 min. The sweat volume for sensor measurements at the detection time was 13.1 ± 11.4 μL (SD), in many cases lower than the minimum sweat volume of 15 μL for conventional testing. The mean difference between sweat chloride concentrations measured by the sensor and the conventional laboratory practice was 6.2 ± 9.5 mEq/L (SD), close to the arm-to-arm variation of about 3 mEq/L. The Pearson correlation coefficient between the two measurements was 0.97 highlighting the excellent agreement between the two methods. A wearable sensor can be used to make real-time measurements of sweat chloride within 15 min following sweat induction, requiring a small sweat volume, and with excellent agreement to standard methods. Copyright © 2018 European Cystic Fibrosis Society. Published by Elsevier B.V. All rights reserved.

  5. A Smart Wearable Sensor System for Counter-Fighting Overweight in Teenagers

    Directory of Open Access Journals (Sweden)

    Carlo Emilio Standoli

    2016-08-01

    Full Text Available PEGASO is a FP7-funded project whose goal is to develop an ICT and mobile-based platform together with an appropriate strategy to tackle the diffusion of obesity and other lifestyle-related illnesses among teenagers. Indeed, the design of an engaging strategy, leveraging a complementary set of technologies, is the approach proposed by the project to promote the adoption of healthy habits such as active lifestyle and balanced nutrition and to effectively counter-fight the emergence of overweight and obesity in the younger population. A technological key element of such a strategy sees the adoption of wearable sensors to monitor teenagers’ activities, which is at the basis of developing awareness about the current lifestyle. This paper describes the experience carried out in the framework of the PEGASO project in developing and evaluating wearable monitoring systems addressed to adolescents. The paper describes the methodological approach based on the co-designing of such a wearable system and the main results that, in the first phase, involved a total of 407 adolescents across Europe in a series of focus groups conducted in three countries for the requirements definition phase. Moreover, it describes an evaluation process of signal reliability during the usage of the wearable system. The main results described here are: (a a prototype of the standardized experimental protocol that has been developed and applied to test signal reliability in smart garments; (b the requirements definition methodology through a co-design activity and approach to address user requirements and preferences and not only technological specifications. Such co-design approach is able to support a higher system acceptance and usability together with a sustained adoption of the solution with respect to the traditional technology push system development strategy.

  6. Recommendations for Assessment of the Reliability, Sensitivity, and Validity of Data Provided by Wearable Sensors Designed for Monitoring Physical Activity.

    Science.gov (United States)

    Düking, Peter; Fuss, Franz Konstantin; Holmberg, Hans-Christer; Sperlich, Billy

    2018-04-30

    Although it is becoming increasingly popular to monitor parameters related to training, recovery, and health with wearable sensor technology (wearables), scientific evaluation of the reliability, sensitivity, and validity of such data is limited and, where available, has involved a wide variety of approaches. To improve the trustworthiness of data collected by wearables and facilitate comparisons, we have outlined recommendations for standardized evaluation. We discuss the wearable devices themselves, as well as experimental and statistical considerations. Adherence to these recommendations should be beneficial not only for the individual, but also for regulatory organizations and insurance companies. ©Peter Düking, Franz Konstantin Fuss, Hans-Christer Holmberg, Billy Sperlich. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 30.04.2018.

  7. Quantification of Finger-Tapping Angle Based on Wearable Sensors.

    Science.gov (United States)

    Djurić-Jovičić, Milica; Jovičić, Nenad S; Roby-Brami, Agnes; Popović, Mirjana B; Kostić, Vladimir S; Djordjević, Antonije R

    2017-01-25

    We propose a novel simple method for quantitative and qualitative finger-tapping assessment based on miniature inertial sensors (3D gyroscopes) placed on the thumb and index-finger. We propose a simplified description of the finger tapping by using a single angle, describing rotation around a dominant axis. The method was verified on twelve subjects, who performed various tapping tasks, mimicking impaired patterns. The obtained tapping angles were compared with results of a motion capture camera system, demonstrating excellent accuracy. The root-mean-square (RMS) error between the two sets of data is, on average, below 4°, and the intraclass correlation coefficient is, on average, greater than 0.972. Data obtained by the proposed method may be used together with scores from clinical tests to enable a better diagnostic. Along with hardware simplicity, this makes the proposed method a promising candidate for use in clinical practice. Furthermore, our definition of the tapping angle can be applied to all tapping assessment systems.

  8. Ultrasensitive, passive and wearable sensors for monitoring human muscle motion and physiological signals.

    Science.gov (United States)

    Cai, Feng; Yi, Changrui; Liu, Shichang; Wang, Yan; Liu, Lacheng; Liu, Xiaoqing; Xu, Xuming; Wang, Li

    2016-03-15

    Flexible sensors have attracted more and more attention as a fundamental part of anthropomorphic robot research, medical diagnosis and physical health monitoring. Here, we constructed an ultrasensitive and passive flexible sensor with the advantages of low cost, lightness and wearability, electric safety and reliability. The fundamental mechanism of the sensor is based on triboelectric effect inducing electrostatic charges on the surfaces between two different materials. Just like a plate capacitor, current will be generated while the distance or size of the parallel capacitors changes caused by the small mechanical disturbance upon it and therefore the output current/voltage will be produced. Typically, the passive sensor unambiguously monitors muscle motions including hand motion from stretch-clench-stretch, mouth motion from open-bite-open, blink and respiration. Moreover, this sensor records the details of the consecutive phases in a cardiac cycle of the apex cardiogram, and identify the peaks including percussion wave, tidal wave and diastolic wave of the radial pulse wave. To record subtle human physiological signals including radial pulsilogram and apex cardiogram with excellent signal/noise ratio, stability and reproducibility, the sensor shows great potential in the applications of medical diagnosis and daily health monitoring. Copyright © 2015 Elsevier B.V. All rights reserved.

  9. A Real-Time Health Monitoring System for Remote Cardiac Patients Using Smartphone and Wearable Sensors

    Directory of Open Access Journals (Sweden)

    Priyanka Kakria

    2015-01-01

    Full Text Available Online telemedicine systems are useful due to the possibility of timely and efficient healthcare services. These systems are based on advanced wireless and wearable sensor technologies. The rapid growth in technology has remarkably enhanced the scope of remote health monitoring systems. In this paper, a real-time heart monitoring system is developed considering the cost, ease of application, accuracy, and data security. The system is conceptualized to provide an interface between the doctor and the patients for two-way communication. The main purpose of this study is to facilitate the remote cardiac patients in getting latest healthcare services which might not be possible otherwise due to low doctor-to-patient ratio. The developed monitoring system is then evaluated for 40 individuals (aged between 18 and 66 years using wearable sensors while holding an Android device (i.e., smartphone under supervision of the experts. The performance analysis shows that the proposed system is reliable and helpful due to high speed. The analyses showed that the proposed system is convenient and reliable and ensures data security at low cost. In addition, the developed system is equipped to generate warning messages to the doctor and patient under critical circumstances.

  10. A Real-Time Health Monitoring System for Remote Cardiac Patients Using Smartphone and Wearable Sensors.

    Science.gov (United States)

    Kakria, Priyanka; Tripathi, N K; Kitipawang, Peerapong

    2015-01-01

    Online telemedicine systems are useful due to the possibility of timely and efficient healthcare services. These systems are based on advanced wireless and wearable sensor technologies. The rapid growth in technology has remarkably enhanced the scope of remote health monitoring systems. In this paper, a real-time heart monitoring system is developed considering the cost, ease of application, accuracy, and data security. The system is conceptualized to provide an interface between the doctor and the patients for two-way communication. The main purpose of this study is to facilitate the remote cardiac patients in getting latest healthcare services which might not be possible otherwise due to low doctor-to-patient ratio. The developed monitoring system is then evaluated for 40 individuals (aged between 18 and 66 years) using wearable sensors while holding an Android device (i.e., smartphone under supervision of the experts). The performance analysis shows that the proposed system is reliable and helpful due to high speed. The analyses showed that the proposed system is convenient and reliable and ensures data security at low cost. In addition, the developed system is equipped to generate warning messages to the doctor and patient under critical circumstances.

  11. Telehealth in older adults with cancer in the United States: The emerging use of wearable sensors.

    Science.gov (United States)

    Shen, John; Naeim, Arash

    2017-11-01

    As the aging and cancer populations in the world continue to increase, the need for complements to traditional geriatric assessments and the logical incorporation of fast and reliable telehealth tools have become interlinked. In the United States, studies examining the use of telehealth for chronic disease management have shown promising results in small groups. The implementation of health technology on a broader scale requires older adults to both accept and adapt such innovation into routine medical care. Though the commercial and recreational use of new technology has increased in older individuals, the transition into creating a smart and connected home that can interface with both patients and healthcare professionals is in its early phases. Current limitations include an inherent digital divide, as well as concerns regarding privacy, data volume, rapid change, cost and reimbursement. The emergence of low-cost, high-fidelity wearable sensors with a spectrum of clinical utility may be the key to increased use and adaptation by older adults. An opportunity to utilize wearable sensors for objective and real-time assessment of older patients with cancer for baseline functional status and treatment toxicity may be on the horizon. Copyright © 2017 Elsevier Inc. All rights reserved.

  12. Reduction of energy intake using just-in-time feedback from a wearable sensor system.

    Science.gov (United States)

    Farooq, Muhammad; McCrory, Megan A; Sazonov, Edward

    2017-04-01

    This work explored the potential use of a wearable sensor system for providing just-in-time (JIT) feedback on the progression of a meal and tested its ability to reduce the total food mass intake. Eighteen participants consumed three meals each in a lab while monitored by a wearable sensor system capable of accurately tracking chew counts. The baseline visit was used to establish the self-determined ingested mass and the associated chew counts. Real-time feedback on chew counts was provided in the next two visits, during which the target chew count was either the same as that at baseline or the baseline chew count reduced by 25% (in randomized order). The target was concealed from the participant and from the experimenter. Nonparametric repeated-measures ANOVAs were performed to compare mass of intake, meal duration, and ratings of hunger, appetite, and thirst across three meals. JIT feedback targeting a 25% reduction in chew counts resulted in a reduction in mass and energy intake without affecting perceived hunger or fullness. JIT feedback on chewing behavior may reduce intake within a meal. This system can be further used to help develop individualized strategies to provide JIT adaptive interventions for reducing energy intake. © 2017 The Obesity Society.

  13. Non-Enzymatic Wearable Sensor for Electrochemical Analysis of Perspiration Glucose.

    Science.gov (United States)

    Zhu, Xiaofei; Ju, Yinhui; Chen, Jian; Liu, Deye; Liu, Hong

    2018-05-16

    We report a non-enzymatic wearable sensor for electrochemical analysis of perspiration glucose. Multi-potential steps are applied on a Au electrode, including a high negative pretreatment potential step for proton reduction which produc-es a localized alkaline condition, a moderate potential step for electrocatalytic oxidation of glucose under the alkaline condi-tion, and a positive potential step to clean and reactivate the electrode surface for the next detection. Fluorocarbon-based materials were coated on the Au electrode for improving the selectivity and robustness of the sensor. A fully integrated wrist-band is developed for continuous real-time monitoring of perspiration glucose during physical activities, and uploading the test result to a Smartphone App via Bluetooth.

  14. On-Line Detection and Segmentation of Sports Motions Using a Wearable Sensor

    Directory of Open Access Journals (Sweden)

    Woosuk Kim

    2018-03-01

    Full Text Available In sports motion analysis, observation is a prerequisite for understanding the quality of motions. This paper introduces a novel approach to detect and segment sports motions using a wearable sensor for supporting systematic observation. The main goal is, for convenient analysis, to automatically provide motion data, which are temporally classified according to the phase definition. For explicit segmentation, a motion model is defined as a sequence of sub-motions with boundary states. A sequence classifier based on deep neural networks is designed to detect sports motions from continuous sensor inputs. The evaluation on two types of motions (soccer kicking and two-handed ball throwing verifies that the proposed method is successful for the accurate detection and segmentation of sports motions. By developing a sports motion analysis system using the motion model and the sequence classifier, we show that the proposed method is useful for observation of sports motions by automatically providing relevant motion data for analysis.

  15. On-Line Detection and Segmentation of Sports Motions Using a Wearable Sensor.

    Science.gov (United States)

    Kim, Woosuk; Kim, Myunggyu

    2018-03-19

    In sports motion analysis, observation is a prerequisite for understanding the quality of motions. This paper introduces a novel approach to detect and segment sports motions using a wearable sensor for supporting systematic observation. The main goal is, for convenient analysis, to automatically provide motion data, which are temporally classified according to the phase definition. For explicit segmentation, a motion model is defined as a sequence of sub-motions with boundary states. A sequence classifier based on deep neural networks is designed to detect sports motions from continuous sensor inputs. The evaluation on two types of motions (soccer kicking and two-handed ball throwing) verifies that the proposed method is successful for the accurate detection and segmentation of sports motions. By developing a sports motion analysis system using the motion model and the sequence classifier, we show that the proposed method is useful for observation of sports motions by automatically providing relevant motion data for analysis.

  16. A wearable mobility device for the blind using retina-inspired dynamic vision sensors.

    Science.gov (United States)

    Ghaderi, Viviane S; Mulas, Marcello; Pereira, Vinicius Felisberto Santos; Everding, Lukas; Weikersdorfer, David; Conradt, Jorg

    2015-01-01

    Proposed is a prototype of a wearable mobility device which aims to assist the blind with navigation and object avoidance via auditory-vision-substitution. The described system uses two dynamic vision sensors and event-based information processing techniques to extract depth information. The 3D visual input is then processed using three different strategies, and converted to a 3D output sound using an individualized head-related transfer function. The performance of the device with different processing strategies is evaluated via initial tests with ten subjects. The outcome of these tests demonstrate promising performance of the system after only very short training times of a few minutes due to the minimal encoding of outputs from the vision sensors which are translated into simple sound patterns easily interpretable for the user. The envisioned system will allow for efficient real-time algorithms on a hands-free and lightweight device with exceptional battery life-time.

  17. Multimodal Teaching Analytics: Automated Extraction of Orchestration Graphs from Wearable Sensor Data.

    Science.gov (United States)

    Prieto, Luis P; Sharma, Kshitij; Kidzinski, Łukasz; Rodríguez-Triana, María Jesús; Dillenbourg, Pierre

    2018-04-01

    The pedagogical modelling of everyday classroom practice is an interesting kind of evidence, both for educational research and teachers' own professional development. This paper explores the usage of wearable sensors and machine learning techniques to automatically extract orchestration graphs (teaching activities and their social plane over time), on a dataset of 12 classroom sessions enacted by two different teachers in different classroom settings. The dataset included mobile eye-tracking as well as audiovisual and accelerometry data from sensors worn by the teacher. We evaluated both time-independent and time-aware models, achieving median F1 scores of about 0.7-0.8 on leave-one-session-out k-fold cross-validation. Although these results show the feasibility of this approach, they also highlight the need for larger datasets, recorded in a wider variety of classroom settings, to provide automated tagging of classroom practice that can be used in everyday practice across multiple teachers.

  18. Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care

    Directory of Open Access Journals (Sweden)

    Jose-Luis Bayo-Monton

    2018-06-01

    Full Text Available Health and sociological indicators alert that life expectancy is increasing, hence so are the years that patients have to live with chronic diseases and co-morbidities. With the advancement in ICT, new tools and paradigms are been explored to provide effective and efficient health care. Telemedicine and health sensors stand as indispensable tools for promoting patient engagement, self-management of diseases and assist doctors to remotely follow up patients. In this paper, we evaluate a rapid prototyping solution for information merging based on five health sensors and two low-cost ubiquitous computing components: Arduino and Raspberry Pi. Our study, which is entirely described with the purpose of reproducibility, aimed to evaluate the extent to which portable technologies are capable of integrating wearable sensors by comparing two deployment scenarios: Raspberry Pi 3 and Personal Computer. The integration is implemented using a choreography engine to transmit data from sensors to a display unit using web services and a simple communication protocol with two modes of data retrieval. Performance of the two set-ups is compared by means of the latency in the wearable data transmission and data loss. PC has a delay of 0.051 ± 0.0035 s (max = 0.2504 s, whereas the Raspberry Pi yields a delay of 0.0175 ± 0.149 s (max = 0.294 s for N = 300. Our analysis confirms that portable devices ( p < < 0 . 01 are suitable to support the transmission and analysis of biometric signals into scalable telemedicine systems.

  19. Improved Reception of In-Body Signals by Means of a Wearable Multi-Antenna System

    Directory of Open Access Journals (Sweden)

    Thijs Castel

    2013-01-01

    Full Text Available High data-rate wireless communication for in-body human implants is mainly performed in the 402–405 MHz Medical Implant Communication System band and the 2.45 GHz Industrial, Scientific and Medical band. The latter band offers larger bandwidth, enabling high-resolution live video transmission. Although in-body signal attenuation is larger, at least 29 dB more power may be transmitted in this band and the antenna efficiency for compact antennas at 2.45 GHz is also up to 10 times higher. Moreover, at the receive side, one can exploit the large surface provided by a garment by deploying multiple compact highly efficient wearable antennas, capturing the signals transmitted by the implant directly at the body surface, yielding stronger signals and reducing interference. In this paper, we implement a reliable 3.5 Mbps wearable textile multi-antenna system suitable for integration into a jacket worn by a patient, and evaluate its potential to improve the In-to-Out Body wireless link reliability by means of spatial receive diversity in a standardized measurement setup. We derive the optimal distribution and the minimum number of on-body antennas required to ensure signal levels that are large enough for real-time wireless endoscopy-capsule applications, at varying positions and orientations of the implant in the human body.

  20. A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

    Directory of Open Access Journals (Sweden)

    Majid Janidarmian

    2017-03-01

    Full Text Available Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results.

  1. A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition.

    Science.gov (United States)

    Janidarmian, Majid; Roshan Fekr, Atena; Radecka, Katarzyna; Zilic, Zeljko

    2017-03-07

    Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results.

  2. Wearable Playware

    DEFF Research Database (Denmark)

    Pagliarini, Luigi; Lund, Henrik Hautop

    2011-01-01

    In this paper we define and trace the contours of a new approach to robotic systems, composed of interactive robotic modules that can be worn on the body, as for an ordinary suit. We label the field as Modular Robotic Wearable (MRW). Further, we describe how the use of modular robotics in creating...... wearable, besides being possible, is a path to obtain a flexible wearable processing system, where freely inter-changeable input/output modules can be positioned on the body suit in accordance with the task at hand. In this concept paper we describe the initial prototypes and show, as an example......, an artistic application. We then show drawing of future works and projects. Finally, by focusing on the intersection of the combination of modular robotic systems, wearability, and body-mind we attempt to explore the theoretical characteristics of such an approach and exploit the possible playware application...

  3. Optimization of wearable microwave antenna with simplified electromagnetic model of the human body

    Science.gov (United States)

    Januszkiewicz, Łukasz; Barba, Paolo Di; Hausman, Sławomir

    2017-12-01

    In this paper the problem of optimization design of a microwave wearable antenna is investigated. Reference is made to a specific antenna design that is a wideband Vee antenna the geometry of which is characterized by 6 parameters. These parameters were automatically adjusted with an evolution strategy based algorithm EStra to obtain the impedance matching of the antenna located in the proximity of the human body. The antenna was designed to operate in the ISM (industrial, scientific, medical) band which covers the frequency range of 2.4 GHz up to 2.5 GHz. The optimization procedure used the finite-difference time-domain method based full-wave simulator with a simplified human body model. In the optimization procedure small movements of antenna towards or away of the human body that are likely to happen during real use were considered. The stability of the antenna parameters irrespective of the movements of the user's body is an important factor in wearable antenna design. The optimization procedure allowed obtaining good impedance matching for a given range of antenna distances with respect to the human body.

  4. Carbon nanotubes (CNTs) based strain sensors for a wearable monitoring and biofeedback system for pressure ulcer prevention and rehabilitation.

    Science.gov (United States)

    Boissy, Patrick; Genest, Jonathan; Patenaude, Johanne; Poirier, Marie-Sol; Chenel, Vanessa; Béland, Jean-Pierre; Legault, Georges-Auguste; Bernier, Louise; Tapin, Danielle; Beauvais, Jacques

    2011-01-01

    This paper presents an overview of the functioning principles of CNTs and their electrical and mechanical properties when used as strain sensors and describes a system embodiment for a wearable monitoring and biofeedback platform for use in pressure ulcer prevention and rehabilitation. Two type of CNTs films (multi-layered CNTs film vs purified film) were characterized electrically and mechanically for potential use as source material. The loosely woven CNTs film (multi-layered) showed substantial less sensitivity than the purified CNTs film but had an almost linear response to stress and better mechanical properties. CNTs have the potential to achieve a much higher sensitivity to strain than other piezoresistors based on regular of conductive particles such as commercially available resistive inks and could become an innovative source material for wearable strain sensors. We are currently continuing the characterization of CNTs based strain sensors and exploring their use in a design for 3-axis strain sensors.

  5. An Analog Circuit Approximation of the Discrete Wavelet Transform for Ultra Low Power Signal Processing in Wearable Sensor Nodes

    OpenAIRE

    Casson, Alexander J.

    2015-01-01

    Ultra low power signal processing is an essential part of all sensor nodes, and particularly so in emerging wearable sensors for biomedical applications. Analog signal processing has an important role in these low power, low voltage, low frequency applications, and there is a key drive to decrease the power consumption of existing analog domain signal processing and to map more signal processing approaches into the analog domain. This paper presents an analog domain signal processing circuit ...

  6. The Next Generation Precision Medical Record - A Framework for Integrating Genomes and Wearable Sensors with Medical Records

    OpenAIRE

    Batra, Prag; Singh, Enakshi; Bog, Anja; Wright, Mark; Ashley, Euan; Waggott, Daryl

    2016-01-01

    Current medical records are rigid with regards to emerging big biomedical data. Examples of poorly integrated big data that already exist in clinical practice include whole genome sequencing and wearable sensors for real time monitoring. Genome sequencing enables conventional diagnostic interrogation and forms the fundamental baseline for precision health throughout a patients lifetime. Mobile sensors enable tailored monitoring regimes for both reducing risk through precision health intervent...

  7. Mobile health: the power of wearables, sensors, and apps to transform clinical trials.

    Science.gov (United States)

    Munos, Bernard; Baker, Pamela C; Bot, Brian M; Crouthamel, Michelle; de Vries, Glen; Ferguson, Ian; Hixson, John D; Malek, Linda A; Mastrototaro, John J; Misra, Veena; Ozcan, Aydogan; Sacks, Leonard; Wang, Pei

    2016-07-01

    Mobile technology has become a ubiquitous part of everyday life, and the practical utility of mobile devices for improving human health is only now being realized. Wireless medical sensors, or mobile biosensors, are one such technology that is allowing the accumulation of real-time biometric data that may hold valuable clues for treating even some of the most devastating human diseases. From wearable gadgets to sophisticated implantable medical devices, the information retrieved from mobile technology has the potential to revolutionize how clinical research is conducted and how disease therapies are delivered in the coming years. Encompassing the fields of science and engineering, analytics, health care, business, and government, this report explores the promise that wearable biosensors, along with integrated mobile apps, hold for improving the quality of patient care and clinical outcomes. The discussion focuses on groundbreaking device innovation, data optimization and validation, commercial platform integration, clinical implementation and regulation, and the broad societal implications of using mobile health technologies. © 2016 New York Academy of Sciences.

  8. Chemically Designed Metallic/Insulating Hybrid Nanostructures with Silver Nanocrystals for Highly Sensitive Wearable Pressure Sensors.

    Science.gov (United States)

    Kim, Haneun; Lee, Seung-Wook; Joh, Hyungmok; Seong, Mingi; Lee, Woo Seok; Kang, Min Su; Pyo, Jun Beom; Oh, Soong Ju

    2018-01-10

    With the increase in interest in wearable tactile pressure sensors for e-skin, researches to make nanostructures to achieve high sensitivity have been actively conducted. However, limitations such as complex fabrication processes using expensive equipment still exist. Herein, simple lithography-free techniques to develop pyramid-like metal/insulator hybrid nanostructures utilizing nanocrystals (NCs) are demonstrated. Ligand-exchanged and unexchanged silver NC thin films are used as metallic and insulating components, respectively. The interfaces of each NC layer are chemically engineered to create discontinuous insulating layers, i.e., spacers for improved sensitivity, and eventually to realize fully solution-processed pressure sensors. Device performance analysis with structural, chemical, and electronic characterization and conductive atomic force microscopy study reveals that hybrid nanostructure based pressure sensor shows an enhanced sensitivity of higher than 500 kPa -1 , reliability, and low power consumption with a wide range of pressure sensing. Nano-/micro-hierarchical structures are also designed by combining hybrid nanostructures with conventional microstructures, exhibiting further enhanced sensing range and achieving a record sensitivity of 2.72 × 10 4 kPa -1 . Finally, all-solution-processed pressure sensor arrays with high pixel density, capable of detecting delicate signals with high spatial selectivity much better than the human tactile threshold, are introduced.

  9. Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients.

    Directory of Open Access Journals (Sweden)

    Nicole A Capela

    Full Text Available Human activity recognition (HAR, using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter. The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree. Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations.

  10. Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients.

    Science.gov (United States)

    Capela, Nicole A; Lemaire, Edward D; Baddour, Natalie

    2015-01-01

    Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks) were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter). The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree). Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations.

  11. Detection of Early Morning Daily Activities with Static Home and Wearable Wireless Sensors

    Directory of Open Access Journals (Sweden)

    David Vanderpool

    2007-10-01

    Full Text Available This paper describes a flexible, cost-effective, wireless in-home activity monitoring system for assisting patients with cognitive impairments due to traumatic brain injury (TBI. The system locates the subject with fixed home sensors and classifies early morning bathroom activities of daily living with a wearable wireless accelerometer. The system extracts time- and frequency-domain features from the accelerometer data and classifies these features with a hybrid classifier that combines Gaussian mixture models and a finite state machine. In particular, the paper establishes that despite similarities between early morning bathroom activities of daily living, it is possible to detect and classify these activities with high accuracy. It also discusses system training and provides data to show that with proper feature selection, accurate detection and classification are possible for any subject with no subject specific training.

  12. A Human-Centered Smart Home System with Wearable-Sensor Behavior Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Ji, Jianting; Liu, Ting; Shen, Chao; Wu, Hongyu; Liu, Wenyi; Su, Man; Chen, Siyun; Jia, Zhanpei

    2016-11-17

    Smart home has recently attracted much research interest owing to its potential in improving the quality of human life. How to obtain user's demand is the most important and challenging task for appliance optimal scheduling in smart home, since it is highly related to user's unpredictable behavior. In this paper, a human-centered smart home system is proposed to identify user behavior, predict their demand and schedule the household appliances. Firstly, the sensor data from user's wearable devices are monitored to profile user's full-day behavior. Then, the appliance-demand matrix is constructed to predict user's demand on home environment, which is extracted from the history of appliance load data and user behavior. Two simulations are designed to demonstrate user behavior identification, appliance-demand matrix construction and strategy of appliance optimal scheduling generation.

  13. Design of wearable hybrid generator for harvesting heat energy from human body depending on physiological activity

    Science.gov (United States)

    Kim, Myoung-Soo; Kim, Min-Ki; Kim, Kyongtae; Kim, Yong-Jun

    2017-09-01

    We developed a prototype of a wearable hybrid generator (WHG) that is used for harvesting the heat energy of the human body. This WHG is constructed by integrating a thermoelectric generator (TEG) in a circular mesh polyester knit fabric, circular-shaped pyroelectric generator (PEG), and quick sweat-pickup/dry-fabric. The fabric packaging enables the TEG part of the WHG to generate energy steadily while maintaining a temperature difference in extreme temperature environments. Moreover, when the body sweats, the evaporation heat of the sweat leads to thermal fluctuations in the WHG. This phenomenon further leads to an increase in the output power of the WHG. These characteristics of the WHG make it possible to produce electrical energy steadily without reduction in the conversion efficiency, as both TEG and PEG use the same energy source of the human skin and the ambient temperature. Under a temperature difference of ˜6.5 °C and temperature change rate of ˜0.62 °C s-1, the output power and output power density of the WHG, respectively, are ˜4.5 nW and ˜1.5 μW m-2. Our hybrid approach will provide a framework to enhance the output power of the wearable generators that harvest heat energy from human body in various environments.

  14. Estimating potential infection transmission routes in hospital wards using wearable proximity sensors.

    Directory of Open Access Journals (Sweden)

    Philippe Vanhems

    Full Text Available BACKGROUND: Contacts between patients, patients and health care workers (HCWs and among HCWs represent one of the important routes of transmission of hospital-acquired infections (HAI. A detailed description and quantification of contacts in hospitals provides key information for HAIs epidemiology and for the design and validation of control measures. METHODS AND FINDINGS: We used wearable sensors to detect close-range interactions ("contacts" between individuals in the geriatric unit of a university hospital. Contact events were measured with a spatial resolution of about 1.5 meters and a temporal resolution of 20 seconds. The study included 46 HCWs and 29 patients and lasted for 4 days and 4 nights. 14,037 contacts were recorded overall, 94.1% of which during daytime. The number and duration of contacts varied between mornings, afternoons and nights, and contact matrices describing the mixing patterns between HCW and patients were built for each time period. Contact patterns were qualitatively similar from one day to the next. 38% of the contacts occurred between pairs of HCWs and 6 HCWs accounted for 42% of all the contacts including at least one patient, suggesting a population of individuals who could potentially act as super-spreaders. CONCLUSIONS: Wearable sensors represent a novel tool for the measurement of contact patterns in hospitals. The collected data can provide information on important aspects that impact the spreading patterns of infectious diseases, such as the strong heterogeneity of contact numbers and durations across individuals, the variability in the number of contacts during a day, and the fraction of repeated contacts across days. This variability is however associated with a marked statistical stability of contact and mixing patterns across days. Our results highlight the need for such measurement efforts in order to correctly inform mathematical models of HAIs and use them to inform the design and evaluation of

  15. Wearable strain sensors based on thin graphite films for human activity monitoring

    Science.gov (United States)

    Saito, Takanari; Kihara, Yusuke; Shirakashi, Jun-ichi

    2017-12-01

    Wearable health-monitoring devices have attracted increasing attention in disease diagnosis and health assessment. In many cases, such devices have been prepared by complicated multistep procedures which result in the waste of materials and require expensive facilities. In this study, we focused on pyrolytic graphite sheet (PGS), which is a low-cost, simple, and flexible material, used as wearable devices for monitoring human activity. We investigated wearable devices based on PGSs for the observation of elbow and finger motions. The thin graphite films were fabricated by cutting small films from PGSs. The wearable devices were then made from the thin graphite films assembled on a commercially available rubber glove. The human motions could be observed using the wearable devices. Therefore, these results suggested that the wearable devices based on thin graphite films may broaden their application in cost-effective wearable electronics for the observation of human activity.

  16. A wearable sensor system for lower-limb rehabilitation evaluation using the GRF and CoP distributions

    Science.gov (United States)

    Tao, Weijun; Zhang, Jianyun; Li, Guangyi; Liu, Tao; Liu, Fengping; Yi, Jingang; Wang, Hesheng; Inoue, Yoshio

    2016-02-01

    Wearable sensors are attractive for gait analysis because these systems can measure and obtain real-time human gait and motion information outside of the laboratory for a longer duration. In this paper, we present a new wearable ground reaction force (GRF) sensing system for ambulatory gait measurement. In addition, the GRF sensor system is also used to quantify the patients' lower-limb gait rehabilitation. We conduct a validation experiment for the sensor system on seven volunteer subjects (weight 62.39 +/- 9.69 kg and height 169.13 +/- 5.64 cm). The experiments include the use of the GRF sensing system for the subjects in the following conditions: (1) normal walking; (2) walking with the rehabilitation training device; and (3) walking with a knee brace and the rehabilitation training device. The experiment results support the hypothesis that the wearable GRF sensor system is capable of quantifying patients' lower-limb rehabilitation. The proposed GRF sensing system can also be used for assessing the effectiveness of a gait rehabilitation system and for providing bio-feedback information to the subjects.

  17. Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework

    Directory of Open Access Journals (Sweden)

    Juan Carlos Davila

    2017-06-01

    Full Text Available The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.

  18. Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework.

    Science.gov (United States)

    Davila, Juan Carlos; Cretu, Ana-Maria; Zaremba, Marek

    2017-06-07

    The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR) and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.

  19. Development of a research prototype computer `Wearables` that one can wear on his or her body; Minitsukeru computer `Wearables` kenkyuyo shisakuki wo kaihatsu

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1999-02-01

    Development has been made on a prototype of a wearable computer `Wearables` that makes the present notebook type PC still smaller in size, can be worn on human body for utilization at any time and from anywhere, and aims at realizing a social infrastructure. Using the company`s portable PC, Libretto as the base, the keyboard and the liquid crystal display panel were removed. To replace these functions, a voice inputting microphone, and various types of head mounting type displays (glasses type) mounted on a head to see images are connected. Provided as the means for information communication between the prototype computer and outside environments are infrared ray interface and data communication function using wireless (electric wave) communications. The wireless desk area network (DAN) technology that can structure dynamically a network between multiple number of computers has realized smooth communications with external environments. The voice recognition technology that can work efficiently against noise has realized keyboard-free operation that gives no neural stress to users. The `wearable computer` aims at not only users utilizing it simply wearing it, but also providing a new perception ability that could not have been seen or heard directly to date, that is realizing the digital sensation. With the computer, a society will be structured in which people can live comfortably and safely, maintaining conversations between the users and the computers, and interactions between the surrounding environment and the social infrastructures, with protection of individual privacy and information security taken into consideration. The company is working with the Massachusetts Institute of Technology (MIT) for research and development of the `wearable computer` as to how it can be utilized and basic technologies that will be required in the future. (translated by NEDO)

  20. Wearable Sensors for eLearning of Manual Tasks: Using Forearm EMG in Hand Hygiene Training.

    Science.gov (United States)

    Kutafina, Ekaterina; Laukamp, David; Bettermann, Ralf; Schroeder, Ulrik; Jonas, Stephan M

    2016-08-03

    In this paper, we propose a novel approach to eLearning that makes use of smart wearable sensors. Traditional eLearning supports the remote and mobile learning of mostly theoretical knowledge. Here we discuss the possibilities of eLearning to support the training of manual skills. We employ forearm armbands with inertial measurement units and surface electromyography sensors to detect and analyse the user's hand motions and evaluate their performance. Hand hygiene is chosen as the example activity, as it is a highly standardized manual task that is often not properly executed. The World Health Organization guidelines on hand hygiene are taken as a model of the optimal hygiene procedure, due to their algorithmic structure. Gesture recognition procedures based on artificial neural networks and hidden Markov modeling were developed, achieving recognition rates of 98 . 30 % ( ± 1 . 26 % ) for individual gestures. Our approach is shown to be promising for further research and application in the mobile eLearning of manual skills.

  1. A Fuzzy Logic-Based Personalized Method to Classify Perceived Exertion in Workplaces Using a Wearable Heart Rate Sensor

    OpenAIRE

    Pancardo, Pablo; Hernández-Nolasco, J. A.; Acosta-Escalante, Francisco

    2018-01-01

    Knowing the perceived exertion of workers during their physical activities facilitates the decision-making of supervisors regarding the worker allocation in the appropriate job, actions to prevent accidents, and reassignment of tasks, among others. However, although wearable heart rate sensors represent an effective way to capture perceived exertion, ergonomic methods are generic and they do not consider the diffuse nature of the ranges that classify the efforts. Personalized monitoring is ne...

  2. Recognizing Academic Performance, Sleep Quality, Stress Level, and Mental Health using Personality Traits, Wearable Sensors and Mobile Phones.

    Science.gov (United States)

    Sano, Akane; Phillips, Andrew J; Yu, Amy Z; McHill, Andrew W; Taylor, Sara; Jaques, Natasha; Czeisler, Charles A; Klerman, Elizabeth B; Picard, Rosalind W

    2015-06-01

    What can wearable sensors and usage of smart phones tell us about academic performance, self-reported sleep quality, stress and mental health condition? To answer this question, we collected extensive subjective and objective data using mobile phones, surveys, and wearable sensors worn day and night from 66 participants, for 30 days each, totaling 1,980 days of data. We analyzed daily and monthly behavioral and physiological patterns and identified factors that affect academic performance (GPA), Pittsburg Sleep Quality Index (PSQI) score, perceived stress scale (PSS), and mental health composite score (MCS) from SF-12, using these month-long data. We also examined how accurately the collected data classified the participants into groups of high/low GPA, good/poor sleep quality, high/low self-reported stress, high/low MCS using feature selection and machine learning techniques. We found associations among PSQI, PSS, MCS, and GPA and personality types. Classification accuracies using the objective data from wearable sensors and mobile phones ranged from 67-92%.

  3. Wearable Inertial Sensors Allow for Quantitative Assessment of Shoulder and Elbow Kinematics in a Cadaveric Knee Arthroscopy Model.

    Science.gov (United States)

    Rose, Michael; Curtze, Carolin; O'Sullivan, Joseph; El-Gohary, Mahmoud; Crawford, Dennis; Friess, Darin; Brady, Jacqueline M

    2017-12-01

    To develop a model using wearable inertial sensors to assess the performance of orthopaedic residents while performing a diagnostic knee arthroscopy. Fourteen subjects performed a diagnostic arthroscopy on a cadaveric right knee. Participants were divided into novices (5 postgraduate year 3 residents), intermediates (5 postgraduate year 4 residents), and experts (4 faculty) based on experience. Arm movement data were collected by inertial measurement units (Opal sensors) by securing 2 sensors to each upper extremity (dorsal forearm and lateral arm) and 2 sensors to the trunk (sternum and lumbar spine). Kinematics of the elbow and shoulder joints were calculated from the inertial data by biomechanical modeling based on a sequence of links connected by joints. Range of motion required to complete the procedure was calculated for each group. Histograms were used to compare the distribution of joint positions for an expert, intermediate, and novice. For both the right and left upper extremities, skill level corresponded well with shoulder abduction-adduction and elbow prono-supination. Novices required on average 17.2° more motion in the right shoulder abduction-adduction plane than experts to complete the diagnostic arthroscopy (P = .03). For right elbow prono-supination (probe hand), novices required on average 23.7° more motion than experts to complete the procedure (P = .03). Histogram data showed novices had markedly more variability in shoulder abduction-adduction and elbow prono-supination compared with the other groups. Our data show wearable inertial sensors can measure joint kinematics during diagnostic knee arthroscopy. Range-of-motion data in the shoulder and elbow correlated inversely with arthroscopic experience. Motion pattern-based analysis shows promise as a metric of resident skill acquisition and development in arthroscopy. Wearable inertial sensors show promise as metrics of arthroscopic skill acquisition among residents. Copyright © 2017

  4. Wearable sensor use for assessing standing balance and walking stability in people with Parkinson's disease: a systematic review.

    Science.gov (United States)

    Hubble, Ryan P; Naughton, Geraldine A; Silburn, Peter A; Cole, Michael H

    2015-01-01

    Postural instability and gait disability threaten the independence and well-being of people with Parkinson's disease and increase the risk of falls and fall-related injuries. Prospective research has shown that commonly-used clinical assessments of balance and walking lack the sensitivity to accurately and consistently identify those people with Parkinson's disease who are at a higher risk of falling. Wearable sensors provide a portable and affordable alternative for researchers and clinicians who are seeking to objectively assess movements and falls risk in the clinical setting. However, no consensus currently exists on the optimal placements for sensors and the best outcome measures to use for assessing standing balance and walking stability in Parkinson's disease patients. Hence, this systematic review aimed to examine the available literature to establish the best sensor types, locations and outcomes to assess standing balance and walking stability in this population. Papers listed in three electronic databases were searched by title and abstract to identify articles measuring standing balance or walking stability with any kind of wearable sensor among adults diagnosed with PD. To be eligible for inclusion, papers were required to be full-text articles published in English between January 1994 and December 2014 that assessed measures of standing balance or walking stability with wearable sensors in people with PD. Articles were excluded if they; i) did not use any form of wearable sensor to measure variables associated with standing balance or walking stability; ii) did not include a control group or control condition; iii) were an abstract and/or included in the proceedings of a conference; or iv) were a review article or case study. The targeted search of the three electronic databases identified 340 articles that were potentially eligible for inclusion, but following title, abstract and full-text review only 26 articles were deemed to meet the inclusion

  5. Wearable Sensor Use for Assessing Standing Balance and Walking Stability in People with Parkinson’s Disease: A Systematic Review

    Science.gov (United States)

    Hubble, Ryan P.; Naughton, Geraldine A.; Silburn, Peter A.; Cole, Michael H.

    2015-01-01

    Background Postural instability and gait disability threaten the independence and well-being of people with Parkinson’s disease and increase the risk of falls and fall-related injuries. Prospective research has shown that commonly-used clinical assessments of balance and walking lack the sensitivity to accurately and consistently identify those people with Parkinson’s disease who are at a higher risk of falling. Wearable sensors provide a portable and affordable alternative for researchers and clinicians who are seeking to objectively assess movements and falls risk in the clinical setting. However, no consensus currently exists on the optimal placements for sensors and the best outcome measures to use for assessing standing balance and walking stability in Parkinson’s disease patients. Hence, this systematic review aimed to examine the available literature to establish the best sensor types, locations and outcomes to assess standing balance and walking stability in this population. Methods Papers listed in three electronic databases were searched by title and abstract to identify articles measuring standing balance or walking stability with any kind of wearable sensor among adults diagnosed with PD. To be eligible for inclusion, papers were required to be full-text articles published in English between January 1994 and December 2014 that assessed measures of standing balance or walking stability with wearable sensors in people with PD. Articles were excluded if they; i) did not use any form of wearable sensor to measure variables associated with standing balance or walking stability; ii) did not include a control group or control condition; iii) were an abstract and/or included in the proceedings of a conference; or iv) were a review article or case study. The targeted search of the three electronic databases identified 340 articles that were potentially eligible for inclusion, but following title, abstract and full-text review only 26 articles were deemed

  6. Wearable sensor use for assessing standing balance and walking stability in people with Parkinson's disease: a systematic review.

    Directory of Open Access Journals (Sweden)

    Ryan P Hubble

    Full Text Available Postural instability and gait disability threaten the independence and well-being of people with Parkinson's disease and increase the risk of falls and fall-related injuries. Prospective research has shown that commonly-used clinical assessments of balance and walking lack the sensitivity to accurately and consistently identify those people with Parkinson's disease who are at a higher risk of falling. Wearable sensors provide a portable and affordable alternative for researchers and clinicians who are seeking to objectively assess movements and falls risk in the clinical setting. However, no consensus currently exists on the optimal placements for sensors and the best outcome measures to use for assessing standing balance and walking stability in Parkinson's disease patients. Hence, this systematic review aimed to examine the available literature to establish the best sensor types, locations and outcomes to assess standing balance and walking stability in this population.Papers listed in three electronic databases were searched by title and abstract to identify articles measuring standing balance or walking stability with any kind of wearable sensor among adults diagnosed with PD. To be eligible for inclusion, papers were required to be full-text articles published in English between January 1994 and December 2014 that assessed measures of standing balance or walking stability with wearable sensors in people with PD. Articles were excluded if they; i did not use any form of wearable sensor to measure variables associated with standing balance or walking stability; ii did not include a control group or control condition; iii were an abstract and/or included in the proceedings of a conference; or iv were a review article or case study. The targeted search of the three electronic databases identified 340 articles that were potentially eligible for inclusion, but following title, abstract and full-text review only 26 articles were deemed to meet the

  7. Measurement of the dynamics in ski jumping using a wearable inertial sensor-based system.

    Science.gov (United States)

    Chardonnens, Julien; Favre, Julien; Cuendet, Florian; Gremion, Gérald; Aminian, Kamiar

    2014-01-01

    Dynamics is a central aspect of ski jumping, particularly during take-off and stable flight. Currently, measurement systems able to measure ski jumping dynamics (e.g. 3D cameras, force plates) are complex and only available in few research centres worldwide. This study proposes a method to determine dynamics using a wearable inertial sensor-based system which can be used routinely on any ski jumping hill. The system automatically calculates characteristic dynamic parameters during take-off (position and velocity of the centre of mass perpendicular to the table, force acting on the centre of mass perpendicular to the table and somersault angular velocity) and stable flight (total aerodynamic force). Furthermore, the acceleration of the ski perpendicular to the table was quantified to characterise the skis lift at take-off. The system was tested with two groups of 11 athletes with different jump distances. The force acting on the centre of mass, acceleration of the ski perpendicular to the table, somersault angular velocity and total aerodynamic force were different between groups and correlated with the jump distances. Furthermore, all dynamic parameters were within the range of prior studies based on stationary measurement systems, except for the centre of mass mean force which was slightly lower.

  8. Improved Hip-Based Individual Recognition Using Wearable Motion Recording Sensor

    Science.gov (United States)

    Gafurov, Davrondzhon; Bours, Patrick

    In todays society the demand for reliable verification of a user identity is increasing. Although biometric technologies based on fingerprint or iris can provide accurate and reliable recognition performance, they are inconvenient for periodic or frequent re-verification. In this paper we propose a hip-based user recognition method which can be suitable for implicit and periodic re-verification of the identity. In our approach we use a wearable accelerometer sensor attached to the hip of the person, and then the measured hip motion signal is analysed for identity verification purposes. The main analyses steps consists of detecting gait cycles in the signal and matching two sets of detected gait cycles. Evaluating the approach on a hip data set consisting of 400 gait sequences (samples) from 100 subjects, we obtained equal error rate (EER) of 7.5% and identification rate at rank 1 was 81.4%. These numbers are improvements by 37.5% and 11.2% respectively of the previous study using the same data set.

  9. Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors

    Directory of Open Access Journals (Sweden)

    Frédéric Li

    2018-02-01

    Full Text Available Getting a good feature representation of data is paramount for Human Activity Recognition (HAR using wearable sensors. An increasing number of feature learning approaches—in particular deep-learning based—have been proposed to extract an effective feature representation by analyzing large amounts of data. However, getting an objective interpretation of their performances faces two problems: the lack of a baseline evaluation setup, which makes a strict comparison between them impossible, and the insufficiency of implementation details, which can hinder their use. In this paper, we attempt to address both issues: we firstly propose an evaluation framework allowing a rigorous comparison of features extracted by different methods, and use it to carry out extensive experiments with state-of-the-art feature learning approaches. We then provide all the codes and implementation details to make both the reproduction of the results reported in this paper and the re-use of our framework easier for other researchers. Our studies carried out on the OPPORTUNITY and UniMiB-SHAR datasets highlight the effectiveness of hybrid deep-learning architectures involving convolutional and Long-Short-Term-Memory (LSTM to obtain features characterising both short- and long-term time dependencies in the data.

  10. Detection of compensatory balance responses using wearable electromyography sensors for fall-risk assessment.

    Science.gov (United States)

    Nouredanesh, Mina; Kukreja, Sunil L; Tung, James

    2016-08-01

    Loss of balance is prevalent in older adults and populations with gait and balance impairments. The present paper aims to develop a method to automatically distinguish compensatory balance responses (CBRs) from normal gait, based on activity patterns of muscles involved in maintaining balance. In this study, subjects were perturbed by lateral pushes while walking and surface electromyography (sEMG) signals were recorded from four muscles in their right leg. To extract sEMG time domain features, several filtering characteristics and segmentation approaches are examined. The performance of three classification methods, i.e., k-nearest neighbor, support vector machines, and random forests, were investigated for accurate detection of CBRs. Our results show that features extracted in the 50-200Hz band, segmented using peak sEMG amplitudes, and a random forest classifier detected CBRs with an accuracy of 92.35%. Moreover, our results support the important role of biceps femoris and rectus femoris muscles in stabilization and consequently discerning CBRs. This study contributes towards the development of wearable sensor systems to accurately and reliably monitor gait and balance control behavior in at-home settings (unsupervised conditions), over long periods of time, towards personalized fall risk assessment tools.

  11. Waterproof Electronic-Bandage with Tunable Sensitivity for Wearable Strain Sensors.

    Science.gov (United States)

    Jeon, Jun-Young; Ha, Tae-Jun

    2016-02-03

    We demonstrate high-performance wearable electronic-bandage (E-bandage) based on carbon nanotube (CNT)/silver nanoparticle (AgNP) composites covered with flexible media of fluoropolymer-coated polydimethylsiloxane (PDMS) films. The E-bandage can be used for motion-related sensors by directly attaching them to human skin, which achieves a fast and accurate electric response with high sensitivity according to the bending and stretching movements that induce changes in the conductivity. This advance in the E-bandage is realized as a result of the sensitivity that can be achieved by controlling the concentration of AgNPs in CNT pastes and by modifying the device architecture. The fluoropolymer encapsulation with hydrophobic surface characteristics allows for the E-bandage to operate in water and protects it from physical and chemical contact with the daily life conditions of the human skin. The reliability and scalability of the E-bandage as well as the compatibility with conventional microfabrication allow new possibilities to integrate flexible human-interactive nanoelectronics into mobile health-care monitoring systems combined with Internet of things (IoTs).

  12. Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors.

    Science.gov (United States)

    Li, Frédéric; Shirahama, Kimiaki; Nisar, Muhammad Adeel; Köping, Lukas; Grzegorzek, Marcin

    2018-02-24

    Getting a good feature representation of data is paramount for Human Activity Recognition (HAR) using wearable sensors. An increasing number of feature learning approaches-in particular deep-learning based-have been proposed to extract an effective feature representation by analyzing large amounts of data. However, getting an objective interpretation of their performances faces two problems: the lack of a baseline evaluation setup, which makes a strict comparison between them impossible, and the insufficiency of implementation details, which can hinder their use. In this paper, we attempt to address both issues: we firstly propose an evaluation framework allowing a rigorous comparison of features extracted by different methods, and use it to carry out extensive experiments with state-of-the-art feature learning approaches. We then provide all the codes and implementation details to make both the reproduction of the results reported in this paper and the re-use of our framework easier for other researchers. Our studies carried out on the OPPORTUNITY and UniMiB-SHAR datasets highlight the effectiveness of hybrid deep-learning architectures involving convolutional and Long-Short-Term-Memory (LSTM) to obtain features characterising both short- and long-term time dependencies in the data.

  13. Balance Improvement Effects of Biofeedback Systems with State-of-the-Art Wearable Sensors: A Systematic Review

    Science.gov (United States)

    Ma, Christina Zong-Hao; Wong, Duo Wai-Chi; Lam, Wing Kai; Wan, Anson Hong-Ping; Lee, Winson Chiu-Chun

    2016-01-01

    Falls and fall-induced injuries are major global public health problems. Balance and gait disorders have been the second leading cause of falls. Inertial motion sensors and force sensors have been widely used to monitor both static and dynamic balance performance. Based on the detected performance, instant visual, auditory, electrotactile and vibrotactile biofeedback could be provided to augment the somatosensory input and enhance balance control. This review aims to synthesize the research examining the effect of biofeedback systems, with wearable inertial motion sensors and force sensors, on balance performance. Randomized and non-randomized clinical trials were included in this review. All studies were evaluated based on the methodological quality. Sample characteristics, device design and study characteristics were summarized. Most previous studies suggested that biofeedback devices were effective in enhancing static and dynamic balance in healthy young and older adults, and patients with balance and gait disorders. Attention should be paid to the choice of appropriate types of sensors and biofeedback for different intended purposes. Maximizing the computing capacity of the micro-processer, while minimizing the size of the electronic components, appears to be the future direction of optimizing the devices. Wearable balance-improving devices have their potential of serving as balance aids in daily life, which can be used indoors and outdoors. PMID:27023558

  14. Balance Improvement Effects of Biofeedback Systems with State-of-the-Art Wearable Sensors: A Systematic Review.

    Science.gov (United States)

    Ma, Christina Zong-Hao; Wong, Duo Wai-Chi; Lam, Wing Kai; Wan, Anson Hong-Ping; Lee, Winson Chiu-Chun

    2016-03-25

    Falls and fall-induced injuries are major global public health problems. Balance and gait disorders have been the second leading cause of falls. Inertial motion sensors and force sensors have been widely used to monitor both static and dynamic balance performance. Based on the detected performance, instant visual, auditory, electrotactile and vibrotactile biofeedback could be provided to augment the somatosensory input and enhance balance control. This review aims to synthesize the research examining the effect of biofeedback systems, with wearable inertial motion sensors and force sensors, on balance performance. Randomized and non-randomized clinical trials were included in this review. All studies were evaluated based on the methodological quality. Sample characteristics, device design and study characteristics were summarized. Most previous studies suggested that biofeedback devices were effective in enhancing static and dynamic balance in healthy young and older adults, and patients with balance and gait disorders. Attention should be paid to the choice of appropriate types of sensors and biofeedback for different intended purposes. Maximizing the computing capacity of the micro-processer, while minimizing the size of the electronic components, appears to be the future direction of optimizing the devices. Wearable balance-improving devices have their potential of serving as balance aids in daily life, which can be used indoors and outdoors.

  15. Balance Improvement Effects of Biofeedback Systems with State-of-the-Art Wearable Sensors: A Systematic Review

    Directory of Open Access Journals (Sweden)

    Christina Zong-Hao Ma

    2016-03-01

    Full Text Available Falls and fall-induced injuries are major global public health problems. Balance and gait disorders have been the second leading cause of falls. Inertial motion sensors and force sensors have been widely used to monitor both static and dynamic balance performance. Based on the detected performance, instant visual, auditory, electrotactile and vibrotactile biofeedback could be provided to augment the somatosensory input and enhance balance control. This review aims to synthesize the research examining the effect of biofeedback systems, with wearable inertial motion sensors and force sensors, on balance performance. Randomized and non-randomized clinical trials were included in this review. All studies were evaluated based on the methodological quality. Sample characteristics, device design and study characteristics were summarized. Most previous studies suggested that biofeedback devices were effective in enhancing static and dynamic balance in healthy young and older adults, and patients with balance and gait disorders. Attention should be paid to the choice of appropriate types of sensors and biofeedback for different intended purposes. Maximizing the computing capacity of the micro-processer, while minimizing the size of the electronic components, appears to be the future direction of optimizing the devices. Wearable balance-improving devices have their potential of serving as balance aids in daily life, which can be used indoors and outdoors.

  16. A wearable biochemical sensor for monitoring alcohol consumption lifestyle through Ethyl glucuronide (EtG) detection in human sweat.

    Science.gov (United States)

    Selvam, Anjan Panneer; Muthukumar, Sriram; Kamakoti, Vikramshankar; Prasad, Shalini

    2016-03-21

    We demonstrate for the first time a wearable biochemical sensor for monitoring alcohol consumption through the detection and quantification of a metabolite of ethanol, ethyl glucuronide (EtG). We designed and fabricated two co-planar sensors with gold and zinc oxide as sensing electrodes. We also designed a LED based reporting for the presence of EtG in the human sweat samples. The sensor functions on affinity based immunoassay principles whereby monoclonal antibodies for EtG were immobilized on the electrodes using thiol based chemistry. Detection of EtG from human sweat was achieved through chemiresistive sensing mechanism. In this method, an AC voltage was applied across the two coplanar electrodes and the impedance across the sensor electrodes was measured and calibrated for physiologically relevant doses of EtG in human sweat. EtG detection over a dose concentration of 0.001-100 μg/L was demonstrated on both glass and polyimide substrates. Detection sensitivity was lower at 1 μg/L with gold electrodes as compared to ZnO, which had detection sensitivity of 0.001 μg/L. Based on the detection range the wearable sensor has the ability to detect alcohol consumption of up to 11 standard drinks in the US over a period of 4 to 9 hours.

  17. Wireless wearable range-of-motion sensor system for upper and lower extremity joints: a validation study.

    Science.gov (United States)

    Kumar, Yogaprakash; Yen, Shih-Cheng; Tay, Arthur; Lee, Wangwei; Gao, Fan; Zhao, Ziyi; Li, Jingze; Hon, Benjamin; Tian-Ma Xu, Tim; Cheong, Angela; Koh, Karen; Ng, Yee-Sien; Chew, Effie; Koh, Gerald

    2015-02-01

    Range-of-motion (ROM) assessment is a critical assessment tool during the rehabilitation process. The conventional approach uses the goniometer which remains the most reliable instrument but it is usually time-consuming and subject to both intra- and inter-therapist measurement errors. An automated wireless wearable sensor system for the measurement of ROM has previously been developed by the current authors. Presented is the correlation and accuracy of the automated wireless wearable sensor system against a goniometer in measuring ROM in the major joints of upper (UEs) and lower extremities (LEs) in 19 healthy subjects and 20 newly disabled inpatients through intra (same) subject comparison of ROM assessments between the sensor system against goniometer measurements by physical therapists. In healthy subjects, ROM measurements using the new sensor system were highly correlated with goniometry, with 95% of differences sensor system were also highly correlated with goniometry, with 95% of the differences being < 20° and 25° for most movements in the major joints of UE and LE, respectively.

  18. A wearable biochemical sensor for monitoring alcohol consumption lifestyle through Ethyl glucuronide (EtG) detection in human sweat

    Science.gov (United States)

    Panneer Selvam, Anjan; Muthukumar, Sriram; Kamakoti, Vikramshankar; Prasad, Shalini

    2016-03-01

    We demonstrate for the first time a wearable biochemical sensor for monitoring alcohol consumption through the detection and quantification of a metabolite of ethanol, ethyl glucuronide (EtG). We designed and fabricated two co-planar sensors with gold and zinc oxide as sensing electrodes. We also designed a LED based reporting for the presence of EtG in the human sweat samples. The sensor functions on affinity based immunoassay principles whereby monoclonal antibodies for EtG were immobilized on the electrodes using thiol based chemistry. Detection of EtG from human sweat was achieved through chemiresistive sensing mechanism. In this method, an AC voltage was applied across the two coplanar electrodes and the impedance across the sensor electrodes was measured and calibrated for physiologically relevant doses of EtG in human sweat. EtG detection over a dose concentration of 0.001-100 μg/L was demonstrated on both glass and polyimide substrates. Detection sensitivity was lower at 1 μg/L with gold electrodes as compared to ZnO, which had detection sensitivity of 0.001 μg/L. Based on the detection range the wearable sensor has the ability to detect alcohol consumption of up to 11 standard drinks in the US over a period of 4 to 9 hours.

  19. A New Proxy Measurement Algorithm with Application to the Estimation of Vertical Ground Reaction Forces Using Wearable Sensors.

    Science.gov (United States)

    Guo, Yuzhu; Storm, Fabio; Zhao, Yifan; Billings, Stephen A; Pavic, Aleksandar; Mazzà, Claudia; Guo, Ling-Zhong

    2017-09-22

    Measurement of the ground reaction forces (GRF) during walking is typically limited to laboratory settings, and only short observations using wearable pressure insoles have been reported so far. In this study, a new proxy measurement method is proposed to estimate the vertical component of the GRF (vGRF) from wearable accelerometer signals. The accelerations are used as the proxy variable. An orthogonal forward regression algorithm (OFR) is employed to identify the dynamic relationships between the proxy variables and the measured vGRF using pressure-sensing insoles. The obtained model, which represents the connection between the proxy variable and the vGRF, is then used to predict the latter. The results have been validated using pressure insoles data collected from nine healthy individuals under two outdoor walking tasks in non-laboratory settings. The results show that the vGRFs can be reconstructed with high accuracy (with an average prediction error of less than 5.0%) using only one wearable sensor mounted at the waist (L5, fifth lumbar vertebra). Proxy measures with different sensor positions are also discussed. Results show that the waist acceleration-based proxy measurement is more stable with less inter-task and inter-subject variability than the proxy measures based on forehead level accelerations. The proposed proxy measure provides a promising low-cost method for monitoring ground reaction forces in real-life settings and introduces a novel generic approach for replacing the direct determination of difficult to measure variables in many applications.

  20. Using hierarchical clustering methods to classify motor activities of COPD patients from wearable sensor data

    Directory of Open Access Journals (Sweden)

    Reilly John J

    2005-06-01

    Full Text Available Abstract Background Advances in miniature sensor technology have led to the development of wearable systems that allow one to monitor motor activities in the field. A variety of classifiers have been proposed in the past, but little has been done toward developing systematic approaches to assess the feasibility of discriminating the motor tasks of interest and to guide the choice of the classifier architecture. Methods A technique is introduced to address this problem according to a hierarchical framework and its use is demonstrated for the application of detecting motor activities in patients with chronic obstructive pulmonary disease (COPD undergoing pulmonary rehabilitation. Accelerometers were used to collect data for 10 different classes of activity. Features were extracted to capture essential properties of the data set and reduce the dimensionality of the problem at hand. Cluster measures were utilized to find natural groupings in the data set and then construct a hierarchy of the relationships between clusters to guide the process of merging clusters that are too similar to distinguish reliably. It provides a means to assess whether the benefits of merging for performance of a classifier outweigh the loss of resolution incurred through merging. Results Analysis of the COPD data set demonstrated that motor tasks related to ambulation can be reliably discriminated from tasks performed in a seated position with the legs in motion or stationary using two features derived from one accelerometer. Classifying motor tasks within the category of activities related to ambulation requires more advanced techniques. While in certain cases all the tasks could be accurately classified, in others merging clusters associated with different motor tasks was necessary. When merging clusters, it was found that the proposed method could lead to more than 12% improvement in classifier accuracy while retaining resolution of 4 tasks. Conclusion Hierarchical

  1. Wearable kinesthetic systems for capturing and classifying body posture and gesture.

    Science.gov (United States)

    Tognetti, Alessandro; Lorussi, Federico; Tesconi, Mario; Bartalesi, Raphael; Zupone, Giuseppe; De Rossi, Danilo

    2005-01-01

    Monitoring body kinematics has fundamental relevance in several biological and technical disciplines. In particular the possibility to know the posture exactly may furnish a main aid in rehabilitation topics. This paper deals with the design, the development and the realization of sensing garments, from the characterization of innovative comfortable and spreadable sensors to the methodologies employed to gather information on posture and movement. In the present work an upper limb kinesthetic garment (ULKG), which allows to reconstruct shoulder, elbow and wrist movements and a kinesthetic glove able to detect posture an gesture of the hand are presented. Sensors are directly integrated in Lycra fabrics by using conductive elastomer (CE) sensors. CE sensors show piezoresistive properties when a deformation is applied and they can be integrated onto fabric or other flexible substrate to be employed as strain sensors.

  2. An Approach to Biometric Verification Based on Human Body Communication in Wearable Devices

    Directory of Open Access Journals (Sweden)

    Jingzhen Li

    2017-01-01

    Full Text Available In this paper, an approach to biometric verification based on human body communication (HBC is presented for wearable devices. For this purpose, the transmission gain S21 of volunteer’s forearm is measured by vector network analyzer (VNA. Specifically, in order to determine the chosen frequency for biometric verification, 1800 groups of data are acquired from 10 volunteers in the frequency range 0.3 MHz to 1500 MHz, and each group includes 1601 sample data. In addition, to achieve the rapid verification, 30 groups of data for each volunteer are acquired at the chosen frequency, and each group contains only 21 sample data. Furthermore, a threshold-adaptive template matching (TATM algorithm based on weighted Euclidean distance is proposed for rapid verification in this work. The results indicate that the chosen frequency for biometric verification is from 650 MHz to 750 MHz. The false acceptance rate (FAR and false rejection rate (FRR based on TATM are approximately 5.79% and 6.74%, respectively. In contrast, the FAR and FRR were 4.17% and 37.5%, 3.37% and 33.33%, and 3.80% and 34.17% using K-nearest neighbor (KNN classification, support vector machines (SVM, and naive Bayesian method (NBM classification, respectively. In addition, the running time of TATM is 0.019 s, whereas the running times of KNN, SVM and NBM are 0.310 s, 0.0385 s, and 0.168 s, respectively. Therefore, TATM is suggested to be appropriate for rapid verification use in wearable devices.

  3. An Approach to Biometric Verification Based on Human Body Communication in Wearable Devices.

    Science.gov (United States)

    Li, Jingzhen; Liu, Yuhang; Nie, Zedong; Qin, Wenjian; Pang, Zengyao; Wang, Lei

    2017-01-10

    In this paper, an approach to biometric verification based on human body communication (HBC) is presented for wearable devices. For this purpose, the transmission gain S21 of volunteer's forearm is measured by vector network analyzer (VNA). Specifically, in order to determine the chosen frequency for biometric verification, 1800 groups of data are acquired from 10 volunteers in the frequency range 0.3 MHz to 1500 MHz, and each group includes 1601 sample data. In addition, to achieve the rapid verification, 30 groups of data for each volunteer are acquired at the chosen frequency, and each group contains only 21 sample data. Furthermore, a threshold-adaptive template matching (TATM) algorithm based on weighted Euclidean distance is proposed for rapid verification in this work. The results indicate that the chosen frequency for biometric verification is from 650 MHz to 750 MHz. The false acceptance rate (FAR) and false rejection rate (FRR) based on TATM are approximately 5.79% and 6.74%, respectively. In contrast, the FAR and FRR were 4.17% and 37.5%, 3.37% and 33.33%, and 3.80% and 34.17% using K-nearest neighbor (KNN) classification, support vector machines (SVM), and naive Bayesian method (NBM) classification, respectively. In addition, the running time of TATM is 0.019 s, whereas the running times of KNN, SVM and NBM are 0.310 s, 0.0385 s, and 0.168 s, respectively. Therefore, TATM is suggested to be appropriate for rapid verification use in wearable devices.

  4. An Approach to Biometric Verification Based on Human Body Communication in Wearable Devices

    Science.gov (United States)

    Li, Jingzhen; Liu, Yuhang; Nie, Zedong; Qin, Wenjian; Pang, Zengyao; Wang, Lei

    2017-01-01

    In this paper, an approach to biometric verification based on human body communication (HBC) is presented for wearable devices. For this purpose, the transmission gain S21 of volunteer’s forearm is measured by vector network analyzer (VNA). Specifically, in order to determine the chosen frequency for biometric verification, 1800 groups of data are acquired from 10 volunteers in the frequency range 0.3 MHz to 1500 MHz, and each group includes 1601 sample data. In addition, to achieve the rapid verification, 30 groups of data for each volunteer are acquired at the chosen frequency, and each group contains only 21 sample data. Furthermore, a threshold-adaptive template matching (TATM) algorithm based on weighted Euclidean distance is proposed for rapid verification in this work. The results indicate that the chosen frequency for biometric verification is from 650 MHz to 750 MHz. The false acceptance rate (FAR) and false rejection rate (FRR) based on TATM are approximately 5.79% and 6.74%, respectively. In contrast, the FAR and FRR were 4.17% and 37.5%, 3.37% and 33.33%, and 3.80% and 34.17% using K-nearest neighbor (KNN) classification, support vector machines (SVM), and naive Bayesian method (NBM) classification, respectively. In addition, the running time of TATM is 0.019 s, whereas the running times of KNN, SVM and NBM are 0.310 s, 0.0385 s, and 0.168 s, respectively. Therefore, TATM is suggested to be appropriate for rapid verification use in wearable devices. PMID:28075375

  5. Estimation of ground reaction forces and joint moments on the basis on plantar pressure insoles and wearable sensors for joint angle measurement.

    Science.gov (United States)

    Ostaszewski, Michal; Pauk, Jolanta

    2018-05-16

    Gait analysis is a useful tool medical staff use to support clinical decision making. There is still an urgent need to develop low-cost and unobtrusive mobile health monitoring systems. The goal of this study was twofold. Firstly, a wearable sensor system composed of plantar pressure insoles and wearable sensors for joint angle measurement was developed. Secondly, the accuracy of the system in the measurement of ground reaction forces and joint moments was examined. The measurements included joint angles and plantar pressure distribution. To validate the wearable sensor system and examine the effectiveness of the proposed method for gait analysis, an experimental study on ten volunteer subjects was conducted. The accuracy of measurement of ground reaction forces and joint moments was validated against the results obtained from a reference motion capture system. Ground reaction forces and joint moments measured by the wearable sensor system showed a root mean square error of 1% for min. GRF and 27.3% for knee extension moment. The correlation coefficient was over 0.9, in comparison with the stationary motion capture system. The study suggests that the wearable sensor system could be recommended both for research and clinical applications outside a typical gait laboratory.

  6. Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review.

    Science.gov (United States)

    O'Reilly, Martin; Caulfield, Brian; Ward, Tomas; Johnston, William; Doherty, Cailbhe

    2018-05-01

    Analysis of lower limb exercises is traditionally completed with four distinct methods: (1) 3D motion capture; (2) depth-camera-based systems; (3) visual analysis from a qualified exercise professional; and (4) self-assessment. Each method is associated with a number of limitations. The aim of this systematic review is to synthesise and evaluate studies which have investigated the capacity for inertial measurement unit (IMU) technologies to assess movement quality in lower limb exercises. A systematic review of studies identified through the databases of PubMed, ScienceDirect and Scopus was conducted. Articles written in English and published in the last 10 years which investigated an IMU system for the analysis of repetition-based targeted lower limb exercises were included. The quality of included studies was measured using an adapted version of the STROBE assessment criteria for cross-sectional studies. The studies were categorised into three groupings: exercise detection, movement classification or measurement validation. Each study was then qualitatively summarised. From the 2452 articles that were identified with the search strategies, 47 papers are included in this review. Twenty-six of the 47 included studies were deemed as being of high quality. Wearable inertial sensor systems for analysing lower limb exercises is a rapidly growing field of research. Research over the past 10 years has predominantly focused on validating measurements that the systems produce and classifying users' exercise quality. There have been very few user evaluation studies and no clinical trials in this field to date.

  7. Design and Implementation of Foot-Mounted Inertial Sensor Based Wearable Electronic Device for Game Play Application

    Directory of Open Access Journals (Sweden)

    Qifan Zhou

    2016-10-01

    Full Text Available Wearable electronic devices have experienced increasing development with the advances in the semiconductor industry and have received more attention during the last decades. This paper presents the development and implementation of a novel inertial sensor-based foot-mounted wearable electronic device for a brand new application: game playing. The main objective of the introduced system is to monitor and identify the human foot stepping direction in real time, and coordinate these motions to control the player operation in games. This proposed system extends the utilized field of currently available wearable devices and introduces a convenient and portable medium to perform exercise in a more compelling way in the near future. This paper provides an overview of the previously-developed system platforms, introduces the main idea behind this novel application, and describes the implemented human foot moving direction identification algorithm. Practical experiment results demonstrate that the proposed system is capable of recognizing five foot motions, jump, step left, step right, step forward, and step backward, and has achieved an over 97% accuracy performance for different users. The functionality of the system for real-time application has also been verified through the practical experiments.

  8. Ultrasensitive Wearable Soft Strain Sensors of Conductive, Self-healing, and Elastic Hydrogels with Synergistic "Soft and Hard" Hybrid Networks.

    Science.gov (United States)

    Liu, Yan-Jun; Cao, Wen-Tao; Ma, Ming-Guo; Wan, Pengbo

    2017-08-02

    Robust, stretchable, and strain-sensitive hydrogels have recently attracted immense research interest because of their potential application in wearable strain sensors. The integration of the synergistic characteristics of decent mechanical properties, reliable self-healing capability, and high sensing sensitivity for fabricating conductive, elastic, self-healing, and strain-sensitive hydrogels is still a great challenge. Inspired by the mechanically excellent and self-healing biological soft tissues with hierarchical network structures, herein, functional network hydrogels are fabricated by the interconnection between a "soft" homogeneous polymer network and a "hard" dynamic ferric (Fe 3+ ) cross-linked cellulose nanocrystals (CNCs-Fe 3+ ) network. Under stress, the dynamic CNCs-Fe 3+ coordination bonds act as sacrificial bonds to efficiently dissipate energy, while the homogeneous polymer network leads to a smooth stress-transfer, which enables the hydrogels to achieve unusual mechanical properties, such as excellent mechanical strength, robust toughness, and stretchability, as well as good self-recovery property. The hydrogels demonstrate autonomously self-healing capability in only 5 min without the need of any stimuli or healing agents, ascribing to the reorganization of CNCs and Fe 3+ via ionic coordination. Furthermore, the resulted hydrogels display tunable electromechanical behavior with sensitive, stable, and repeatable variations in resistance upon mechanical deformations. Based on the tunable electromechanical behavior, the hydrogels can act as a wearable strain sensor to monitor finger joint motions, breathing, and even the slight blood pulse. This strategy of building synergistic "soft and hard" structures is successful to integrate the decent mechanical properties, reliable self-healing capability, and high sensing sensitivity together for assembling a high-performance, flexible, and wearable strain sensor.

  9. An Analog Circuit Approximation of the Discrete Wavelet Transform for Ultra Low Power Signal Processing in Wearable Sensor Nodes.

    Science.gov (United States)

    Casson, Alexander J

    2015-12-17

    Ultra low power signal processing is an essential part of all sensor nodes, and particularly so in emerging wearable sensors for biomedical applications. Analog signal processing has an important role in these low power, low voltage, low frequency applications, and there is a key drive to decrease the power consumption of existing analog domain signal processing and to map more signal processing approaches into the analog domain. This paper presents an analog domain signal processing circuit which approximates the output of the Discrete Wavelet Transform (DWT) for use in ultra low power wearable sensors. Analog filters are used for the DWT filters and it is demonstrated how these generate analog domain DWT-like information that embeds information from Butterworth and Daubechies maximally flat mother wavelet responses. The Analog DWT is realised in hardware via g(m)C circuits, designed to operate from a 1.3 V coin cell battery, and provide DWT-like signal processing using under 115 nW of power when implemented in a 0.18 μm CMOS process. Practical examples demonstrate the effective use of the new Analog DWT on ECG (electrocardiogram) and EEG (electroencephalogram) signals recorded from humans.

  10. An Analog Circuit Approximation of the Discrete Wavelet Transform for Ultra Low Power Signal Processing in Wearable Sensor Nodes

    Directory of Open Access Journals (Sweden)

    Alexander J. Casson

    2015-12-01

    Full Text Available Ultra low power signal processing is an essential part of all sensor nodes, and particularly so in emerging wearable sensors for biomedical applications. Analog signal processing has an important role in these low power, low voltage, low frequency applications, and there is a key drive to decrease the power consumption of existing analog domain signal processing and to map more signal processing approaches into the analog domain. This paper presents an analog domain signal processing circuit which approximates the output of the Discrete Wavelet Transform (DWT for use in ultra low power wearable sensors. Analog filters are used for the DWT filters and it is demonstrated how these generate analog domain DWT-like information that embeds information from Butterworth and Daubechies maximally flat mother wavelet responses. The Analog DWT is realised in hardware via g m C circuits, designed to operate from a 1.3 V coin cell battery, and provide DWT-like signal processing using under 115 nW of power when implemented in a 0.18 μm CMOS process. Practical examples demonstrate the effective use of the new Analog DWT on ECG (electrocardiogram and EEG (electroencephalogram signals recorded from humans.

  11. Designing Metallic and Insulating Nanocrystal Heterostructures to Fabricate Highly Sensitive and Solution Processed Strain Gauges for Wearable Sensors.

    Science.gov (United States)

    Lee, Woo Seok; Lee, Seung-Wook; Joh, Hyungmok; Seong, Mingi; Kim, Haneun; Kang, Min Su; Cho, Ki-Hyun; Sung, Yun-Mo; Oh, Soong Ju

    2017-12-01

    All-solution processed, high-performance wearable strain sensors are demonstrated using heterostructure nanocrystal (NC) solids. By incorporating insulating artificial atoms of CdSe quantum dot NCs into metallic artificial atoms of Au NC thin film matrix, metal-insulator heterostructures are designed. This hybrid structure results in a shift close to the percolation threshold, modifying the charge transport mechanism and enhancing sensitivity in accordance with the site percolation theory. The number of electrical pathways is also manipulated by creating nanocracks to further increase its sensitivity, inspired from the bond percolation theory. The combination of the two strategies achieves gauge factor up to 5045, the highest sensitivity recorded among NC-based strain gauges. These strain sensors show high reliability, durability, frequency stability, and negligible hysteresis. The fundamental charge transport behavior of these NC solids is investigated and the combined site and bond percolation theory is developed to illuminate the origin of their enhanced sensitivity. Finally, all NC-based and solution-processed strain gauge sensor arrays are fabricated, which effectively measure the motion of each finger joint, the pulse of heart rate, and the movement of vocal cords of human. This work provides a pathway for designing low-cost and high-performance electronic skin or wearable devices. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Wearable technology: role in respiratory health and disease

    OpenAIRE

    Aliverti, Andrea

    2017-01-01

    In the future, diagnostic devices will be able to monitor a patient’s physiological or biochemical parameters continuously, under natural physiological conditions and in any environment through wearable biomedical sensors. Together with apps that capture and interpret data, and integrated enterprise and cloud data repositories, the networks of wearable devices and body area networks will constitute the healthcare’s Internet of Things. In this review, four main areas of interest for respirator...

  13. Stretch sensors for human body motion

    Science.gov (United States)

    O'Brien, Ben; Gisby, Todd; Anderson, Iain A.

    2014-03-01

    Sensing motion of the human body is a difficult task. From an engineers' perspective people are soft highly mobile objects that move in and out of complex environments. As well as the technical challenge of sensing, concepts such as comfort, social intrusion, usability, and aesthetics are paramount in determining whether someone will adopt a sensing solution or not. At the same time the demands for human body motion sensing are growing fast. Athletes want feedback on posture and technique, consumers need new ways to interact with augmented reality devices, and healthcare providers wish to track recovery of a patient. Dielectric elastomer stretch sensors are ideal for bridging this gap. They are soft, flexible, and precise. They are low power, lightweight, and can be easily mounted on the body or embedded into clothing. From a commercialisation point of view stretch sensing is easier than actuation or generation - such sensors can be low voltage and integrated with conventional microelectronics. This paper takes a birds-eye view of the use of these sensors to measure human body motion. A holistic description of sensor operation and guidelines for sensor design will be presented to help technologists and developers in the space.

  14. Wearable technology: role in respiratory health and disease.

    Science.gov (United States)

    Aliverti, Andrea

    2017-06-01

    In the future, diagnostic devices will be able to monitor a patient's physiological or biochemical parameters continuously, under natural physiological conditions and in any environment through wearable biomedical sensors. Together with apps that capture and interpret data, and integrated enterprise and cloud data repositories, the networks of wearable devices and body area networks will constitute the healthcare's Internet of Things. In this review, four main areas of interest for respiratory healthcare are described: pulse oximetry, pulmonary ventilation, activity tracking and air quality assessment. Although several issues still need to be solved, smart wearable technologies will provide unique opportunities for the future or personalised respiratory medicine.

  15. Validity and Reliability of a Wearable Inertial Sensor to Measure Velocity and Power in the Back Squat and Bench Press.

    Science.gov (United States)

    Orange, Samuel T; Metcalfe, James W; Liefeith, Andreas; Marshall, Phil; Madden, Leigh A; Fewster, Connor R; Vince, Rebecca V

    2018-05-08

    Orange, ST, Metcalfe, JW, Liefeith, A, Marshall, P, Madden, LA, Fewster, CR, and Vince, RV. Validity and reliability of a wearable inertial sensor to measure velocity and power in the back squat and bench press. J Strength Cond Res XX(X): 000-000, 2018-This study examined the validity and reliability of a wearable inertial sensor to measure velocity and power in the free-weight back squat and bench press. Twenty-nine youth rugby league players (18 ± 1 years) completed 2 test-retest sessions for the back squat followed by 2 test-retest sessions for the bench press. Repetitions were performed at 20, 40, 60, 80, and 90% of 1 repetition maximum (1RM) with mean velocity, peak velocity, mean power (MP), and peak power (PP) simultaneously measured using an inertial sensor (PUSH) and a linear position transducer (GymAware PowerTool). The PUSH demonstrated good validity (Pearson's product-moment correlation coefficient [r]) and reliability (intraclass correlation coefficient [ICC]) only for measurements of MP (r = 0.91; ICC = 0.83) and PP (r = 0.90; ICC = 0.80) at 20% of 1RM in the back squat. However, it may be more appropriate for athletes to jump off the ground with this load to optimize power output. Further research should therefore evaluate the usability of inertial sensors in the jump squat exercise. In the bench press, good validity and reliability were evident only for the measurement of MP at 40% of 1RM (r = 0.89; ICC = 0.83). The PUSH was unable to provide a valid and reliable estimate of any other criterion variable in either exercise. Practitioners must be cognizant of the measurement error when using inertial sensor technology to quantify velocity and power during resistance training, particularly with loads other than 20% of 1RM in the back squat and 40% of 1RM in the bench press.

  16. A New Proxy Measurement Algorithm with Application to the Estimation of Vertical Ground Reaction Forces Using Wearable Sensors

    Directory of Open Access Journals (Sweden)

    Yuzhu Guo

    2017-09-01

    Full Text Available Measurement of the ground reaction forces (GRF during walking is typically limited to laboratory settings, and only short observations using wearable pressure insoles have been reported so far. In this study, a new proxy measurement method is proposed to estimate the vertical component of the GRF (vGRF from wearable accelerometer signals. The accelerations are used as the proxy variable. An orthogonal forward regression algorithm (OFR is employed to identify the dynamic relationships between the proxy variables and the measured vGRF using pressure-sensing insoles. The obtained model, which represents the connection between the proxy variable and the vGRF, is then used to predict the latter. The results have been validated using pressure insoles data collected from nine healthy individuals under two outdoor walking tasks in non-laboratory settings. The results show that the vGRFs can be reconstructed with high accuracy (with an average prediction error of less than 5.0% using only one wearable sensor mounted at the waist (L5, fifth lumbar vertebra. Proxy measures with different sensor positions are also discussed. Results show that the waist acceleration-based proxy measurement is more stable with less inter-task and inter-subject variability than the proxy measures based on forehead level accelerations. The proposed proxy measure provides a promising low-cost method for monitoring ground reaction forces in real-life settings and introduces a novel generic approach for replacing the direct determination of difficult to measure variables in many applications.

  17. Modular Robotic Wearable

    DEFF Research Database (Denmark)

    Lund, Henrik Hautop; Pagliarini, Luigi

    2009-01-01

    In this concept paper we trace the contours and define a new approach to robotic systems, composed of interactive robotic modules which are somehow worn on the body. We label such a field as Modular Robotic Wearable (MRW). We describe how, by using modular robotics for creating wearable....... Finally, by focusing on the intersection of the combination modular robotic systems, wearability, and bodymind we attempt to explore the theoretical characteristics of such approach and exploit the possible playware application fields....

  18. Machine learning for large-scale wearable sensor data in Parkinson's disease: Concepts, promises, pitfalls, and futures.

    Science.gov (United States)

    Kubota, Ken J; Chen, Jason A; Little, Max A

    2016-09-01

    For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, "wearable," sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that "learn" from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice. © 2016 International Parkinson and Movement Disorder Society. © 2016 International Parkinson and Movement Disorder Society.

  19. Fabrication of Ultra-Thin Printed Organic TFT CMOS Logic Circuits Optimized for Low-Voltage Wearable Sensor Applications.

    Science.gov (United States)

    Takeda, Yasunori; Hayasaka, Kazuma; Shiwaku, Rei; Yokosawa, Koji; Shiba, Takeo; Mamada, Masashi; Kumaki, Daisuke; Fukuda, Kenjiro; Tokito, Shizuo

    2016-05-09

    Ultrathin electronic circuits that can be manufactured by using conventional printing technologies are key elements necessary to realize wearable health sensors and next-generation flexible electronic devices. Due to their low level of power consumption, complementary (CMOS) circuits using both types of semiconductors can be easily employed in wireless devices. Here, we describe ultrathin CMOS logic circuits, for which not only the source/drain electrodes but also the semiconductor layers were printed. Both p-type and n-type organic thin film transistor devices were employed in a D-flip flop circuit in the newly developed stacked structure and exhibited excellent electrical characteristics, including good carrier mobilities of 0.34 and 0.21 cm(2) V(-1) sec(-1), and threshold voltages of nearly 0 V with low operating voltages. These printed organic CMOS D-flip flop circuits exhibit operating frequencies of 75 Hz and demonstrate great potential for flexible and printed electronics technology, particularly for wearable sensor applications with wireless connectivity.

  20. On-body inertial sensor location recognition

    NARCIS (Netherlands)

    Weenk, D.; van Beijnum, Bernhard J.F.; Goaied, Salma; Baten, Christian T.M.; Hermens, Hermanus J.; Veltink, Petrus H.

    2015-01-01

    Introduction and past research: In previous work we presented an algorithm for automatically identifying the body segment to which an inertial sensor is attached during walking [1]. Using this method, the set-up of inertial motion capture systems becomes easier and attachment errors are avoided. The

  1. A body sensor platform for concurrent applications

    NARCIS (Netherlands)

    Bui, T.V.; Verhoeven, R.; Lukkien, J.J.

    2012-01-01

    This paper presents a Body Sensor Platform supporting concurrent applications that share resources and data. Concerns are application isolation, data privacy and platform trustworthiness in view of dynamic loading of applications. A prototype has been built on commercial-off-the-shelf hardware. The

  2. Bio-assembled, piezoelectric prawn shell made self-powered wearable sensor for non-invasive physiological signal monitoring

    Science.gov (United States)

    Ghosh, Sujoy Kumar; Mandal, Dipankar

    2017-03-01

    A human interactive self-powered wearable sensor is designed using waste by-product prawn shells. The structural origin of intrinsic piezoelectric characteristics of bio-assembled chitin nanofibers has been investigated. It allows the prawn shell to make a tactile sensor that performs also as a highly durable mechanical energy harvester/nanogenerator. The feasibility and fundamental physics of self-powered consumer electronics even from human perception is highlighted by prawn shells made nanogenerator (PSNG). High fidelity and non-invasive monitoring of vital signs, such as radial artery pulse wave and coughing actions, may lead to the potential use of PSNG for early intervention. It is presumed that PSNG has enormous future aspects in real-time as well as remote health care assessment.

  3. A Wireless Biomedical Signal Interface System-on-Chip for Body Sensor Networks.

    Science.gov (United States)

    Lei Wang; Guang-Zhong Yang; Jin Huang; Jinyong Zhang; Li Yu; Zedong Nie; Cumming, D R S

    2010-04-01

    Recent years have seen the rapid development of biosensor technology, system-on-chip design, wireless technology. and ubiquitous computing. When assembled into an autonomous body sensor network (BSN), the technologies become powerful tools in well-being monitoring, medical diagnostics, and personal connectivity. In this paper, we describe the first demonstration of a fully customized mixed-signal silicon chip that has most of the attributes required for use in a wearable or implantable BSN. Our intellectual-property blocks include low-power analog sensor interface for temperature and pH, a data multiplexing and conversion module, a digital platform based around an 8-b microcontroller, data encoding for spread-spectrum wireless transmission, and a RF section requiring very few off-chip components. The chip has been fully evaluated and tested by connection to external sensors, and it satisfied typical system requirements.

  4. Analysing the Effectiveness of Wearable Wireless Sensors in Controlling Crowd Disasters

    NARCIS (Netherlands)

    Teo, Y.H.A.; Viswanathan, V.; Lees, M.; Cai, W.

    2014-01-01

    The Love Parade disaster in Duisberg, Germany lead to several deaths and injuries. Disasters like this occur due to the existence of high densities in a limited area. We propose a wearable electronic device that helps reduce such disasters by directing people and thus controlling the density of the

  5. Wearable sensor platform and mobile application for use in cognitive behavioral therapy for drug addiction and PTSD.

    Science.gov (United States)

    Fletcher, Richard Ribón; Tam, Sharon; Omojola, Olufemi; Redemske, Richard; Kwan, Joyce

    2011-01-01

    We present a wearable sensor platform designed for monitoring and studying autonomic nervous system (ANS) activity for the purpose of mental health treatment and interventions. The mobile sensor system consists of a sensor band worn on the ankle that continuously monitors electrodermal activity (EDA), 3-axis acceleration, and temperature. A custom-designed ECG heart monitor worn on the chest is also used as an optional part of the system. The EDA signal from the ankle bands provides a measure sympathetic nervous system activity and used to detect arousal events. The optional ECG data can be used to improve the sensor classification algorithm and provide a measure of emotional "valence." Both types of sensor bands contain a Bluetooth radio that enables communication with the patient's mobile phone. When a specific arousal event is detected, the phone automatically presents therapeutic and empathetic messages to the patient in the tradition of Cognitive Behavioral Therapy (CBT). As an example of clinical use, we describe how the system is currently being used in an ongoing study for patients with drug-addiction and post-traumatic stress disorder (PTSD).

  6. A Body-and-Mind-Centric Approach to Wearable Personal Assistants

    DEFF Research Database (Denmark)

    Jalaliniya, Shahram

    also need to tie the computer system closer to the conscious and unconscious parts of our minds. In this thesis, I propose a conceptual model for integrating wearable systems into the human perception-cognition-action loop. I empirically investigate the utility of the proposed model for design......Tight integration between humans and computers has long been a vision in wearable computing (“man-machine symbiosis”, “cyborg”), motivated by the potential augmented capabilities in thinking, perceiving, and acting such integration could potentially bring. However, even recent wearable computers (e......; explicit human-computer dialogue) to what is in fact a completely new context of use in which computer users interact with the device(s) on the move and in parallel with real-world tasks. This gives rise to several physical, perceptual, and cognitive challenges due to the limitations of human attentional...

  7. Variable-stiffness joints with embedded force sensor for high-performance wearable gait exoskeletons

    OpenAIRE

    Cestari Soto, Manuel

    2017-01-01

    The growing field of exoskeletons and wearable devices for walking assistance and rehabilitation has advanced considerably over the past few years. The current use of commercial devices is in-hospital rehabilitation of spinal cord injured, nevertheless the purpose of this technology is challenging: to provide gait assistance in daily life activities to the broadest segment of neurological disorders affecting walking and balance. A number of difficulties make this goal a challenge, but to name...

  8. A Body-and-Mind-Centric Approach to Wearable Personal Assistants

    DEFF Research Database (Denmark)

    Jalaliniya, Shahram

    and evaluation of a Wearable Personal Assistant (WPA) for clinicians on the Google Glass platform. The results of my field study in a Copenhagen hospital simulation facility revealed several challenges for WPA users such as unwanted interruptions, social and perceptual problems of parallel interaction...

  9. An IR Sensor Based Smart System to Approximate Core Body Temperature.

    Science.gov (United States)

    Ray, Partha Pratim

    2017-08-01

    Herein demonstrated experiment studies two methods, namely convection and body resistance, to approximate human core body temperature. The proposed system is highly energy efficient that consumes only 165 mW power and runs on 5 VDC source. The implemented solution employs an IR thermographic sensor of industry grade along with AT Mega 328 breakout board. Ordinarily, the IR sensor is placed 1.5-30 cm away from human forehead (i.e., non-invasive) and measured the raw data in terms of skin and ambient temperature which is then converted using appropriate approximation formula to find out core body temperature. The raw data is plotted, visualized, and stored instantaneously in a local machine by means of two tools such as Makerplot, and JAVA-JAR. The test is performed when human object is in complete rest and after 10 min of walk. Achieved results are compared with the CoreTemp CM-210 sensor (by Terumo, Japan) which is calculated to be 0.7 °F different from the average value of BCT, obtained by the proposed IR sensor system. Upon a slight modification, the presented model can be connected with a remotely placed Internet of Things cloud service, which may be useful to inform and predict the user's core body temperature through a probabilistic view. It is also comprehended that such system can be useful as wearable device to be worn on at the hat attachable way.

  10. A Fuzzy Logic-Based Personalized Method to Classify Perceived Exertion in Workplaces Using a Wearable Heart Rate Sensor

    Directory of Open Access Journals (Sweden)

    Pablo Pancardo

    2018-01-01

    Full Text Available Knowing the perceived exertion of workers during their physical activities facilitates the decision-making of supervisors regarding the worker allocation in the appropriate job, actions to prevent accidents, and reassignment of tasks, among others. However, although wearable heart rate sensors represent an effective way to capture perceived exertion, ergonomic methods are generic and they do not consider the diffuse nature of the ranges that classify the efforts. Personalized monitoring is needed to enable a real and efficient classification of perceived individual efforts. In this paper, we propose a heart rate-based personalized method to assess perceived exertion; our method uses fuzzy logic as an option to manage imprecision and uncertainty in involved variables. We applied some experiments to cleaning staff and obtained results that highlight the importance of a custom method to classify perceived exertion of people doing physical work.

  11. Identification of Characteristic Motor Patterns Preceding Freezing of Gait in Parkinson’s Disease Using Wearable Sensors

    Directory of Open Access Journals (Sweden)

    Luca Palmerini

    2017-08-01

    Full Text Available Freezing of gait (FOG is a disabling symptom that is common among patients with advanced Parkinson’s disease (PD. External cues such as rhythmic auditory stimulation can help PD patients experiencing freezing to resume walking. Wearable systems for automatic freezing detection have been recently developed. However, these systems detect a FOG episode after it has happened. Instead, in this study, a new approach for the prediction of FOG (before it actually happens is presented. Prediction of FOG might enable preventive cueing, reducing the likelihood that FOG will occur. Moreover, understanding the causes and circumstances of FOG is still an open research problem. Hence, a quantitative characterization of movement patterns just before FOG (the pre-FOG phase is of great importance. In this study, wearable inertial sensors were used to identify and quantify the characteristics of gait during the pre-FOG phase and compare them with the characteristics of gait that do not precede FOG. The hypothesis of this study is based on the threshold-based model of FOG, which suggests that before FOG occurs, there is a degradation of the gait pattern. Eleven PD subjects were analyzed. Six features extracted from movement signals recorded by inertial sensors showed significant differences between gait and pre-FOG. A classification algorithm was developed in order to test if it is feasible to predict FOG (i.e., detect it before it happens. The aim of the classification procedure was to identify the pre-FOG phase. Results confirm that there is a degradation of gait occurring before freezing. Results also provide preliminary evidence on the feasibility of creating an automatic algorithm to predict FOG. Although some limitations are present, this study shows promising findings for characterizing and identifying pre-FOG patterns, another step toward a better understanding, prediction, and prevention of this disabling symptom.

  12. Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform.

    Science.gov (United States)

    Xu, Huile; Liu, Jinyi; Hu, Haibo; Zhang, Yi

    2016-12-02

    Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.

  13. Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform

    Directory of Open Access Journals (Sweden)

    Huile Xu

    2016-12-01

    Full Text Available Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT or wavelet transform (WT. However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA and instantaneous frequency (IF by means of empirical mode decomposition (EMD, as well as instantaneous energy density (IE and marginal spectrum (MS derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.

  14. The Museum Wearable: Real-Time Sensor-Driven Understanding of Visitors' Interests for Personalized Visually-Augmented Museum Experiences.

    Science.gov (United States)

    Sparacino, Flavia

    This paper describes the museum wearable: a wearable computer that orchestrates an audiovisual narration as a function of the visitors' interests gathered from their physical path in the museum and length of stops. The wearable consists of a lightweight and small computer that people carry inside a shoulder pack. It offers an audiovisual…

  15. Body-Sensor-Network-Based Spasticity Detection.

    Science.gov (United States)

    Misgeld, Berno J E; Luken, Markus; Heitzmann, Daniel; Wolf, Sebastian I; Leonhardt, Steffen

    2016-05-01

    Spasticity is a common disorder of the skeletal muscle with a high incidence in industrialised countries. A quantitative measure of spasticity using body-worn sensors is important in order to assess rehabilitative motor training and to adjust the rehabilitative therapy accordingly. We present a new approach to spasticity detection using the Integrated Posture and Activity Network by Medit Aachen body sensor network (BSN). For this, a new electromyography (EMG) sensor node was developed and employed in human locomotion. Following an analysis of the clinical gait data of patients with unilateral cerebral palsy, a novel algorithm was developed based on the idea to detect coactivation of antagonistic muscle groups as observed in the exaggerated stretch reflex with associated joint rigidity. The algorithm applies a cross-correlation function to the EMG signals of two antagonistically working muscles and subsequent weighting using a Blackman window. The result is a coactivation index which is also weighted by the signal equivalent energy to exclude positive detection of inactive muscles. Our experimental study indicates good performance in the detection of coactive muscles associated with spasticity from clinical data as well as measurements from a BSN in qualitative comparison with the Modified Ashworth Scale as classified by clinical experts. Possible applications of the new algorithm include (but are not limited to) use in robotic sensorimotor therapy to reduce the effect of spasticity.

  16. A Comparative Study of Physiological Monitoring with a Wearable Opto-Electronic Patch Sensor (OEPS for Motion Reduction

    Directory of Open Access Journals (Sweden)

    Abdullah Alzahrani

    2015-06-01

    Full Text Available This paper presents a comparative study in physiological monitoring between a wearable opto-electronic patch sensor (OEPS comprising a three-axis Microelectromechanical systems (MEMs accelerometer (3MA and commercial devices. The study aims to effectively capture critical physiological parameters, for instance, oxygen saturation, heart rate, respiration rate and heart rate variability, as extracted from the pulsatile waveforms captured by OEPS against motion artefacts when using the commercial probe. The protocol involved 16 healthy subjects and was designed to test the features of OEPS, with emphasis on the effective reduction of motion artefacts through the utilization of a 3MA as a movement reference. The results show significant agreement between the heart rates from the reference measurements and the recovered signals. Significance of standard deviation and error of mean yield values of 2.27 and 0.65 beats per minute, respectively; and a high correlation (0.97 between the results of the commercial sensor and OEPS. T, Wilcoxon and Bland-Altman with 95% limit of agreement tests were also applied in the comparison of heart rates extracted from these sensors, yielding a mean difference (MD: 0.08. The outcome of the present work incites the prospects of OEPS on physiological monitoring during physical activities.

  17. A bi-articular model for scapular-humeral rhythm reconstruction through data from wearable sensors.

    Science.gov (United States)

    Lorussi, Federico; Carbonaro, Nicola; De Rossi, Danilo; Tognetti, Alessandro

    2016-04-23

    Patient-specific performance assessment of arm movements in daily life activities is fundamental for neurological rehabilitation therapy. In most applications, the shoulder movement is simplified through a socket-ball joint, neglecting the movement of the scapular-thoracic complex. This may lead to significant errors. We propose an innovative bi-articular model of the human shoulder for estimating the position of the hand in relation to the sternum. The model takes into account both the scapular-toracic and gleno-humeral movements and their ratio governed by the scapular-humeral rhythm, fusing the information of inertial and textile-based strain sensors. To feed the reconstruction algorithm based on the bi-articular model, an ad-hoc sensing shirt was developed. The shirt was equipped with two inertial measurement units (IMUs) and an integrated textile strain sensor. We built the bi-articular model starting from the data obtained in two planar movements (arm abduction and flexion in the sagittal plane) and analysing the error between the reference data - measured through an optical reference system - and the socket-ball approximation of the shoulder. The 3D model was developed by extending the behaviour of the kinematic chain revealed in the planar trajectories through a parameter identification that takes into account the body structure of the subject. The bi-articular model was evaluated in five subjects in comparison with the optical reference system. The errors were computed in terms of distance between the reference position of the trochlea (end-effector) and the correspondent model estimation. The introduced method remarkably improved the estimation of the position of the trochlea (and consequently the estimation of the hand position during reaching activities) reducing position errors from 11.5 cm to 1.8 cm. Thanks to the developed bi-articular model, we demonstrated a reliable estimation of the upper arm kinematics with a minimal sensing system suitable for

  18. Leveraging knowledge from physiological data: on-body heat stress risk prediction with sensor networks.

    Science.gov (United States)

    Gaura, Elena; Kemp, John; Brusey, James

    2013-12-01

    The paper demonstrates that wearable sensor systems, coupled with real-time on-body processing and actuation, can enhance safety for wearers of heavy protective equipment who are subjected to harsh thermal environments by reducing risk of Uncompensable Heat Stress (UHS). The work focuses on Explosive Ordnance Disposal operatives and shows that predictions of UHS risk can be performed in real-time with sufficient accuracy for real-world use. Furthermore, it is shown that the required sensory input for such algorithms can be obtained with wearable, non-intrusive sensors. Two algorithms, one based on Bayesian nets and another on decision trees, are presented for determining the heat stress risk, considering the mean skin temperature prediction as a proxy. The algorithms are trained on empirical data and have accuracies of 92.1±2.9% and 94.4±2.1%, respectively when tested using leave-one-subject-out cross-validation. In applications such as Explosive Ordnance Disposal operative monitoring, such prediction algorithms can enable autonomous actuation of cooling systems and haptic alerts to minimize casualties.

  19. Coaxial Thermoplastic Elastomer-Wrapped Carbon Nanotube Fibers for Deformable and Wearable Strain Sensors

    KAUST Repository

    Zhou, Jian; Xu, Xuezhu; Xin, Yangyang; Lubineau, Gilles

    2018-01-01

    performances in these design requirements. Here, achieving highly stretchable and sensitive strain sensors by using a coaxial structure, prepared via coaxial wet spinning of thermoplastic elastomer-wrapped carbon nanotube fibers, is proposed. The sensors attain

  20. Gas sensors boosted by two-dimensional h-BN enabled transfer on thin substrate foils: towards wearable and portable applications.

    Science.gov (United States)

    Ayari, Taha; Bishop, Chris; Jordan, Matthew B; Sundaram, Suresh; Li, Xin; Alam, Saiful; ElGmili, Youssef; Patriarche, Gilles; Voss, Paul L; Salvestrini, Jean Paul; Ougazzaden, Abdallah

    2017-11-09

    The transfer of GaN based gas sensors to foreign substrates provides a pathway to enhance sensor performance, lower the cost and extend the applications to wearable, mobile or disposable systems. The main keys to unlocking this pathway is to grow and fabricate the sensors on large h-BN surface and to transfer them to the flexible substrate without any degradation of the performances. In this work, we develop a new generation of AlGaN/GaN gas sensors with boosted performances on a low cost flexible substrate. We fabricate 2-inch wafer scale AlGaN/GaN gas sensors on sacrificial two-dimensional (2D) nano-layered h-BN without any delamination or cracks and subsequently transfer sensors to an acrylic surface on metallic foil. This technique results in a modification of relevant device properties, leading to a doubling of the sensitivity to NO 2 gas and a response time that is more than 6 times faster than before transfer. This new approach for GaN-based sensor design opens new avenues for sensor improvement via transfer to more suitable substrates, and is promising for next-generation wearable and portable opto-electronic devices.

  1. Wearable Android Android wear and Google Fit app development

    CERN Document Server

    Mishra, Sanjay M

    2015-01-01

    Software Development/Mobile/Android/Wearable/Fitness Build ""Wearable"" Applications on the Android Wear and Google Fit Platforms This book covers wearable computing and wearable application development particularly for Android Wear (smartwatches) and Google Fit (fitness sensors). It provides relevant history, background and core concepts of wearable computing and ubiquitous computing, as a foundation for designing/developing applications for the Android Wear and Google Fit platforms. This book is intended for Android wearable enthusiasts, technologists and software developers. Gain ins

  2. A bendable and wearable cardiorespiratory monitoring device fusing two noncontact sensor principles.

    Science.gov (United States)

    Teichmann, Daniel; De Matteis, Dennis; Bartelt, Thorsten; Walter, Marian; Leonhardt, Steffen

    2015-05-01

    A mobile device is presented for monitoring both respiration and pulse. The device is developed as a bendable/flexible inlay that can be placed in a shirt pocket or the inside pocket of a jacket. To achieve optimum monitoring performance, the device combines two sensor principles, which work in a safe noncontact way through several layers of cotton or other textiles. One sensor, based on magnetic induction, is intended for respiratory monitoring, and the other is a reflective photoplethysmography sensor intended for pulse detection. Because each sensor signal has some dependence on both physiological parameters, fusing the sensor signals allows enhanced signal coverage.

  3. Wearable and flexible electronics for continuous molecular monitoring.

    Science.gov (United States)

    Yang, Yiran; Gao, Wei

    2018-04-03

    Wearable biosensors have received tremendous attention over the past decade owing to their great potential in predictive analytics and treatment toward personalized medicine. Flexible electronics could serve as an ideal platform for personalized wearable devices because of their unique properties such as light weight, low cost, high flexibility and great conformability. Unlike most reported flexible sensors that mainly track physical activities and vital signs, the new generation of wearable and flexible chemical sensors enables real-time, continuous and fast detection of accessible biomarkers from the human body, and allows for the collection of large-scale information about the individual's dynamic health status at the molecular level. In this article, we review and highlight recent advances in wearable and flexible sensors toward continuous and non-invasive molecular analysis in sweat, tears, saliva, interstitial fluid, blood, wound exudate as well as exhaled breath. The flexible platforms, sensing mechanisms, and device and system configurations employed for continuous monitoring are summarized. We also discuss the key challenges and opportunities of the wearable and flexible chemical sensors that lie ahead.

  4. Wearable Sensors for Measuring Movement in Short Sessions of Mindfulness Sitting Meditation: A Pilot Study

    Directory of Open Access Journals (Sweden)

    Victor H. Rodriguez

    2018-01-01

    Full Text Available Mindfulness techniques are useful tools in health and well-being. To improve and facilitate formal training, beginners need to know if they are in a stable sitting posture and if they can hold it. Previous monitoring studies did not consider stability during sitting meditation or were specific for longer traditional practices. In this paper, we have extended and adapted previous studies to modern mindfulness practices and posed two questions: (a Which is the best meditation seat for short sessions? In this way, the applications of stability measures are expanded to meditation activities, in which the sitting posture favors stability, and (b Which is the most sensitive location of an accelerometer to measure body motion during short meditation sessions? A pilot study involving 31 volunteers was conducted using inertial sensors. The results suggest that thumb, head, or infraclavicular locations can be chosen to measure stability despite the habitual lumbar or sacral region found in the literature. Another important finding of this study is that zafus, chairs, and meditation benches are suitable for short meditation sessions in a sitting posture, although the zafu seems to allow for fewer postural changes. This finding opens new opportunities to design very simple and comfortable measuring systems.

  5. Omni-Purpose Stretchable Strain Sensor Based on a Highly Dense Nanocracking Structure for Whole-Body Motion Monitoring.

    Science.gov (United States)

    Jeon, Hyungkook; Hong, Seong Kyung; Kim, Min Seo; Cho, Seong J; Lim, Geunbae

    2017-12-06

    Here, we report an omni-purpose stretchable strain sensor (OPSS sensor) based on a nanocracking structure for monitoring whole-body motions including both joint-level and skin-level motions. By controlling and optimizing the nanocracking structure, inspired by the spider sensory system, the OPSS sensor is endowed with both high sensitivity (gauge factor ≈ 30) and a wide working range (strain up to 150%) under great linearity (R 2 = 0.9814) and fast response time (sensor has advantages of being extremely simple, patternable, integrated circuit-compatible, and reliable in terms of reproducibility. Using the OPSS sensor, we detected various human body motions including both moving of joints and subtle deforming of skin such as pulsation. As specific medical applications of the sensor, we also successfully developed a glove-type hand motion detector and a real-time Morse code communication system for patients with general paralysis. Therefore, considering the outstanding sensing performances, great advantages of the fabrication process, and successful results from a variety of practical applications, we believe that the OPSS sensor is a highly suitable strain sensor for whole-body motion monitoring and has potential for a wide range of applications, such as medical robotics and wearable healthcare devices.

  6. Contact Patterns in a High School: A Comparison between Data Collected Using Wearable Sensors, Contact Diaries and Friendship Surveys.

    Directory of Open Access Journals (Sweden)

    Rossana Mastrandrea

    Full Text Available Given their importance in shaping social networks and determining how information or transmissible diseases propagate in a population, interactions between individuals are the subject of many data collection efforts. To this aim, different methods are commonly used, ranging from diaries and surveys to decentralised infrastructures based on wearable sensors. These methods have each advantages and limitations but are rarely compared in a given setting. Moreover, as surveys targeting friendship relations might suffer less from memory biases than contact diaries, it is interesting to explore how actual contact patterns occurring in day-to-day life compare with friendship relations and with online social links. Here we make progresses in these directions by leveraging data collected in a French high school and concerning (i face-to-face contacts measured by two concurrent methods, namely wearable sensors and contact diaries, (ii self-reported friendship surveys, and (iii online social links. We compare the resulting data sets and find that most short contacts are not reported in diaries while long contacts have a large reporting probability, and that the durations of contacts tend to be overestimated in the diaries. Moreover, measured contacts corresponding to reported friendship can have durations of any length but all long contacts do correspond to a reported friendship. On the contrary, online links that are not also reported in the friendship survey correspond to short face-to-face contacts, highlighting the difference of nature between reported friendships and online links. Diaries and surveys suffer moreover from a low sampling rate, as many students did not fill them, showing that the sensor-based platform had a higher acceptability. We also show that, despite the biases of diaries and surveys, the overall structure of the contact network, as quantified by the mixing patterns between classes, is correctly captured by both networks of self

  7. Contact Patterns in a High School: A Comparison between Data Collected Using Wearable Sensors, Contact Diaries and Friendship Surveys.

    Science.gov (United States)

    Mastrandrea, Rossana; Fournet, Julie; Barrat, Alain

    2015-01-01

    Given their importance in shaping social networks and determining how information or transmissible diseases propagate in a population, interactions between individuals are the subject of many data collection efforts. To this aim, different methods are commonly used, ranging from diaries and surveys to decentralised infrastructures based on wearable sensors. These methods have each advantages and limitations but are rarely compared in a given setting. Moreover, as surveys targeting friendship relations might suffer less from memory biases than contact diaries, it is interesting to explore how actual contact patterns occurring in day-to-day life compare with friendship relations and with online social links. Here we make progresses in these directions by leveraging data collected in a French high school and concerning (i) face-to-face contacts measured by two concurrent methods, namely wearable sensors and contact diaries, (ii) self-reported friendship surveys, and (iii) online social links. We compare the resulting data sets and find that most short contacts are not reported in diaries while long contacts have a large reporting probability, and that the durations of contacts tend to be overestimated in the diaries. Moreover, measured contacts corresponding to reported friendship can have durations of any length but all long contacts do correspond to a reported friendship. On the contrary, online links that are not also reported in the friendship survey correspond to short face-to-face contacts, highlighting the difference of nature between reported friendships and online links. Diaries and surveys suffer moreover from a low sampling rate, as many students did not fill them, showing that the sensor-based platform had a higher acceptability. We also show that, despite the biases of diaries and surveys, the overall structure of the contact network, as quantified by the mixing patterns between classes, is correctly captured by both networks of self-reported contacts and

  8. Open source platform for collaborative construction of wearable sensor datasets for human motion analysis and an application for gait analysis.

    Science.gov (United States)

    Llamas, César; González, Manuel A; Hernández, Carmen; Vegas, Jesús

    2016-10-01

    Nearly every practical improvement in modeling human motion is well founded in a properly designed collection of data or datasets. These datasets must be made publicly available for the community could validate and accept them. It is reasonable to concede that a collective, guided enterprise could serve to devise solid and substantial datasets, as a result of a collaborative effort, in the same sense as the open software community does. In this way datasets could be complemented, extended and expanded in size with, for example, more individuals, samples and human actions. For this to be possible some commitments must be made by the collaborators, being one of them sharing the same data acquisition platform. In this paper, we offer an affordable open source hardware and software platform based on inertial wearable sensors in a way that several groups could cooperate in the construction of datasets through common software suitable for collaboration. Some experimental results about the throughput of the overall system are reported showing the feasibility of acquiring data from up to 6 sensors with a sampling frequency no less than 118Hz. Also, a proof-of-concept dataset is provided comprising sampled data from 12 subjects suitable for gait analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

  9. Towards Building a Computer Aided Education System for Special Students Using Wearable Sensor Technologies

    Directory of Open Access Journals (Sweden)

    Raja Majid Mehmood

    2017-02-01

    Full Text Available Human computer interaction is a growing field in terms of helping people in their daily life to improve their living. Especially, people with some disability may need an interface which is more appropriate and compatible with their needs. Our research is focused on similar kinds of problems, such as students with some mental disorder or mood disruption problems. To improve their learning process, an intelligent emotion recognition system is essential which has an ability to recognize the current emotional state of the brain. Nowadays, in special schools, instructors are commonly use some conventional methods for managing special students for educational purposes. In this paper, we proposed a novel computer aided method for instructors at special schools where they can teach special students with the support of our system using wearable technologies.

  10. Highly sensitive wearable strain sensor based on silver nanowires and nanoparticles

    Science.gov (United States)

    Shengbo, Sang; Lihua, Liu; Aoqun, Jian; Qianqian, Duan; Jianlong, Ji; Qiang, Zhang; Wendong, Zhang

    2018-06-01

    Here, we propose a highly sensitive and stretchable strain sensor based on silver nanoparticles and nanowires (Ag NPs and NWs), advancing the rapid development of electronic skin. To improve the sensitivity of strain sensors based on silver nanowires (Ag NWs), Ag NPs and NWs were added to polydimethylsiloxane (PDMS) as an aid filler. Silver nanoparticles (Ag NPs) increase the conductive paths for electrons, leading to the low resistance of the resulting sensor (14.9 Ω). The strain sensor based on Ag NPs and NWs showed strong piezoresistivity with a tunable gauge factor (GF) at 3766, and a change in resistance as the strain linearly increased from 0% to 28.1%. The high GF demonstrates the irreplaceable role of Ag NPs in the sensor. Moreover, the applicability of our high-performance strain sensor has been demonstrated by its ability to sense movements caused by human talking, finger bending, wrist raising and walking.

  11. Human movement activity classification approaches that use wearable sensors and mobile devices

    Science.gov (United States)

    Kaghyan, Sahak; Sarukhanyan, Hakob; Akopian, David

    2013-03-01

    Cell phones and other mobile devices become part of human culture and change activity and lifestyle patterns. Mobile phone technology continuously evolves and incorporates more and more sensors for enabling advanced applications. Latest generations of smart phones incorporate GPS and WLAN location finding modules, vision cameras, microphones, accelerometers, temperature sensors etc. The availability of these sensors in mass-market communication devices creates exciting new opportunities for data mining applications. Particularly healthcare applications exploiting build-in sensors are very promising. This paper reviews different approaches of human activity recognition.

  12. A Synchronous Multi-Body Sensor Platform in a Wireless Body Sensor Network: Design and Implementation

    Science.gov (United States)

    Gil, Yeongjoon; Wu, Wanqing; Lee, Jungtae

    2012-01-01

    Background Human life can be further improved if diseases and disorders can be predicted before they become dangerous, by correctly recognizing signals from the human body, so in order to make disease detection more precise, various body-signals need to be measured simultaneously in a synchronized manner. Object This research aims at developing an integrated system for measuring four signals (EEG, ECG, respiration, and PPG) and simultaneously producing synchronous signals on a Wireless Body Sensor Network. Design We designed and implemented a platform for multiple bio-signals using Bluetooth communication. Results First, we developed a prototype board and verified the signals from the sensor platform using frequency responses and quantities. Next, we designed and implemented a lightweight, ultra-compact, low cost, low power-consumption Printed Circuit Board. Conclusion A synchronous multi-body sensor platform is expected to be very useful in telemedicine and emergency rescue scenarios. Furthermore, this system is expected to be able to analyze the mutual effects among body signals. PMID:23112605

  13. A Synchronous Multi-Body Sensor Platform in a Wireless Body Sensor Network: Design and Implementation

    Directory of Open Access Journals (Sweden)

    Jungtae Lee

    2012-07-01

    Full Text Available Background: Human life can be further improved if diseases and disorders can be predicted before they become dangerous, by correctly recognizing signals from the human body, so in order to make disease detection more precise, various body-signals need to be measured simultaneously in a synchronized manner. Object: This research aims at developing an integrated system for measuring four signals (EEG, ECG, respiration, and PPG and simultaneously producing synchronous signals on a Wireless Body Sensor Network. Design: We designed and implemented a platform for multiple bio-signals using Bluetooth communication. Results: First, we developed a prototype board and verified the signals from the sensor platform using frequency responses and quantities. Next, we designed and implemented a lightweight, ultra-compact, low cost, low power-consumption Printed Circuit Board. Conclusion: A synchronous multi-body sensor platform is expected to be very useful in telemedicine and emergency rescue scenarios. Furthermore, this system is expected to be able to analyze the mutual effects among body signals.

  14. Use of Wearable Sensors and Biometric Variables in an Artificial Pancreas System

    Science.gov (United States)

    Turksoy, Kamuran; Monforti, Colleen; Park, Minsun; Griffith, Garett; Quinn, Laurie; Cinar, Ali

    2017-01-01

    An artificial pancreas (AP) computes the optimal insulin dose to be infused through an insulin pump in people with Type 1 Diabetes (T1D) based on information received from a continuous glucose monitoring (CGM) sensor. It has been recognized that exercise is a major challenge in the development of an AP system. The use of biometric physiological variables in an AP system may be beneficial for prevention of exercise-induced challenges and better glucose regulation. The goal of the present study is to find a correlation between biometric variables such as heart rate (HR), heat flux (HF), skin temperature (ST), near-body temperature (NBT), galvanic skin response (GSR), and energy expenditure (EE), 2D acceleration-mean of absolute difference (MAD) and changes in glucose concentrations during exercise via partial least squares (PLS) regression and variable importance in projection (VIP) in order to determine which variables would be most useful to include in a future artificial pancreas. PLS and VIP analyses were performed on data sets that included seven different types of exercises. Data were collected from 26 clinical experiments. Clinical results indicate ST to be the most consistently important (important for six out of seven tested exercises) variable over all different exercises tested. EE and HR are also found to be important variables over several types of exercise. We also found that the importance of GSR and NBT observed in our experiments might be related to stress and the effect of changes in environmental temperature on glucose concentrations. The use of the biometric measurements in an AP system may provide better control of glucose concentration. PMID:28272368

  15. Multi-Functional Sensor System for Heart Rate, Body Position and Movement Intensity Analysis

    Directory of Open Access Journals (Sweden)

    Michael MAO

    2008-12-01

    Full Text Available A novel multi-functional wearable sensor has been developed with multi-axis accelerometer, disposable hydro-gel electrodes, and analog filtering components. This novel sensor implementation can be used for detecting common body positions, movement intensity, and measures bio-potential signals for ECG and heart rate analysis. Based on the novel sensor principle, a prototype combines position detection, heart rate detection, and motion intensity level detection together in a handheld device that records the physiological information and wirelessly transmits the signals through Bluetooth to a mobile phone. Static body positions such as standing/sitting, lying supine, prone, and on the sides have been detected with high accuracy (97.7 % during the subject tests. Further, an algorithm that detects body movement intensity that can potentially be applied in real-time monitoring physical activity level is proposed based on average variance values. Motion intensity results show variance values increase and exercise intensity increases for almost all of the cases. A clear relation between movement intensity level shown by an increase in frequency and/or speed of exercise increases the variance values detected in all three spatial axes.

  16. Telehealth, Wearable Sensors, and the Internet: Will They Improve Stroke Outcomes Through Increased Intensity of Therapy, Motivation, and Adherence to Rehabilitation Programs?

    Science.gov (United States)

    Burridge, Jane H; Lee, Alan Chong W; Turk, Ruth; Stokes, Maria; Whitall, Jill; Vaidyanathan, Ravi; Clatworthy, Phil; Hughes, Ann-Marie; Meagher, Claire; Franco, Enrico; Yardley, Lucy

    2017-07-01

    Stroke, predominantly a condition of older age, is a major cause of acquired disability in the global population and puts an increasing burden on health care resources. Clear evidence for the importance of intensity of therapy in optimizing functional outcomes is found in animal models, supported by neuroimaging and behavioral research, and strengthened by recent meta-analyses from multiple clinical trials. However, providing intensive therapy using conventional treatment paradigms is expensive and sometimes not feasible because of social and environmental factors. This article addresses the need for cost-effective increased intensity of practice and suggests potential benefits of telehealth (TH) as an innovative model of care in physical therapy. We provide an overview of TH and present evidence that a web-supported program, used in conjunction with constraint-induced therapy (CIT), can increase intensity and adherence to a rehabilitation regimen. The design and feasibility testing of this web-based program, "LifeCIT," is presented. We describe how wearable sensors can monitor activity and provide feedback to patients and therapists. The methodology for the development of a wearable device with embedded inertial and mechanomyographic sensors, algorithms to classify functional movement, and a graphical user interface to present meaningful data to patients to support a home exercise program is explained. We propose that wearable sensor technologies and TH programs have the potential to provide most-effective, intensive, home-based stroke rehabilitation.

  17. Clip-on wireless wearable microwave sensor for ambulatory cardiac monitoring.

    Science.gov (United States)

    Fletcher, Richard R; Kulkarni, Sarang

    2010-01-01

    We present a new type of non-contact sensor for use in ambulatory cardiac monitoring. The sensor operation is based on a microwave Doppler technique; however, instead of detecting the heart activity from a distance, the sensor is placed on the patient's chest over the clothing. The microwave sensor directly measures heart movement rather than electrical activity, and is thus complementary to ECG. The primary advantages of the microwave sensor includes small size, light weight, low power, low-cost, and the ability to operate through clothing. We present a sample sensor design that incorporates a 2.4 GHz Doppler circuit, integrated microstrip patch antenna, and microntroller with 12-bit ADC data sampling. The prototype sensor also includes a wireless data link for sending data to a remote PC or mobile phone. Sample data is shown for several subjects and compared to data from a commercial portable ECG device. Data collected from the microwave sensor exhibits a significant amount of features, indicating possible use as a tool for monitoring heart mechanics and detection of abnormalities such as fibrillation and akinesia.

  18. 3D Printed Wearable Sensors with Liquid Metals for the Pose Detection of Snakelike Soft Robots.

    Science.gov (United States)

    Zhou, Luyu; Gao, Qing; Zhan, Jun-Fu; Xie, Chao-Qi; Fu, Jianzhong; He, Yong

    2018-06-18

    Liquid metal-based flexible sensors, which utilize advanced liquid conductive material to serve as sensitive element, is emerging as a promising solution to measure large deformations. Nowadays, one of the biggest challenges for precise control of soft robots is the detection of their real time positions. Existing fabrication methods are unable to fabricate flexible sensors that match the shape of soft robots. In this report, we firstly described a novel 3D printed multi-function inductance flexible and stretchable sensor with liquid metals (LMs), which is capable of measuring both axial tension and curvature. This sensor is fabricated with a developed coaxial liquid metal 3D printer by co-printing of silicone rubber and LMs. Due to the solenoid shape, this sensor can be easily installed on snakelike soft robots and can accurately distinguish different degrees of tensile and bending deformation. We determined the structural parameters of the sensor and proved its excellent stability and reliability. As a demonstration, we used this sensor to measure the curvature of a finger and feedback the position of endoscope, a typical snakelike structure. Because of its bending deformation form consistent with the actual working status of the soft robot and unique shape, this sensor has better practical application prospects in the pose detection.

  19. Crystallization and mechanical behavior of the ferroelectric polymer nonwoven fiber fabrics for highly durable wearable sensor applications

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Z.H. [Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan (China); Center for Nanoscience & Nanotechnology, National Sun Yat-Sen University, Taiwan (China); National Science Council Core Facilities Laboratory for Nano-Science and Nano-Technology in Kaohsiung-Pingtung Area, Taiwan (China); Micro/Meso Mechanical Manufacturing R& D Department, Metal Industries Research and Development Centre, Kaohsiung 81160, Taiwan (China); Pan, C.T., E-mail: panct@mail.nsysu.edu.tw [Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan (China); Center for Nanoscience & Nanotechnology, National Sun Yat-Sen University, Taiwan (China); National Science Council Core Facilities Laboratory for Nano-Science and Nano-Technology in Kaohsiung-Pingtung Area, Taiwan (China); Yen, C.K. [Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan (China); Center for Nanoscience & Nanotechnology, National Sun Yat-Sen University, Taiwan (China); National Science Council Core Facilities Laboratory for Nano-Science and Nano-Technology in Kaohsiung-Pingtung Area, Taiwan (China); Lin, L.W. [Department of Mechanical Engineering, University of California, Berkeley, CA 94720 (United States); Berkeley Sensor and Actuator Center, University of California, Berkeley, CA 94720 (United States); Huang, J.C. [Department of Materials and Optoelectronic Science, National Sun Yat-Sen University, Kaohsiung, Taiwan (China); Ke, C.A. [Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan (China)

    2015-08-15

    Highlights: • Performance of the hollow cylindrical near-field electrospinning (HCNFES). • Well-aligned self-assembled PVDF nonwoven fiber fabrics. • Highly durable wearable sensors. • The mechanical characterization of HCNFES piezoelectric NFFs. • The formation of β-form extended-chain crystallites in the PVDF nanofibers. - Abstract: The mechanical characterization of the electrospinning polyvinylidene fluoride (PVDF) nonwoven fiber fabrics (NFFs) doped with multi-walled carbon nanotubes (MWCNTs) was investigated. Piezoelectric composite nanofibers of the PVDF/MWCNTs were directly electrospun by the hollow cylindrical near-field electrospinning (HCNFES) without any post-poling treatment. We have made the HCNFES NFFs consisted of high-orderly arranged nanofiber assemblies for further characterizing the effect of MWCNTs filling PVDF nanofibers. An in situ electrical poling and high uniaxial stretching imparted on the polymer jet during the HCNFES process, which naturally align the dipoles in the PVDF crystals and promote the formation of the polar β-crystalline phase within the fibers. Moreover, the reinforcement of the HCNFES PVDF nanofibers indicated the improvement in mechanical properties and the degree of high oriented extended-chain crystallites through adding adequate contents of MWCNTs. In the case of alignment of the all-trans polymer chains in the vicinity of MWCNTs along the fiber axis, X-ray diffraction (XRD) patterns showed the strongest diffraction peak of the β-crystalline phase. In the comparison of the near-field electrospinning (NFES), the HCNFES nanofibers with smooth surface and smaller diameter can easily form high density structural NFFs. After nano-indentation and tensile strength measurements, the results indicated that the mechanical properties of the HCNFES NFFs are better than the NFES ones. When 16 wt% PVDF solution doped with 0.03 wt% MWCNTs, the results reveal that Young's modulus, hardness, yield stress, yield strain

  20. Loose Coupling of Wearable-Based INSs with Automatic Heading Evaluation

    OpenAIRE

    Bousdar Ahmed, Dina; Munoz Diaz, Estefania

    2017-01-01

    Position tracking of pedestrians by means of inertial sensors is a highly explored field of research. In fact, there are already many approaches to implement inertial navigation systems (INSs). However, most of them use a single inertial measurement unit (IMU) attached to the pedestrian’s body. Since wearable-devices will be given items in the future, this work explores the implementation of an INS using two wearable-based IMUs. A loosely coupled approach is proposed to combine the outputs of...

  1. Novel highly sensitive and wearable pressure sensors from conductive three-dimensional fabric structures

    International Nuclear Information System (INIS)

    Li, Jianfeng; Xu, Bingang

    2015-01-01

    Pressure sensors based on three-dimensional fabrics have all the excellent properties of the textile substrate: excellent compressibility, good air permeability and moisture transmission ability, which will find applications ranging from the healthcare industry to daily usage. In this paper, novel pressure sensors based on 3D spacer fabrics have been developed by a proposed multi-coating method. By this coating method, carbon black can be coated uniformly on the silicon elastomer which is attached and slightly cured on the 3D fabric surface beforehand. The as-made pressure sensors have good conductivity and can measure external pressure up to 283 kPa with an electrical conductivity range of 9.8 kΩ. The sensitivity of 3D fabric pressure sensors can be as high as 50.31×10 −3 kPa −1 , which is better than other textile based pressure sensors. When the as-made sensors are pressed, their electrical resistance will decrease because of more conductive connections and bending of fibers in the spacer layer. The sensing mechanism related to fiber bending has been explored by using an equivalent resistance model. The newly developed 3D sensor devices can be designed to exhibit different sensing performances by simply changing the structures of fabric substrate, which endows this kind of device more flexibility in related applications. (paper)

  2. A wearable diffuse reflectance sensor for continuous monitoring of cutaneous blood content

    International Nuclear Information System (INIS)

    Zakharov, P; Talary, M S; Caduff, A

    2009-01-01

    An optical diffuse reflectance sensor for characterization of cutaneous blood content and optimized for continuous monitoring has been developed as part of a non-invasive multisensor system for glucose monitoring. A Monte Carlo simulation of the light propagation in the multilayered skin model has been performed in order to estimate the optimal geometrical separation of the light source and detector for skin and underlying tissue. We have observed that the pathlength within the upper vascular plexus of the skin which defines the sensor sensitivity initially grows with increasing source-detector distance (SDD) before reaching a maximum at 3.5 mm and starts to decay with further increase. At the same time, for distances above 2.4 mm, the sensor becomes sensitive to muscle blood content, which decreases the specificity to skin perfusion monitoring. Thus, the SDDs in the range from 1.5 mm to 2.4 mm satisfy the requirements of sensor sensitivity and specificity. The hardware implementation of the system has been realized and tested in laboratory experiments with a venous occlusion procedure and in an outpatient clinical study in 16 patients with type 1 diabetes mellitus. For both testing procedures, the optical sensor demonstrated high sensitivity to perfusion change provoking events. The general build-up of cutaneous blood under the sensor has been observed which can be associated with pressure-induced vasodilation as a response to the sensor application.

  3. A Novel Feature Optimization for Wearable Human-Computer Interfaces Using Surface Electromyography Sensors

    Directory of Open Access Journals (Sweden)

    Han Sun

    2018-03-01

    Full Text Available The novel human-computer interface (HCI using bioelectrical signals as input is a valuable tool to improve the lives of people with disabilities. In this paper, surface electromyography (sEMG signals induced by four classes of wrist movements were acquired from four sites on the lower arm with our designed system. Forty-two features were extracted from the time, frequency and time-frequency domains. Optimal channels were determined from single-channel classification performance rank. The optimal-feature selection was according to a modified entropy criteria (EC and Fisher discrimination (FD criteria. The feature selection results were evaluated by four different classifiers, and compared with other conventional feature subsets. In online tests, the wearable system acquired real-time sEMG signals. The selected features and trained classifier model were used to control a telecar through four different paradigms in a designed environment with simple obstacles. Performance was evaluated based on travel time (TT and recognition rate (RR. The results of hardware evaluation verified the feasibility of our acquisition systems, and ensured signal quality. Single-channel analysis results indicated that the channel located on the extensor carpi ulnaris (ECU performed best with mean classification accuracy of 97.45% for all movement’s pairs. Channels placed on ECU and the extensor carpi radialis (ECR were selected according to the accuracy rank. Experimental results showed that the proposed FD method was better than other feature selection methods and single-type features. The combination of FD and random forest (RF performed best in offline analysis, with 96.77% multi-class RR. Online results illustrated that the state-machine paradigm with a 125 ms window had the highest maneuverability and was closest to real-life control. Subjects could accomplish online sessions by three sEMG-based paradigms, with average times of 46.02, 49.06 and 48.08 s

  4. Flexible, Highly Sensitive, and Wearable Pressure and Strain Sensors with Graphene Porous Network Structure.

    Science.gov (United States)

    Pang, Yu; Tian, He; Tao, Luqi; Li, Yuxing; Wang, Xuefeng; Deng, Ningqin; Yang, Yi; Ren, Tian-Ling

    2016-10-03

    A mechanical sensor with graphene porous network (GPN) combined with polydimethylsiloxane (PDMS) is demonstrated by the first time. Using the nickel foam as template and chemically etching method, the GPN can be created in the PDMS-nickel foam coated with graphene, which can achieve both pressure and strain sensing properties. Because of the pores in the GPN, the composite as pressure and strain sensor exhibit wide pressure sensing range and highest sensitivity among the graphene foam-based sensors, respectively. In addition, it shows potential applications in monitoring or even recognize the walking states, finger bending degree, and wrist blood pressure.

  5. A wearable RF sensor on fabric substrate for pulmonary edema monitoring

    KAUST Repository

    Tayyab, Muhammad; Sharawi, Mohammad S.; Shamim, Atif

    2017-01-01

    We propose a radio frequency (RF) sensor built on a fabric textile substrate for pulmonary edema monitoring. The 37-port RF sensor is designed and optimized to operate at 60 MHz with a low input power of 1 mW. By applying the least squares (LS) method, an equation was obtained for dielectric constant estimation using the transmission coefficient of each RF sensor port. The simulated errors are estimated for normal lung, edema and emphysema infected lung cases using a human chest model with an average error of 0.57%. Inkjet printing of the proposed design is then discussed.

  6. A wearable RF sensor on fabric substrate for pulmonary edema monitoring

    KAUST Repository

    Tayyab, Muhammad

    2017-11-30

    We propose a radio frequency (RF) sensor built on a fabric textile substrate for pulmonary edema monitoring. The 37-port RF sensor is designed and optimized to operate at 60 MHz with a low input power of 1 mW. By applying the least squares (LS) method, an equation was obtained for dielectric constant estimation using the transmission coefficient of each RF sensor port. The simulated errors are estimated for normal lung, edema and emphysema infected lung cases using a human chest model with an average error of 0.57%. Inkjet printing of the proposed design is then discussed.

  7. Wearable, wireless gas sensors using highly stretchable and transparent structures of nanowires and graphene

    Science.gov (United States)

    Park, Jihun; Kim, Joohee; Kim, Kukjoo; Kim, So-Yun; Cheong, Woon Hyung; Park, Kyeongmin; Song, Joo Hyeb; Namgoong, Gyeongho; Kim, Jae Joon; Heo, Jaeyeong; Bien, Franklin; Park, Jang-Ung

    2016-05-01

    Herein, we report the fabrication of a highly stretchable, transparent gas sensor based on silver nanowire-graphene hybrid nanostructures. Due to its superb mechanical and optical characteristics, the fabricated sensor demonstrates outstanding and stable performances even under extreme mechanical deformation (stable until 20% of strain). The integration of a Bluetooth system or an inductive antenna enables the wireless operation of the sensor. In addition, the mechanical robustness of the materials allows the device to be transferred onto various nonplanar substrates, including a watch, a bicycle light, and the leaves of live plants, thereby achieving next-generation sensing electronics for the `Internet of Things' area.Herein, we report the fabrication of a highly stretchable, transparent gas sensor based on silver nanowire-graphene hybrid nanostructures. Due to its superb mechanical and optical characteristics, the fabricated sensor demonstrates outstanding and stable performances even under extreme mechanical deformation (stable until 20% of strain). The integration of a Bluetooth system or an inductive antenna enables the wireless operation of the sensor. In addition, the mechanical robustness of the materials allows the device to be transferred onto various nonplanar substrates, including a watch, a bicycle light, and the leaves of live plants, thereby achieving next-generation sensing electronics for the `Internet of Things' area. Electronic supplementary information (ESI) available. See DOI: 10.1039/c6nr01468b

  8. Measurement and visualization of face-to-face interaction among community-dwelling older adults using wearable sensors.

    Science.gov (United States)

    Masumoto, Kouhei; Yaguchi, Takaharu; Matsuda, Hiroshi; Tani, Hideaki; Tozuka, Keisuke; Kondo, Narihiko; Okada, Shuichi

    2017-10-01

    A number of interventions have been undertaken to develop and promote social networks among community-dwelling older adults. However, it has been difficult to examine the effects of these interventions, because of problems in assessing interactions. The present study was designed to quantitatively measure and visualize face-to-face interactions among elderly participants in an exercise program. We also examined relationships among interactional variables, personality and interest in community involvement, including interactions with the local community. Older adults living in the same community were recruited to participate in an exercise program that consisted of four sessions. We collected data on face-to-face interactions of the participants by using a wearable sensor technology device. Network analysis identified the communication networks of participants in the exercise program, as well as changes in these networks. Additionally, there were significant correlations between the number of people involved in face-to-face interactions and changes in both interest in community involvement and interactions with local community residents, as well as personality traits, including agreeableness. Social networks in the community are essential for solving problems caused by the aging society. We showed the possible applications of face-to-face interactional data for identifying core participants having many interactions, and isolated participants having only a few interactions within the community. Such data would be useful for carrying out efficient interventions for increasing participants' involvement with their community. Geriatr Gerontol Int 2017; 17: 1752-1758. © 2017 Japan Geriatrics Society.

  9. Silver Nanowire Embedded Colorless Polyimide Heater for Wearable Chemical Sensors: Improved Reversible Reaction Kinetics of Optically Reduced Graphene Oxide.

    Science.gov (United States)

    Choi, Seon-Jin; Kim, Sang-Joon; Jang, Ji-Soo; Lee, Ji-Hyun; Kim, Il-Doo

    2016-09-14

    Optically reduced graphene oxide (ORGO) sheets are successfully integrated on silver nanowire (Ag NW)-embedded transparent and flexible substrate. As a heating element, Ag NWs are embedded in a colorless polyimide (CPI) film by covering Ag NW networks using polyamic acid and subsequent imidization. Graphene oxide dispersed aqueous solution is drop-coated on the Ag NW-embedded CPI (Ag NW-CPI) film and directly irradiated by intense pulsed light to obtain ORGO sheets. The heat generation property of Ag NW-CPI film is investigated by applying DC voltage, which demonstrates unprecedentedly reliable and stable characteristics even in dynamic bending condition. To demonstrate the potential application in wearable chemical sensors, NO 2 sensing characteristic of ORGO is investigated with respect to the different heating temperature (22.7-71.7 °C) of Ag NW-CPI film. The result reveals that the ORGO sheets exhibit high sensitivity of 2.69% with reversible response/recovery sensing properties and minimal deviation of baseline resistance of around 1% toward NO 2 molecules when the temperature of Ag NW-CPI film is 71.7 °C. This work first demonstrates the improved reversible NO 2 sensing properties of ORGO sheets on flexible and transparent Ag NW-CPI film assisted by Ag NW heating networks. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  10. The Role of Heart-Rate Variability Parameters in Activity Recognition and Energy-Expenditure Estimation Using Wearable Sensors.

    Science.gov (United States)

    Park, Heesu; Dong, Suh-Yeon; Lee, Miran; Youn, Inchan

    2017-07-24

    Human-activity recognition (HAR) and energy-expenditure (EE) estimation are major functions in the mobile healthcare system. Both functions have been investigated for a long time; however, several challenges remain unsolved, such as the confusion between activities and the recognition of energy-consuming activities involving little or no movement. To solve these problems, we propose a novel approach using an accelerometer and electrocardiogram (ECG). First, we collected a database of six activities (sitting, standing, walking, ascending, resting and running) of 13 voluntary participants. We compared the HAR performances of three models with respect to the input data type (with none, all, or some of the heart-rate variability (HRV) parameters). The best recognition performance was 96.35%, which was obtained with some selected HRV parameters. EE was also estimated for different choices of the input data type (with or without HRV parameters) and the model type (single and activity-specific). The best estimation performance was found in the case of the activity-specific model with HRV parameters. Our findings indicate that the use of human physiological data, obtained by wearable sensors, has a significant impact on both HAR and EE estimation, which are crucial functions in the mobile healthcare system.

  11. Motor Function Evaluation of Hemiplegic Upper-Extremities Using Data Fusion from Wearable Inertial and Surface EMG Sensors

    Directory of Open Access Journals (Sweden)

    Yanran Li

    2017-03-01

    Full Text Available Quantitative evaluation of motor function is of great demand for monitoring clinical outcome of applied interventions and further guiding the establishment of therapeutic protocol. This study proposes a novel framework for evaluating upper limb motor function based on data fusion from inertial measurement units (IMUs and surface electromyography (EMG sensors. With wearable sensors worn on the tested upper limbs, subjects were asked to perform eleven straightforward, specifically designed canonical upper-limb functional tasks. A series of machine learning algorithms were applied to the recorded motion data to produce evaluation indicators, which is able to reflect the level of upper-limb motor function abnormality. Sixteen healthy subjects and eighteen stroke subjects with substantial hemiparesis were recruited in the experiment. The combined IMU and EMG data yielded superior performance over the IMU data alone and the EMG data alone, in terms of decreased normal data variation rate (NDVR and improved determination coefficient (DC from a regression analysis between the derived indicator and routine clinical assessment score. Three common unsupervised learning algorithms achieved comparable performance with NDVR around 10% and strong DC around 0.85. By contrast, the use of a supervised algorithm was able to dramatically decrease the NDVR to 6.55%. With the proposed framework, all the produced indicators demonstrated high agreement with the routine clinical assessment scale, indicating their capability of assessing upper-limb motor functions. This study offers a feasible solution to motor function assessment in an objective and quantitative manner, especially suitable for home and community use.

  12. Assessing hopping developmental level in childhood using wearable inertial sensor devices.

    Science.gov (United States)

    Masci, Ilaria; Vannozzi, Giuseppe; Getchell, Nancy; Cappozzo, Aurelio

    2012-07-01

    Assessing movement skills is a fundamental issue in motor development. Current process-oriented assessments, such as developmental sequences, are based on subjective judgments; if paired with quantitative assessments, a better understanding of movement performance and developmental change could be obtained. Our purpose was to examine the use of inertial sensors to evaluate developmental differences in hopping over distance. Forty children executed the task wearing the inertial sensor and relevant time durations and 3D accelerations were obtained. Subjects were also categorized in different developmental levels according to the hopping developmental sequence. Results indicated that some time and kinematic parameters changed with some developmental levels, possibly as a function of anthropometry and previous motor experience. We concluded that, since inertial sensors were suitable in describing hopping performance and sensitive to developmental changes, this technology is promising as an in-field and user-independent motor development assessment tool.

  13. Development of a research prototype computer 'Wearables' that one can wear on his or her body. Minitsukeru computer 'Wearables' kenkyuyo shisakuki wo kaihatsu

    Energy Technology Data Exchange (ETDEWEB)

    1999-02-01

    Development has been made on a prototype of a wearable computer 'Wearables' that makes the present notebook type PC still smaller in size, can be worn on human body for utilization at any time and from anywhere, and aims at realizing a social infrastructure. Using the company's portable PC, Libretto as the base, the keyboard and the liquid crystal display panel were removed. To replace these functions, a voice inputting microphone, and various types of head mounting type displays (glasses type) mounted on a head to see images are connected. Provided as the means for information communication between the prototype computer and outside environments are infrared ray interface and data communication function using wireless (electric wave) communications. The wireless desk area network (DAN) technology that can structure dynamically a network between multiple number of computers has realized smooth communications with external environments. The voice recognition technology that can work efficiently against noise has realized keyboard-free operation that gives no neural stress to users. The 'wearable computer' aims at not only users utilizing it simply wearing it, but also providing a new perception ability that could not have been seen or heard directly to date, that is realizing the digital sensation. With the computer, a society will be structured in which people can live comfortably and safely, maintaining conversations between the users and the computers, and interactions between the surrounding environment and the social infrastructures, with protection of individual privacy and information security taken into consideration. The company is working with the Massachusetts Institute of Technology (MIT) for research and development of the 'wearable computer' as to how it can be utilized and basic technologies that will be required in the future. (translated by NEDO)

  14. Automatic event-based synchronization of multimodal data streams from wearable and ambient sensors

    NARCIS (Netherlands)

    Bannach, D.; Amft, O.D.; Lukowicz, P.; Barnaghi, P.; Moessner, K.; Presser, M.; Meissner, S.

    2009-01-01

    A major challenge in using multi-modal, distributed sensor systems for activity recognition is to maintain a temporal synchronization between individually recorded data streams. A common approach is to use well defined ‘synchronization actions’ performed by the user to generate, easily identifiable

  15. Toward real time detection of the basic living activity in home using a wearable sensor and smart home sensors.

    Science.gov (United States)

    Bang, Sunlee; Kim, Minho; Song, Sa-Kwang; Park, Soo-Jun

    2008-01-01

    As the elderly people living alone are enormously increasing recently, we need the system inferring activities of daily living (ADL) for maintaining healthy life and recognizing emergency. The system should be constructed with sensors, which are used to associate with people's living while remaining as non intrusive views as possible. To do this, the proposed system use a triaxial accelerometer sensor and environment sensors indicating contact with subject in home. Particularly, in order to robustly infer ADLs, we present component ADL, which is decided with conjunction of human motion together, not just only contacted object identification. It is an important component in inferring ADL. In special, component ADL decision firstly refines misclassified initial activities, which improves the accuracy of recognizing ADL. Preliminary experiments results for proposed system provides overall recognition rate of over 97% over 8 component ADLs, which can be effectively applicable to recognize the final ADLs.

  16. The Borg–eye and the We–I. The production of a collective living body through wearable computers

    NARCIS (Netherlands)

    Liberati, Nicola

    2018-01-01

    The aim of this work is to analyze the constitution of a new collective subject thanks to wearable computers. Wearable computers are emerging technologies which are supposed to become pervasively used in the near future. They are devices designed to be on us every single moment of our life and to

  17. A Novel Wireless Wearable Volatile Organic Compound (VOC Monitoring Device with Disposable Sensors

    Directory of Open Access Journals (Sweden)

    Yue Deng

    2016-12-01

    Full Text Available A novel portable wireless volatile organic compound (VOC monitoring device with disposable sensors is presented. The device is miniaturized, light, easy-to-use, and cost-effective. Different field tests have been carried out to identify the operational, analytical, and functional performance of the device and its sensors. The device was compared to a commercial photo-ionization detector, gas chromatography-mass spectrometry, and carbon monoxide detector. In addition, environmental operational conditions, such as barometric change, temperature change and wind conditions were also tested to evaluate the device performance. The multiple comparisons and tests indicate that the proposed VOC device is adequate to characterize personal exposure in many real-world scenarios and is applicable for personal daily use.

  18. Design and characterization of a wearable macrobending fiber optic sensor for human joint angle determination

    Science.gov (United States)

    Silva, Ana S.; Catarino, André; Correia, Miguel V.; Frazão, Orlando

    2013-12-01

    The work presented here describes the development and characterization of intensity fiber optic sensor integrated in a specifically designed piece of garment to measure elbow flexion. The sensing head is based on macrobending incorporated in the garment, and the increase of curvature number was studied in order to investigate which scheme provided a good result in terms of sensitivity and repeatability. Results showed the configuration that assured a higher sensitivity (0.644 dBm/deg) and better repeatability was the one with four loops. Ultimately, this sensor can be used for rehabilitation purposes to monitor human joint angles, namely, elbow flexion on stroke survivors while performing the reach functional task, which is the most common upper-limb human gesture.

  19. Quantifying social contacts in a household setting of rural Kenya using wearable proximity sensors

    OpenAIRE

    Kiti, Moses C.; Tizzoni, Michele; Kinyanjui, Timothy M.; Koech, Dorothy C.; Munywoki, Patrick K.; Meriac, Milosch; Cappa, Luca; Panisson, André; Barrat, Alain; Cattuto, Ciro; Nokes, D. James

    2016-01-01

    Close proximity interactions between individuals influence how infections spread. Quantifying close contacts in developing world settings, where such data is sparse yet disease burden is high, can provide insights into the design of intervention strategies such as vaccination. Recent technological advances have enabled collection of time-resolved face-to-face human contact data using radio frequency proximity sensors. The acceptability and practicalities of using proximity devices within the ...

  20. Extracting breathing rate information from a wearable reflectance pulse oximeter sensor.

    Science.gov (United States)

    Johnston, W S; Mendelson, Y

    2004-01-01

    The integration of multiple vital physiological measurements could help combat medics and field commanders to better predict a soldier's health condition and enhance their ability to perform remote triage procedures. In this paper we demonstrate the feasibility of extracting accurate breathing rate information from a photoplethysmographic signal that was recorded by a reflectance pulse oximeter sensor mounted on the forehead and subsequently processed by a simple time domain filtering and frequency domain Fourier analysis.

  1. inContexto: framework to obtain people context using wearable sensors and social network sites

    OpenAIRE

    Blázquez Gil, Gonzalo

    2016-01-01

    Mención Internacional en el título de doctor Ambient Intelligent (AmI) technology is developing fast and will promote a new generation of applications with some characteristics in the area of context awareness, anticipatory behavior, home security, monitoring, Health Care and video surveillance. AmI Environments should be surrounded by multiples sensors in order to discover people needs. These kind of scenarios are characterized by intelligent environments, which are able to recognize inco...

  2. NATURAL USER INTERFACE SENSORS FOR HUMAN BODY MEASUREMENT

    Directory of Open Access Journals (Sweden)

    J. Boehm

    2012-08-01

    Full Text Available The recent push for natural user interfaces (NUI in the entertainment and gaming industry has ushered in a new era of low cost three-dimensional sensors. While the basic idea of using a three-dimensional sensor for human gesture recognition dates some years back it is not until recently that such sensors became available on the mass market. The current market leader is PrimeSense who provide their technology for the Microsoft Xbox Kinect. Since these sensors are developed to detect and observe human users they should be ideally suited to measure the human body. We describe the technology of a line of NUI sensors and assess their performance in terms of repeatability and accuracy. We demonstrate the implementation of a prototype scanner integrating several NUI sensors to achieve full body coverage. We present the results of the obtained surface model of a human body.

  3. Natural User Interface Sensors for Human Body Measurement

    Science.gov (United States)

    Boehm, J.

    2012-08-01

    The recent push for natural user interfaces (NUI) in the entertainment and gaming industry has ushered in a new era of low cost three-dimensional sensors. While the basic idea of using a three-dimensional sensor for human gesture recognition dates some years back it is not until recently that such sensors became available on the mass market. The current market leader is PrimeSense who provide their technology for the Microsoft Xbox Kinect. Since these sensors are developed to detect and observe human users they should be ideally suited to measure the human body. We describe the technology of a line of NUI sensors and assess their performance in terms of repeatability and accuracy. We demonstrate the implementation of a prototype scanner integrating several NUI sensors to achieve full body coverage. We present the results of the obtained surface model of a human body.

  4. Monitoring of Vital Signs with Flexible and Wearable Medical Devices.

    Science.gov (United States)

    Khan, Yasser; Ostfeld, Aminy E; Lochner, Claire M; Pierre, Adrien; Arias, Ana C

    2016-06-01

    Advances in wireless technologies, low-power electronics, the internet of things, and in the domain of connected health are driving innovations in wearable medical devices at a tremendous pace. Wearable sensor systems composed of flexible and stretchable materials have the potential to better interface to the human skin, whereas silicon-based electronics are extremely efficient in sensor data processing and transmission. Therefore, flexible and stretchable sensors combined with low-power silicon-based electronics are a viable and efficient approach for medical monitoring. Flexible medical devices designed for monitoring human vital signs, such as body temperature, heart rate, respiration rate, blood pressure, pulse oxygenation, and blood glucose have applications in both fitness monitoring and medical diagnostics. As a review of the latest development in flexible and wearable human vitals sensors, the essential components required for vitals sensors are outlined and discussed here, including the reported sensor systems, sensing mechanisms, sensor fabrication, power, and data processing requirements. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. Wearable Sensors and the Assessment of Frailty among Vulnerable Older Adults: An Observational Cohort Study

    Directory of Open Access Journals (Sweden)

    Javad Razjouyan

    2018-04-01

    Full Text Available Background: The geriatric syndrome of frailty is one of the greatest challenges facing the U.S. aging population. Frailty in older adults is associated with higher adverse outcomes, such as mortality and hospitalization. Identifying precise early indicators of pre-frailty and measures of specific frailty components are of key importance to enable targeted interventions and remediation. We hypothesize that sensor-derived parameters, measured by a pendant accelerometer device in the home setting, are sensitive to identifying pre-frailty. Methods: Using the Fried frailty phenotype criteria, 153 community-dwelling, ambulatory older adults were classified as pre-frail (51%, frail (22%, or non-frail (27%. A pendant sensor was used to monitor the at home physical activity, using a chest acceleration over 48 h. An algorithm was developed to quantify physical activity pattern (PAP, physical activity behavior (PAB, and sleep quality parameters. Statistically significant parameters were selected to discriminate the pre-frail from frail and non-frail adults. Results: The stepping parameters, walking parameters, PAB parameters (sedentary and moderate-to-vigorous activity, and the combined parameters reached and area under the curve of 0.87, 0.85, 0.85, and 0.88, respectively, for identifying pre-frail adults. No sleep parameters discriminated the pre-frail from the rest of the adults. Conclusions: This study demonstrates that a pendant sensor can identify pre-frailty via daily home monitoring. These findings may open new opportunities in order to remotely measure and track frailty via telehealth technologies.

  6. Assessing locomotor skills development in childhood using wearable inertial sensor devices: the running paradigm.

    Science.gov (United States)

    Masci, Ilaria; Vannozzi, Giuseppe; Bergamini, Elena; Pesce, Caterina; Getchell, Nancy; Cappozzo, Aurelio

    2013-04-01

    Objective quantitative evaluation of motor skill development is of increasing importance to carefully drive physical exercise programs in childhood. Running is a fundamental motor skill humans adopt to accomplish locomotion, which is linked to physical activity levels, although the assessment is traditionally carried out using qualitative evaluation tests. The present study aimed at investigating the feasibility of using inertial sensors to quantify developmental differences in the running pattern of young children. Qualitative and quantitative assessment tools were adopted to identify a skill-sensitive set of biomechanical parameters for running and to further our understanding of the factors that determine progression to skilled running performance. Running performances of 54 children between the ages of 2 and 12 years were submitted to both qualitative and quantitative analysis, the former using sequences of developmental level, the latter estimating temporal and kinematic parameters from inertial sensor measurements. Discriminant analysis with running developmental level as dependent variable allowed to identify a set of temporal and kinematic parameters, within those obtained with the sensor, that best classified children into the qualitative developmental levels (accuracy higher than 67%). Multivariate analysis of variance with the quantitative parameters as dependent variables allowed to identify whether and which specific parameters or parameter subsets were differentially sensitive to specific transitions between contiguous developmental levels. The findings showed that different sets of temporal and kinematic parameters are able to tap all steps of the transitional process in running skill described through qualitative observation and can be prospectively used for applied diagnostic and sport training purposes. Copyright © 2012 Elsevier B.V. All rights reserved.

  7. [The development of a wearable pulse oximeter sensor and study of the calibration method].

    Science.gov (United States)

    Wu, Xiaoling; Cai, Guiyan

    2009-08-01

    The paper first analyses the principles of measurement of the two-wave oximeter and their limitations in technology. We propose to filter off motion interference from pulse oximeter signal using an algorithm based on the Beer-Lambert law that requires a three-wave probe (660 nm, 850 nm, and 940 nm). Based on the new algorithm, this paper describes the design principle of the circuitry and the software flowchart. Also, we study the calibration method of the pulse oximeter sensor and discuss the results in this paper.

  8. Faller Classification in Older Adults Using Wearable Sensors Based on Turn and Straight-Walking Accelerometer-Based Features.

    Science.gov (United States)

    Drover, Dylan; Howcroft, Jennifer; Kofman, Jonathan; Lemaire, Edward D

    2017-06-07

    Faller classification in elderly populations can facilitate preventative care before a fall occurs. A novel wearable-sensor based faller classification method for the elderly was developed using accelerometer-based features from straight walking and turns. Seventy-six older individuals (74.15 ± 7.0 years), categorized as prospective fallers and non-fallers, completed a six-minute walk test with accelerometers attached to their lower legs and pelvis. After segmenting straight and turn sections, cross validation tests were conducted on straight and turn walking features to assess classification performance. The best "classifier model-feature selector" combination used turn data, random forest classifier, and select-5-best feature selector (73.4% accuracy, 60.5% sensitivity, 82.0% specificity, and 0.44 Matthew's Correlation Coefficient (MCC)). Using only the most frequently occurring features, a feature subset (minimum of anterior-posterior ratio of even/odd harmonics for right shank, standard deviation (SD) of anterior left shank acceleration SD, SD of mean anterior left shank acceleration, maximum of medial-lateral first quartile of Fourier transform (FQFFT) for lower back, maximum of anterior-posterior FQFFT for lower back) achieved better classification results, with 77.3% accuracy, 66.1% sensitivity, 84.7% specificity, and 0.52 MCC score. All classification performance metrics improved when turn data was used for faller classification, compared to straight walking data. Combining turn and straight walking features decreased performance metrics compared to turn features for similar classifier model-feature selector combinations.

  9. Mobile voice health monitoring using a wearable accelerometer sensor and a smartphone platform.

    Science.gov (United States)

    Mehta, Daryush D; Zañartu, Matías; Feng, Shengran W; Cheyne, Harold A; Hillman, Robert E

    2012-11-01

    Many common voice disorders are chronic or recurring conditions that are likely to result from faulty and/or abusive patterns of vocal behavior, referred to generically as vocal hyperfunction. An ongoing goal in clinical voice assessment is the development and use of noninvasively derived measures to quantify and track the daily status of vocal hyperfunction so that the diagnosis and treatment of such behaviorally based voice disorders can be improved. This paper reports on the development of a new, versatile, and cost-effective clinical tool for mobile voice monitoring that acquires the high-bandwidth signal from an accelerometer sensor placed on the neck skin above the collarbone. Using a smartphone as the data acquisition platform, the prototype device provides a user-friendly interface for voice use monitoring, daily sensor calibration, and periodic alert capabilities. Pilot data are reported from three vocally normal speakers and three subjects with voice disorders to demonstrate the potential of the device to yield standard measures of fundamental frequency and sound pressure level and model-based glottal airflow properties. The smartphone-based platform enables future clinical studies for the identification of the best set of measures for differentiating between normal and hyperfunctional patterns of voice use.

  10. Quantitative demonstration of the efficacy of night-time apomorphine infusion to treat nocturnal hypokinesia in Parkinson's disease using wearable sensors.

    Science.gov (United States)

    Bhidayasiri, Roongroj; Sringean, Jirada; Anan, Chanawat; Boonpang, Kamolwan; Thanawattano, Chusak; Ray Chaudhuri, K

    2016-12-01

    Nocturnal hypokinesia/akinesia is one of the common night-time symptoms in patients with Parkinson's disease (PD), negatively affecting quality of life of patients and caregivers. The recognition of this problem and treatment options are limited in clinical practice. To evaluate the efficacy of nocturnal apomorphine infusion, using a wearable sensor, in patients who are already on daytime continuous subcutaneous apomorphine infusion and still suffer from nocturnal hypokinesia. Nocturnal parameters in 10 PD patients before and during nocturnal infusion were assessed over two nights at their homes, using a wearable sensor (trunk). Nocturnal parameters included number, velocity, acceleration, degree, and duration of rolling over, and number of times they got out of bed. Correlations with validated clinical rating scales were performed. Following nocturnal apomorphine infusion (34.8 ± 6.5 mg per night), there were significant improvements in the number of turns in bed (p = 0.027), turning velocity (p = 0.046), and the degree of turning (p = 0.028) in PD patients. Significant improvements of Modified Parkinson's Disease Sleep Scale (p = 0.005), the axial score of Unified Parkinson's Disease Rating Scale (p = 0.013), and Nocturnal Akinesia Dystonia and Cramp Scale (p = 0.014) were also observed. Our study was able to demonstrate quantitatively the efficacy of nocturnal apomorphine infusion in PD patients with nocturnal hypokinesia and demonstrated the feasibility of using wearable sensors to yield objective and quantifiable outcomes in a clinical trial setting. More studies are needed to determine the long-term efficacy of this treatment in a large prospective cohort of PD patients. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Activity Monitoring and Heart Rate Variability as Indicators of Fall Risk: Proof-of-Concept for Application of Wearable Sensors in the Acute Care Setting.

    Science.gov (United States)

    Razjouyan, Javad; Grewal, Gurtej Singh; Rishel, Cindy; Parthasarathy, Sairam; Mohler, Jane; Najafi, Bijan

    2017-07-01

    Growing concern for falls in acute care settings could be addressed with objective evaluation of fall risk. The current proof-of-concept study evaluated the feasibility of using a chest-worn sensor during hospitalization to determine fall risk. Physical activity and heart rate variability (HRV) of 31 volunteers admitted to a 29-bed adult inpatient unit were recorded using a single chest-worn sensor. Sensor data during the first 24-hour recording were analyzed. Participants were stratified using the Hendrich II fall risk assessment into high and low fall risk groups. Univariate analysis revealed age, daytime activity, nighttime side lying posture, and HRV were significantly different between groups. Results suggest feasibility of wearable technology to consciously monitor physical activity, sleep postures, and HRV as potential markers of fall risk in the acute care setting. Further study is warranted to confirm the results and examine the efficacy of the proposed wearable technology to manage falls in hospitals. [Journal of Gerontological Nursing, 43(7), 53-62.]. Copyright 2017, SLACK Incorporated.

  12. Evaluation of a 433 MHz Band Body Sensor Network for Biomedical Applications

    Science.gov (United States)

    Kim, Saim; Brendle, Christian; Lee, Hyun-Young; Walter, Marian; Gloeggler, Sigrid; Krueger, Stefan; Leonhardt, Steffen

    2013-01-01

    Body sensor networks (BSN) are an important research topic due to various advantages over conventional measurement equipment. One main advantage is the feasibility to deploy a BSN system for 24/7 health monitoring applications. The requirements for such an application are miniaturization of the network nodes and the use of wireless data transmission technologies to ensure wearability and ease of use. Therefore, the reliability of such a system depends on the quality of the wireless data transmission. At present, most BSNs use ZigBee or other IEEE 802.15.4 based transmission technologies. Here, we evaluated the performance of a wireless transmission system of a novel BSN for biomedical applications in the 433 MHz ISM band, called Integrated Posture and Activity NEtwork by Medit Aachen (IPANEMA) BSN. The 433 MHz ISM band is used mostly by implanted sensors and thus allows easy integration of such into the BSN. Multiple measurement scenarios have been assessed, including varying antenna orientations, transmission distances and the number of network participants. The mean packet loss rate (PLR) was 0.63% for a single slave, which is comparable to IEEE 802.15.4 BSNs in the proximity of Bluetooth or WiFi networks. Secondly, an enhanced version is evaluated during on-body measurements with five slaves. The mean PLR results show a comparable good performance for measurements on a treadmill (2.5%), an outdoor track (3.4%) and in a climate chamber (1.5%). PMID:23344383

  13. [Wireless Passive Body Sensor for Temperature Monitoring Using Near Field Communication Technology].

    Science.gov (United States)

    Shi, Bo; Zhang, Li; Zhang, Genxuan; Tsau, Young; Zhang, Sai; Li, Lei

    2017-01-01

    In this study, we designed a wireless body temperature sensor (WBTS) based on near field communication (NFC) technology. Just attaching the WBTS to a mobile phone with NFC function, the real-time body temperature of human subjects can be acquired by an application program without seperate power supply. The WBTS is mainly composed of a digital body temperature probe (d-BTP), a NFC unit and an antenna. The d-BTP acquires and processes body temperature data through a micro control er, and the NFC unit and antenna are used for wireless energy transmission and data communication between the mobile phone and WBTS. UART communication protocol is used in the communication between the d-BTP and NFC unit, and data compression technique is adopted for improving transmission efficiency and decreasing power loss. In tests, the error of WBTS is ±0.1 oC, in range of 32 oC to 42 oC. The WBTS has advantages of high accuracy, low power loss, strong anti-interference ability, dispensation with independent power supply etc., and it can be integrated into wearable apparatuses for temperature monitoring and health management.

  14. Development of an improved wearable device for core body temperature monitoring based on the dual heat flux principle.

    Science.gov (United States)

    Feng, Jingjie; Zhou, Congcong; He, Cheng; Li, Yuan; Ye, Xuesong

    2017-04-01

    In this paper, a miniaturized wearable core body temperature (CBT) monitoring system based on the dual heat flux (DHF) principle was developed. By interspersing calcium carbonate powder in PolyDimethylsiloxane (PDMS), a reformative heat transfer medium was produced to reduce the thermal equilibrium time. Besides, a least mean square (LMS) algorithm based active noise cancellation (ANC) method was adopted to diminish the impact of ambient temperature fluctuations. Theoretical analyses, finite element simulation, experiments on a hot plate and human volunteers were performed. The results showed that the proposed system had the advantages of small size, reduced initial time (~23.5 min), and good immunity to fluctuations of the air temperature. For the range of 37-41 °C on the hot plate, the error compared with a Fluke high accuracy thermometer was 0.08  ±  0.20 °C. In the human experiments, the measured temperature in the rest trial (34 subjects) had a difference of 0.13  ±  0.22 °C compared with sublingual temperature, while a significant increase of 1.36  ±  0.44 °C from rest to jogging was found in the exercise trial (30 subjects). This system has the potential for reliable continuous CBT measurement in rest and can reflect CBT variations during exercise.

  15. Two Proximal Skin Electrodes — A Respiration Rate Body Sensor

    Directory of Open Access Journals (Sweden)

    Viktor Avbelj

    2012-10-01

    Full Text Available We propose a new body sensor for extracting the respiration rate based on the amplitude changes in the body surface potential differences between two proximal body electrodes. The sensor could be designed as a plaster-like reusable unit that can be easily fixed onto the surface of the body. It could be equipped either with a sufficiently large memory for storing the measured data or with a low-power radio system that can transmit the measured data to a gateway for further processing. We explore the influence of the sensor’s position on the quality of the extracted results using multi-channel ECG measurements and considering all the pairs of two neighboring electrodes as potential respiration-rate sensors. The analysis of the clinical measurements, which also include reference thermistor-based respiration signals, shows that the proposed approach is a viable option for monitoring the respiration frequency and for a rough classification of breathing types. The obtained results were evaluated on a wireless prototype of a respiration body sensor. We indicate the best positions for the respiration body sensor and prove that a single sensor for body surface potential difference on proximal skin electrodes can be used for combined measurements of respiratory and cardiac activities.

  16. Early Improper Motion Detection in Golf Swings Using Wearable Motion Sensors: The First Approach

    Science.gov (United States)

    Stančin, Sara; Tomažič, Sašo

    2013-01-01

    This paper presents an analysis of a golf swing to detect improper motion in the early phase of the swing. Led by the desire to achieve a consistent shot outcome, a particular golfer would (in multiple trials) prefer to perform completely identical golf swings. In reality, some deviations from the desired motion are always present due to the comprehensive nature of the swing motion. Swing motion deviations that are not detrimental to performance are acceptable. This analysis is conducted using a golfer's leading arm kinematic data, which are obtained from a golfer wearing a motion sensor that is comprised of gyroscopes and accelerometers. Applying the principal component analysis (PCA) to the reference observations of properly performed swings, the PCA components of acceptable swing motion deviations are established. Using these components, the motion deviations in the observations of other swings are examined. Any unacceptable deviations that are detected indicate an improper swing motion. Arbitrarily long observations of an individual player's swing sequences can be included in the analysis. The results obtained for the considered example show an improper swing motion in early phase of the swing, i.e., the first part of the backswing. An early detection method for improper swing motions that is conducted on an individual basis provides assistance for performance improvement. PMID:23752563

  17. Wearable Computing in E-education

    Directory of Open Access Journals (Sweden)

    Aleksandra Labus

    2015-03-01

    Full Text Available Emerging technologies such as mobile computing, sensors and sensor networks, and augmented reality have led to innovations in the field of wearable computing. Devices such as smart watches and smart glasses allow users to interact with devices worn under, with, or on top of clothing. This paper analyzes the possibilities of application of wearable computing in e-education. The focus is on integration of wearables into e-learning systems, in order to support ubiquitous learning, interaction and collaborative work. We present a model for integration of wearable technology in an e-education system and discuss technical, pedagogical and social aspects.

  18. The promise of mHealth: daily activity monitoring and outcome assessments by wearable sensors.

    Science.gov (United States)

    Dobkin, Bruce H; Dorsch, Andrew

    2011-01-01

    Mobile health tools that enable clinicians and researchers to monitor the type, quantity, and quality of everyday activities of patients and trial participants have long been needed to improve daily care, design more clinically meaningful randomized trials of interventions, and establish cost-effective, evidence-based practices. Inexpensive, unobtrusive wireless sensors, including accelerometers, gyroscopes, and pressure-sensitive textiles, combined with Internet-based communications and machine-learning algorithms trained to recognize upper- and lower-extremity movements, have begun to fulfill this need. Continuous data from ankle triaxial accelerometers, for example, can be transmitted from the home and community via WiFi or a smartphone to a remote data analysis server. Reports can include the walking speed and duration of every bout of ambulation, spatiotemporal symmetries between the legs, and the type, duration, and energy used during exercise. For daily care, this readily accessible flow of real-world information allows clinicians to monitor the amount and quality of exercise for risk factor management and compliance in the practice of skills. Feedback may motivate better self-management as well as serve home-based rehabilitation efforts. Monitoring patients with chronic diseases and after hospitalization or the start of new medications for a decline in daily activity may help detect medical complications before rehospitalization becomes necessary. For clinical trials, repeated laboratory-quality assessments of key activities in the community, rather than by clinic testing, self-report, and ordinal scales, may reduce the cost and burden of travel, improve recruitment and retention, and capture more reliable, valid, and responsive ratio-scaled outcome measures that are not mere surrogates for changes in daily impairment, disability, and functioning.

  19. Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study.

    Science.gov (United States)

    Sano, Akane; Taylor, Sara; McHill, Andrew W; Phillips, Andrew Jk; Barger, Laura K; Klerman, Elizabeth; Picard, Rosalind

    2018-06-08

    Wearable and mobile devices that capture multimodal data have the potential to identify risk factors for high stress and poor mental health and to provide information to improve health and well-being. We developed new tools that provide objective physiological and behavioral measures using wearable sensors and mobile phones, together with methods that improve their data integrity. The aim of this study was to examine, using machine learning, how accurately these measures could identify conditions of self-reported high stress and poor mental health and which of the underlying modalities and measures were most accurate in identifying those conditions. We designed and conducted the 1-month SNAPSHOT study that investigated how daily behaviors and social networks influence self-reported stress, mood, and other health or well-being-related factors. We collected over 145,000 hours of data from 201 college students (age: 18-25 years, male:female=1.8:1) at one university, all recruited within self-identified social groups. Each student filled out standardized pre- and postquestionnaires on stress and mental health; during the month, each student completed twice-daily electronic diaries (e-diaries), wore two wrist-based sensors that recorded continuous physical activity and autonomic physiology, and installed an app on their mobile phone that recorded phone usage and geolocation patterns. We developed tools to make data collection more efficient, including data-check systems for sensor and mobile phone data and an e-diary administrative module for study investigators to locate possible errors in the e-diaries and communicate with participants to correct their entries promptly, which reduced the time taken to clean e-diary data by 69%. We constructed features and applied machine learning to the multimodal data to identify factors associated with self-reported poststudy stress and mental health, including behaviors that can be possibly modified by the individual to improve

  20. Ultrasensitive and Highly Stable Resistive Pressure Sensors with Biomaterial-Incorporated Interfacial Layers for Wearable Health-Monitoring and Human-Machine Interfaces.

    Science.gov (United States)

    Chang, Hochan; Kim, Sungwoong; Jin, Sumin; Lee, Seung-Woo; Yang, Gil-Tae; Lee, Ki-Young; Yi, Hyunjung

    2018-01-10

    Flexible piezoresistive sensors have huge potential for health monitoring, human-machine interfaces, prosthetic limbs, and intelligent robotics. A variety of nanomaterials and structural schemes have been proposed for realizing ultrasensitive flexible piezoresistive sensors. However, despite the success of recent efforts, high sensitivity within narrower pressure ranges and/or the challenging adhesion and stability issues still potentially limit their broad applications. Herein, we introduce a biomaterial-based scheme for the development of flexible pressure sensors that are ultrasensitive (resistance change by 5 orders) over a broad pressure range of 0.1-100 kPa, promptly responsive (20 ms), and yet highly stable. We show that employing biomaterial-incorporated conductive networks of single-walled carbon nanotubes as interfacial layers of contact-based resistive pressure sensors significantly enhances piezoresistive response via effective modulation of the interlayer resistance and provides stable interfaces for the pressure sensors. The developed flexible sensor is capable of real-time monitoring of wrist pulse waves under external medium pressure levels and providing pressure profiles applied by a thumb and a forefinger during object manipulation at a low voltage (1 V) and power consumption (<12 μW). This work provides a new insight into the material candidates and approaches for the development of wearable health-monitoring and human-machine interfaces.

  1. Significant Change Spotting for Periodic Human Motion Segmentation of Cleaning Tasks Using Wearable Sensors

    Directory of Open Access Journals (Sweden)

    Kai-Chun Liu

    2017-01-01

    Full Text Available The proportion of the aging population is rapidly increasing around the world, which will cause stress on society and healthcare systems. In recent years, advances in technology have created new opportunities for automatic activities of daily living (ADL monitoring to improve the quality of life and provide adequate medical service for the elderly. Such automatic ADL monitoring requires reliable ADL information on a fine-grained level, especially for the status of interaction between body gestures and the environment in the real-world. In this work, we propose a significant change spotting mechanism for periodic human motion segmentation during cleaning task performance. A novel approach is proposed based on the search for a significant change of gestures, which can manage critical technical issues in activity recognition, such as continuous data segmentation, individual variance, and category ambiguity. Three typical machine learning classification algorithms are utilized for the identification of the significant change candidate, including a Support Vector Machine (SVM, k-Nearest Neighbors (kNN, and Naive Bayesian (NB algorithm. Overall, the proposed approach achieves 96.41% in the F1-score by using the SVM classifier. The results show that the proposed approach can fulfill the requirement of fine-grained human motion segmentation for automatic ADL monitoring.

  2. Strategies for Optimal MAC Parameters Tuning in IEEE 802.15.6 Wearable Wireless Sensor Networks.

    Science.gov (United States)

    Alam, Muhammad Mahtab; Ben Hamida, Elyes

    2015-09-01

    Wireless body area networks (WBAN) has penetrated immensely in revolutionizing the classical heath-care system. Recently, number of WBAN applications has emerged which introduce potential limits to existing solutions. In particular, IEEE 802.15.6 standard has provided great flexibility, provisions and capabilities to deal emerging applications. In this paper, we investigate the application-specific throughput analysis by fine-tuning the physical (PHY) and medium access control (MAC) parameters of the IEEE 802.15.6 standard. Based on PHY characterizations in narrow band, at the MAC layer, carrier sense multiple access collision avoidance (CSMA/CA) and scheduled access protocols are extensively analyzed. It is concluded that, IEEE 802.15.6 standard can satisfy most of the WBANs applications throughput requirements by maximum achieving 680 Kbps. However, those emerging applications which require high quality audio or video transmissions, standard is not able to meet their constraints. Moreover, delay, energy efficiency and successful packet reception are considered as key performance metrics for comparing the MAC protocols. CSMA/CA protocol provides the best results to meet the delay constraints of medical and non-medical WBAN applications. Whereas, the scheduled access approach, performs very well both in energy efficiency and packet reception ratio.

  3. Wearable physiological systems and technologies for metabolic monitoring.

    Science.gov (United States)

    Gao, Wei; Brooks, George A; Klonoff, David C

    2018-03-01

    Wearable sensors allow continuous monitoring of metabolites for diabetes, sports medicine, exercise science, and physiology research. These sensors can continuously detect target analytes in skin interstitial fluid (ISF), tears, saliva, and sweat. In this review, we will summarize developments on wearable devices and their potential applications in research, clinical practice, and recreational and sporting activities. Sampling skin ISF can require insertion of a needle into the skin, whereas sweat, tears, and saliva can be sampled by devices worn outside the body. The most widely sampled metabolite from a wearable device is glucose in skin ISF for monitoring diabetes patients. Continuous ISF glucose monitoring allows estimation of the glucose concentration in blood without the pain, inconvenience, and blood waste of fingerstick capillary blood glucose testing. This tool is currently used by diabetes patients to provide information for dosing insulin and determining a diet and exercise plan. Similar technologies for measuring concentrations of other analytes in skin ISF could be used to monitor athletes, emergency responders, warfighters, and others in states of extreme physiological stress. Sweat is a potentially useful substrate for sampling analytes for metabolic monitoring during exercise. Lactate, sodium, potassium, and hydrogen ions can be measured in sweat. Tools for converting the concentrations of these analytes sampled from sweat, tears, and saliva into blood concentrations are being developed. As an understanding of the relationships between the concentrations of analytes in blood and easily sampled body fluid increases, then the benefits of new wearable devices for metabolic monitoring will also increase.

  4. Sustainable Wearables: Wearable Technology for Enhancing the Quality of Human Life

    Directory of Open Access Journals (Sweden)

    Jaewoon Lee

    2016-05-01

    Full Text Available This paper aims to elicit insights about sustainable wearables by investigating recent advancements in wearable technology and their applications. Wearable technology has advanced considerably from a technical perspective, but it has stagnated due to barriers without penetrating wider society despite early positive expectations. This situation is the motivation behind the focus on studies by many research groups in recent years into wearable applications that can provide the best value from a human-oriented perspective. The expectation is that a new means to resolve the issue can be found from a viewpoint of sustainability; this is the main point of this paper. This paper first focuses on the trend of wearable technology like bodily status monitoring, multi-wearable device control, and smart networking between wearable sensors. Second, the development intention of such technology is investigated. Finally, this paper discusses about the applications of current wearable technology from the sustainable perspective, rather than detailed description of the component technologies employed in wearables. In this paper, the definition of sustainable wearables is discussed in the context of improving the quality of individual life, social impact, and social public interest; those wearable applications include the areas of wellness, healthcare, assistance for the visually impaired, disaster relief, and public safety. In the future, wearables will not be simple data trackers or fun accessories but will gain extended objectives and meanings that play a valuable role for individuals and societies. Successful and sustainable wearables will lead to positive changes for both individuals and societies overall.

  5. Developing Accessibility Design Guidelines for Wearables: Accessibility Standards for Multimodal Wearable Devices

    NARCIS (Netherlands)

    Wentzel, M.J.; Velleman, Eric M.; van der Geest, Thea; Antona, Margherita; Stephanidis, Constantine

    2016-01-01

    Smart wearable devices are integrated our everyday lives. Such wearable technology is worn on or near the body, while leaving both hands free. This enables users to receive and send information in a non-obtrusive way. Because of the ability to continuously assist and support activities, wearables

  6. Real-Time Classification of Patients with Balance Disorders vs. Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor

    Directory of Open Access Journals (Sweden)

    Bhargava Teja Nukala

    2016-11-01

    Full Text Available Gait analysis using wearable wireless sensors can be an economical, convenient and effective way to provide diagnostic and clinical information for various health-related issues. In this work, our custom designed low-cost wireless gait analysis sensor that contains a basic inertial measurement unit (IMU was used to collect the gait data for four patients diagnosed with balance disorders and additionally three normal subjects, each performing the Dynamic Gait Index (DGI tests while wearing the custom wireless gait analysis sensor (WGAS. The small WGAS includes a tri-axial accelerometer integrated circuit (IC, two gyroscopes ICs and a Texas Instruments (TI MSP430 microcontroller and is worn by each subject at the T4 position during the DGI tests. The raw gait data are wirelessly transmitted from the WGAS to a near-by PC for real-time gait data collection and analysis. In order to perform successful classification of patients vs. normal subjects, we used several different classification algorithms, such as the back propagation artificial neural network (BP-ANN, support vector machine (SVM, k-nearest neighbors (KNN and binary decision trees (BDT, based on features extracted from the raw gait data of the gyroscopes and accelerometers. When the range was used as the input feature, the overall classification accuracy obtained is 100% with BP-ANN, 98% with SVM, 96% with KNN and 94% using BDT. Similar high classification accuracy results were also achieved when the standard deviation or other values were used as input features to these classifiers. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real time using various classifiers, the success of which may eventually lead to accurate and objective diagnosis of abnormal human gaits and their underlying etiologies in the future, as more patient data are being collected.

  7. Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors

    Science.gov (United States)

    2012-01-01

    A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel; however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering. PMID:22284235

  8. Biosignal and context monitoring: Distributed multimedia applications of body area networks in healthcare

    NARCIS (Netherlands)

    Jones, Valerie M.; Huis in 't Veld, M.H.A.; Tonis, T.; Tönis, Thijs; Bults, Richard G.A.; van Beijnum, Bernhard J.F.; Widya, I.A.; Vollenbroek-Hutten, Miriam Marie Rosé; Hermens, Hermanus J.

    2008-01-01

    We are investigating the use of Body Area Networks (BANs), wearable sensors and wireless communications for measuring, processing, transmission, interpretation and display of biosignals. The goal is to provide telemonitoring and teletreatment services for patients. The remote health professional can

  9. Using Wearable Sensors and Machine Learning Models to Separate Functional Upper Extremity Use From Walking-Associated Arm Movements.

    Science.gov (United States)

    McLeod, Adam; Bochniewicz, Elaine M; Lum, Peter S; Holley, Rahsaan J; Emmer, Geoff; Dromerick, Alexander W

    2016-02-01

    To improve measurement of upper extremity (UE) use in the community by evaluating the feasibility of using body-worn sensor data and machine learning models to distinguish productive prehensile and bimanual UE activity use from extraneous movements associated with walking. Comparison of machine learning classification models with criterion standard of manually scored videos of performance in UE prosthesis users. Rehabilitation hospital training apartment. Convenience sample of UE prosthesis users (n=5) and controls (n=13) similar in age and hand dominance (N=18). Participants were filmed executing a series of functional activities; a trained observer annotated each frame to indicate either UE movement directed at functional activity or walking. Synchronized data from an inertial sensor attached to the dominant wrist were similarly classified as indicating either a functional use or walking. These data were used to train 3 classification models to predict the functional versus walking state given the associated sensor information. Models were trained over 4 trials: on UE amputees and controls and both within subject and across subject. Model performance was also examined with and without preprocessing (centering) in the across-subject trials. Percent correct classification. With the exception of the amputee/across-subject trial, at least 1 model classified >95% of test data correctly for all trial types. The top performer in the amputee/across-subject trial classified 85% of test examples correctly. We have demonstrated that computationally lightweight classification models can use inertial data collected from wrist-worn sensors to reliably distinguish prosthetic UE movements during functional use from walking-associated movement. This approach has promise in objectively measuring real-world UE use of prosthetic limbs and may be helpful in clinical trials and in measuring response to treatment of other UE pathologies. Copyright © 2016 American Congress of

  10. Wearable Multi-Frequency and Multi-Segment Bioelectrical Impedance Spectroscopy for Unobtrusively Tracking Body Fluid Shifts during Physical Activity in Real-Field Applications: A Preliminary Study

    Directory of Open Access Journals (Sweden)

    Federica Villa

    2016-05-01

    Full Text Available Bioelectrical Impedance Spectroscopy (BIS allows assessing the composition of body districts noninvasively and quickly, potentially providing important physiological/clinical information. However, neither portable commercial instruments nor more advanced wearable prototypes simultaneously satisfy the demanding needs of unobtrusively tracking body fluid shifts in different segments simultaneously, over a broad frequency range, for long periods and with high measurements rate. These needs are often required to evaluate exercise tests in sports or rehabilitation medicine, or to assess gravitational stresses in aerospace medicine. Therefore, the aim of this work is to present a new wearable prototype for monitoring multi-segment and multi-frequency BIS unobtrusively over long periods. Our prototype guarantees low weight, small size and low power consumption. An analog board with current-injecting and voltage-sensing electrodes across three body segments interfaces a digital board that generates square-wave current stimuli and computes impedance at 10 frequencies from 1 to 796 kHz. To evaluate the information derivable from our device, we monitored the BIS of three body segments in a volunteer before, during and after physical exercise and postural shift. We show that it can describe the dynamics of exercise-induced changes and the effect of a sit-to-stand maneuver in active and inactive muscular districts separately and simultaneously.

  11. Design of flexible thermoelectric generator as human body sensor

    DEFF Research Database (Denmark)

    Qing, Shaowei; Rezaniakolaei, Alireza; Rosendahl, Lasse Aistrup

    2018-01-01

    Flexible thermoelectric generator (TEG) became an attractive technology that has been widely used especially for curved surfaces applications. This study aims an optimal design of a flexible TEG for human body application. The flexible TEG is part of a sensor and supplies required electrical power...... for data transmission by the sensor. The TEG module includes ink based thermoelements made of nano-carbon bismuth telluride materials. One flexible fin conducts the body heat to the TEG module and there are two fins that exchange the heat from the cold side of the TEG to the ambient. The proposed design...

  12. CCS_WHMS: A Congestion Control Scheme for Wearable Health Management System.

    Science.gov (United States)

    Kafi, Mohamed Amine; Ben Othman, Jalel; Bagaa, Miloud; Badache, Nadjib

    2015-12-01

    Wearable computing is becoming a more and more attracting field in the last years thanks to the miniaturisation of electronic devices. Wearable healthcare monitoring systems (WHMS) as an important client of wearable computing technology has gained a lot. Indeed, the wearable sensors and their surrounding healthcare applications bring a lot of benefits to patients, elderly people and medical staff, so facilitating their daily life quality. But from a research point of view, there is still work to accomplish in order to overcome the gap between hardware and software parts. In this paper, we target the problem of congestion control when all these healthcare sensed data have to reach the destination in a reliable manner that avoids repetitive transmission which wastes precious energy or leads to loss of important information in emergency cases, too. We propose a congestion control scheme CCS_WHMS that ensures efficient and fair data delivery while used in the body wearable system part or in the multi-hop inter bodies wearable ones to get the destination. As the congestion detection paradigm is very important in the control process, we do experimental tests to compare between state of the art congestion detection methods, using MICAz motes, in order to choose the appropriate one for our scheme.

  13. Wearable Performance Devices in Sports Medicine.

    Science.gov (United States)

    Li, Ryan T; Kling, Scott R; Salata, Michael J; Cupp, Sean A; Sheehan, Joseph; Voos, James E

    2016-01-01

    Wearable performance devices and sensors are becoming more readily available to the general population and athletic teams. Advances in technology have allowed individual endurance athletes, sports teams, and physicians to monitor functional movements, workloads, and biometric markers to maximize performance and minimize injury. Movement sensors include pedometers, accelerometers/gyroscopes, and global positioning satellite (GPS) devices. Physiologic sensors include heart rate monitors, sleep monitors, temperature sensors, and integrated sensors. The purpose of this review is to familiarize health care professionals and team physicians with the various available types of wearable sensors, discuss their current utilization, and present future applications in sports medicine. Data were obtained from peer-reviewed literature through a search of the PubMed database. Included studies searched development, outcomes, and validation of wearable performance devices such as GPS, accelerometers, and physiologic monitors in sports. Clinical review. Level 4. Wearable sensors provide a method of monitoring real-time physiologic and movement parameters during training and competitive sports. These parameters can be used to detect position-specific patterns in movement, design more efficient sports-specific training programs for performance optimization, and screen for potential causes of injury. More recent advances in movement sensors have improved accuracy in detecting high-acceleration movements during competitive sports. Wearable devices are valuable instruments for the improvement of sports performance. Evidence for use of these devices in professional sports is still limited. Future developments are needed to establish training protocols using data from wearable devices. © 2015 The Author(s).

  14. Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0

    Directory of Open Access Journals (Sweden)

    Melanie Swan

    2012-11-01

    Full Text Available The number of devices on the Internet exceeded the number of people on the Internet in 2008, and is estimated to reach 50 billion in 2020. A wide-ranging Internet of Things (IOT ecosystem is emerging to support the process of connecting real-world objects like buildings, roads, household appliances, and human bodies to the Internet via sensors and microprocessor chips that record and transmit data such as sound waves, temperature, movement, and other variables. The explosion in Internet-connected sensors means that new classes of technical capability and application are being created. More granular 24/7 quantified monitoring is leading to a deeper understanding of the internal and external worlds encountered by humans. New data literacy behaviors such as correlation assessment, anomaly detection, and high-frequency data processing are developing as humans adapt to the different kinds of data flows enabled by the IOT. The IOT ecosystem has four critical functional steps: data creation, information generation, meaning-making, and action-taking. This paper provides a comprehensive review of the current and rapidly emerging ecosystem of the Internet of Things (IOT.

  15. On PHY and MAC Performance in Body Sensor Networks

    Directory of Open Access Journals (Sweden)

    Higgins Henry

    2009-01-01

    Full Text Available Abstract This paper presents an empirical investigation on the performance of body implant communication using radio frequency (RF technology. In body implant communication, the electrical properties of the body influence the signal propagation in several ways. We use a Perspex body model (30 cm diameter, 80 cm height and 0.5 cm thickness filled with a liquid that mimics the electrical properties of the basic body tissues. This model is used to observe the effects of body tissue on the RF communication. We observe best performance at 3cm depth inside the liquid. We further present a simulation study of several low-power MAC protocols for an on-body sensor network and discuss the derived results. Also, the traditional preamble-based TMDA protocol is extended towards a beacon-based TDMA protocol in order to avoid preamble collision and to ensure low-power communication.

  16. On PHY and MAC Performance in Body Sensor Networks

    Directory of Open Access Journals (Sweden)

    Sana Ullah

    2009-01-01

    Full Text Available This paper presents an empirical investigation on the performance of body implant communication using radio frequency (RF technology. In body implant communication, the electrical properties of the body influence the signal propagation in several ways. We use a Perspex body model (30 cm diameter, 80 cm height and 0.5 cm thickness filled with a liquid that mimics the electrical properties of the basic body tissues. This model is used to observe the effects of body tissue on the RF communication. We observe best performance at 3cm depth inside the liquid. We further present a simulation study of several low-power MAC protocols for an on-body sensor network and discuss the derived results. Also, the traditional preamble-based TMDA protocol is extended towards a beacon-based TDMA protocol in order to avoid preamble collision and to ensure low-power communication.

  17. Understanding the role of contrasting urban contexts in healthy aging: an international cohort study using wearable sensor devices (the CURHA study protocol).

    Science.gov (United States)

    Kestens, Yan; Chaix, Basile; Gerber, Philippe; Desprès, Michel; Gauvin, Lise; Klein, Olivier; Klein, Sylvain; Köppen, Bernhard; Lord, Sébastien; Naud, Alexandre; Payette, Hélène; Richard, Lucie; Rondier, Pierre; Shareck, Martine; Sueur, Cédric; Thierry, Benoit; Vallée, Julie; Wasfi, Rania

    2016-05-05

    Given the challenges of aging populations, calls have been issued for more sustainable urban re-development and implementation of local solutions to address global environmental and healthy aging issues. However, few studies have considered older adults' daily mobility to better understand how local built and social environments may contribute to healthy aging. Meanwhile, wearable sensors and interactive map-based applications offer novel means for gathering information on people's mobility, levels of physical activity, or social network structure. Combining such data with classical questionnaires on well-being, physical activity, perceived environments and qualitative assessment of experience of places opens new opportunities to assess the complex interplay between individuals and environments. In line with current gaps and novel analytical capabilities, this research proposes an international research agenda to collect and analyse detailed data on daily mobility, social networks and health outcomes among older adults using interactive web-based questionnaires and wearable sensors. Our study resorts to a battery of innovative data collection methods including use of a novel multisensor device for collection of location and physical activity, interactive map-based questionnaires on regular destinations and social networks, and qualitative assessment of experience of places. This rich data will allow advanced quantitative and qualitative analyses in the aim to disentangle the complex people-environment interactions linking urban local contexts to healthy aging, with a focus on active living, social networks and participation, and well-being. This project will generate evidence about what characteristics of urban environments relate to active mobility, social participation, and well-being, three important dimensions of healthy aging. It also sets the basis for an international research agenda on built environment and healthy aging based on a shared and comprehensive

  18. The application of wearable technology in surgery: ensuring the positive impact of the wearable revolution on surgical patients.

    Science.gov (United States)

    Slade Shantz, Jesse Alan; Veillette, Christian J H

    2014-01-01

    Wearable technology has become an important trend in consumer electronics in the past year. The miniaturization and mass production of myriad sensors have made possible the integration of sensors and output devices in wearable platforms. Despite the consumer focus of the wearable revolution some surgical applications are being developed. These fall into augmentative, assistive, and assessment functions and primarily layer onto current surgical workflows. Some challenges to the adoption of wearable technologies are discussed and a conceptual framework for understanding the potential of wearable technology to revolutionize surgical practice are presented.

  19. Tablet PC Enabled Body Sensor System for Rural Telehealth Applications

    Directory of Open Access Journals (Sweden)

    Nitha V. Panicker

    2016-01-01

    Full Text Available Telehealth systems benefit from the rapid growth of mobile communication technology for measuring physiological signals. Development and validation of a tablet PC enabled noninvasive body sensor system for rural telehealth application are discussed in this paper. This system includes real time continuous collection of physiological parameters (blood pressure, pulse rate, and temperature and fall detection of a patient with the help of a body sensor unit and wireless transmission of the acquired information to a tablet PC handled by the medical staff in a Primary Health Center (PHC. Abnormal conditions are automatically identified and alert messages are given to the medical officer in real time. Clinical validation is performed in a real environment and found to be successful. Bland-Altman analysis is carried out to validate the wrist blood pressure sensor used. The system works well for all measurements.

  20. Software authentication to enhance trust in body sensor networks

    NARCIS (Netherlands)

    Groot, de J.A.; Bui, T.V.; Linnartz, J.P.M.G.; Lukkien, J.J.; Verhoeven, R.; Jøsang, A.; Samarati, P.; Petrocchi, M.

    2013-01-01

    This paper studies an approach to enhance the trust in the widespread use of Body Sensor Networks (BSN) in Healthcare. To address the wide variety in medical indications and differences between patients, we assume that such BSNs are programmable and highly flexible in their functionality. Yet this

  1. Fall detection with body-worn sensors : A systematic review

    NARCIS (Netherlands)

    Schwickert, L.; Becker, C.; Lindemann, U.; Marechal, C.; Bourke, A.; Chiari, L.; Helbostad, J. L.; Zijlstra, Wiebren; Aminian, K.; Todd, C.; Bandinelli, S.; Klenk, J.

    2013-01-01

    Background and aims. Falls among older people remain a major public health challenge. Body-worn sensors are needed to improve the understanding of the underlying mechanisms and kinematics of falls. The aim of this systematic review is to assemble, extract and critically discuss the information

  2. Body/bone-marrow differential-temperature sensor

    Science.gov (United States)

    Anselmo, V. J.; Berdahl, C. M.

    1978-01-01

    Differential-temperature sensor developed to compare bone-marrow and body temperature in leukemia patients uses single stable amplifier to monitor temperature difference recorded by thermocouples. Errors are reduced by referencing temperatures to each other, not to separate calibration points.

  3. Scalable fabric tactile sensor arrays for soft bodies

    Science.gov (United States)

    Day, Nathan; Penaloza, Jimmy; Santos, Veronica J.; Killpack, Marc D.

    2018-06-01

    Soft robots have the potential to transform the way robots interact with their environment. This is due to their low inertia and inherent ability to more safely interact with the world without damaging themselves or the people around them. However, existing sensing for soft robots has at least partially limited their ability to control interactions with their environment. Tactile sensors could enable soft robots to sense interaction, but most tactile sensors are made from rigid substrates and are not well suited to applications for soft robots which can deform. In addition, the benefit of being able to cheaply manufacture soft robots may be lost if the tactile sensors that cover them are expensive and their resolution does not scale well for manufacturability. This paper discusses the development of a method to make affordable, high-resolution, tactile sensor arrays (manufactured in rows and columns) that can be used for sensorizing soft robots and other soft bodies. However, the construction results in a sensor array that exhibits significant amounts of cross-talk when two taxels in the same row are compressed. Using the same fabric-based tactile sensor array construction design, two different methods for cross-talk compensation are presented. The first uses a mathematical model to calculate a change in resistance of each taxel directly. The second method introduces additional simple circuit components that enable us to isolate each taxel electrically and relate voltage to force directly. Fabric sensor arrays are demonstrated for two different soft-bodied applications: an inflatable single link robot and a human wrist.

  4. A Novel Approach to ECG Classification Based upon Two-Layered HMMs in Body Sensor Networks

    Directory of Open Access Journals (Sweden)

    Wei Liang

    2014-03-01

    Full Text Available This paper presents a novel approach to ECG signal filtering and classification. Unlike the traditional techniques which aim at collecting and processing the ECG signals with the patient being still, lying in bed in hospitals, our proposed algorithm is intentionally designed for monitoring and classifying the patient’s ECG signals in the free-living environment. The patients are equipped with wearable ambulatory devices the whole day, which facilitates the real-time heart attack detection. In ECG preprocessing, an integral-coefficient-band-stop (ICBS filter is applied, which omits time-consuming floating-point computations. In addition, two-layered Hidden Markov Models (HMMs are applied to achieve ECG feature extraction and classification. The periodic ECG waveforms are segmented into ISO intervals, P subwave, QRS complex and T subwave respectively in the first HMM layer where expert-annotation assisted Baum-Welch algorithm is utilized in HMM modeling. Then the corresponding interval features are selected and applied to categorize the ECG into normal type or abnormal type (PVC, APC in the second HMM layer. For verifying the effectiveness of our algorithm on abnormal signal detection, we have developed an ECG body sensor network (BSN platform, whereby real-time ECG signals are collected, transmitted, displayed and the corresponding classification outcomes are deduced and shown on the BSN screen.

  5. A Novel Approach to ECG Classification Based upon Two-Layered HMMs in Body Sensor Networks

    Science.gov (United States)

    Liang, Wei; Zhang, Yinlong; Tan, Jindong; Li, Yang

    2014-01-01

    This paper presents a novel approach to ECG signal filtering and classification. Unlike the traditional techniques which aim at collecting and processing the ECG signals with the patient being still, lying in bed in hospitals, our proposed algorithm is intentionally designed for monitoring and classifying the patient's ECG signals in the free-living environment. The patients are equipped with wearable ambulatory devices the whole day, which facilitates the real-time heart attack detection. In ECG preprocessing, an integral-coefficient-band-stop (ICBS) filter is applied, which omits time-consuming floating-point computations. In addition, two-layered Hidden Markov Models (HMMs) are applied to achieve ECG feature extraction and classification. The periodic ECG waveforms are segmented into ISO intervals, P subwave, QRS complex and T subwave respectively in the first HMM layer where expert-annotation assisted Baum-Welch algorithm is utilized in HMM modeling. Then the corresponding interval features are selected and applied to categorize the ECG into normal type or abnormal type (PVC, APC) in the second HMM layer. For verifying the effectiveness of our algorithm on abnormal signal detection, we have developed an ECG body sensor network (BSN) platform, whereby real-time ECG signals are collected, transmitted, displayed and the corresponding classification outcomes are deduced and shown on the BSN screen. PMID:24681668

  6. A Survey of Routing Protocols in Wireless Body Sensor Networks

    Science.gov (United States)

    Bangash, Javed Iqbal; Abdullah, Abdul Hanan; Anisi, Mohammad Hossein; Khan, Abdul Waheed

    2014-01-01

    Wireless Body Sensor Networks (WBSNs) constitute a subset of Wireless Sensor Networks (WSNs) responsible for monitoring vital sign-related data of patients and accordingly route this data towards a sink. In routing sensed data towards sinks, WBSNs face some of the same routing challenges as general WSNs, but the unique requirements of WBSNs impose some more constraints that need to be addressed by the routing mechanisms. This paper identifies various issues and challenges in pursuit of effective routing in WBSNs. Furthermore, it provides a detailed literature review of the various existing routing protocols used in the WBSN domain by discussing their strengths and weaknesses. PMID:24419163

  7. Wearable Photoplethysmographic Sensors—Past and Present

    Directory of Open Access Journals (Sweden)

    Toshiyo Tamura

    2014-04-01

    Full Text Available Photoplethysmography (PPG technology has been used to develop small, wearable, pulse rate sensors. These devices, consisting of infrared light-emitting diodes (LEDs and photodetectors, offer a simple, reliable, low-cost means of monitoring the pulse rate noninvasively. Recent advances in optical technology have facilitated the use of high-intensity green LEDs for PPG, increasing the adoption of this measurement technique. In this review, we briefly present the history of PPG and recent developments in wearable pulse rate sensors with green LEDs. The application of wearable pulse rate monitors is discussed.

  8. Development of Wearable Sheet-Type Shear Force Sensor and Measurement System that is Insusceptible to Temperature and Pressure.

    Science.gov (United States)

    Toyama, Shigeru; Tanaka, Yasuhiro; Shirogane, Satoshi; Nakamura, Takashi; Umino, Tokio; Uehara, Ryo; Okamoto, Takuma; Igarashi, Hiroshi

    2017-07-31

    A sheet-type shear force sensor and a measurement system for the sensor were developed. The sensor has an original structure where a liquid electrolyte is filled in a space composed of two electrode-patterned polymer films and an elastic rubber ring. When a shear force is applied on the surface of the sensor, the two electrode-patterned films mutually move so that the distance between the internal electrodes of the sensor changes, resulting in current increase or decrease between the electrodes. Therefore, the shear force can be calculated by monitoring the current between the electrodes. Moreover, it is possible to measure two-dimensional shear force given that the sensor has multiple electrodes. The diameter and thickness of the sensor head were 10 mm and 0.7 mm, respectively. Additionally, we also developed a measurement system that drives the sensor, corrects the baseline of the raw sensor output, displays data, and stores data as a computer file. Though the raw sensor output was considerably affected by the surrounding temperature, the influence of temperature was drastically decreased by introducing a simple arithmetical calculation. Moreover, the influence of pressure simultaneously decreased after the same calculation process. A demonstrative measurement using the sensor revealed the practical usefulness for on-site monitoring.

  9. Performance of a wearable acoustic system for fetal movement discrimination.

    Directory of Open Access Journals (Sweden)

    Jonathan Lai

    Full Text Available Fetal movements (FM are a key factor in clinical management of high-risk pregnancies such as fetal growth restriction. While maternal perception of reduced FM can trigger self-referral to obstetric services, maternal sensation is highly subjective. Objective, reliable monitoring of fetal movement patterns outside clinical environs is not currently possible. A wearable and non-transmitting system capable of sensing fetal movements over extended periods of time would be extremely valuable, not only for monitoring individual fetal health, but also for establishing normal levels of movement in the population at large. Wearable monitors based on accelerometers have previously been proposed as a means of tracking FM, but such systems have difficulty separating maternal and fetal activity and have not matured to the level of clinical use. We introduce a new wearable system based on a novel combination of accelerometers and bespoke acoustic sensors as well as an advanced signal processing architecture to identify and discriminate between types of fetal movements. We validate the system with concurrent ultrasound tests on a cohort of 44 pregnant women and demonstrate that the garment is capable of both detecting and discriminating the vigorous, whole-body 'startle' movements of a fetus. These results demonstrate the promise of multimodal sensing for the development of a low-cost, non-transmitting wearable monitor for fetal movements.

  10. Pressure sensor apparatus for indicating pressure in the body

    International Nuclear Information System (INIS)

    Hittman, F.; Fleischmann, L.W.

    1981-01-01

    A novel pressure sensor for indicating pressure in the body cavities of humans or animals is described in detail. The pressure sensor apparatus is relatively small and is easily implantable. It consists of a radioactive source (e.g. Pr-145, C-14, Ni-63, Sr-90 and Am-241) and associated radiation shielding and a bellows. The pressure acting upon the sensing tambour causes the bellows to expand and contract. This is turn causes the radiation shielding to move and changes in pressure can then be monitored external to the body using a conventional nuclear detector. The bellows is made of resilient material (e.g. gold plated nickel) and has a wall thickness of approximately 0.0003 inches. The apparatus is essentially insensitive to temperature variations. (U.K.)

  11. Human-motion energy harvester for autonomous body area sensors

    Science.gov (United States)

    Geisler, M.; Boisseau, S.; Perez, M.; Gasnier, P.; Willemin, J.; Ait-Ali, I.; Perraud, S.

    2017-03-01

    This paper reports on a method to optimize an electromagnetic energy harvester converting the low-frequency body motion and aimed at powering wireless body area sensors. This method is based on recorded accelerations, and mechanical and transduction models that enable an efficient joint optimization of the structural parameters. An optimized prototype of 14.8 mmØ × 52 mm, weighting 20 g, has generated up to 4.95 mW in a resistive load when worn at the arm during a run, and 6.57 mW when hand-shaken. Among the inertial electromagnetic energy harvesters reported so far, this one exhibits one of the highest power densities (up to 730 μW cm-3). The energy harvester was finally used to power a bluetooth low energy wireless sensor node with accelerations measurements at 25 Hz.

  12. Wearable Gait Measurement System with an Instrumented Cane for Exoskeleton Control

    Directory of Open Access Journals (Sweden)

    Modar Hassan

    2014-01-01

    Full Text Available In this research we introduce a wearable sensory system for motion intention estimation and control of exoskeleton robot. The system comprises wearable inertial motion sensors and shoe-embedded force sensors. The system utilizes an instrumented cane as a part of the interface between the user and the robot. The cane reflects the motion of upper limbs, and is used in terms of human inter-limb synergies. The developed control system provides assisted motion in coherence with the motion of other unassisted limbs. The system utilizes the instrumented cane together with body worn sensors, and provides assistance for start, stop and continuous walking. We verified the function of the proposed method and the developed wearable system through gait trials on treadmill and on ground. The achievement contributes to finding an intuitive and feasible interface between human and robot through wearable gait sensors for practical use of assistive technology. It also contributes to the technology for cognitively assisted locomotion, which helps the locomotion of physically challenged people.

  13. Infrared and Visible links for medical Body Sensor Networks

    OpenAIRE

    Lebas , C; Sahuguede , S; Julien-Vergonjanne , A; Combeau , P; Aveneau , L

    2018-01-01

    International audience; — Our previous studies focused on channel simulation and performance evaluation of optical wireless links for medical body sensor networks. This allowed us to increase our expertise in this field and to propose here a full optical wireless bidirectional system named as LiFi communication system for medical monitoring applications. The full duplex bidirectional communication is based on an infrared uplink and visible downlink. The studied scenario considers a patient we...

  14. A reliable low-cost wireless and wearable gait monitoring system based on a plastic optical fibre sensor

    International Nuclear Information System (INIS)

    Bilro, L; Pinto, J L; Oliveira, J G; Nogueira, R N

    2011-01-01

    A wearable and wireless system designed to evaluate quantitatively the human gait is presented. It allows knee sagittal motion monitoring over long distances and periods with a portable and low-cost package. It is based on the measurement of transmittance changes when a side-polished plastic optical fibre is bent. Four voluntary healthy subjects, on five different days, were tested in order to assess inter-day and inter-subject reliability. Results have shown that this technique is reliable, allows a one-time calibration and is suitable in the diagnosis and rehabilitation of knee injuries or for monitoring the performance of competitive athletes. Environmental testing was accomplished in order to study the influence of different temperatures and humidity conditions

  15. NASA Wearable Technology CLUSTER 2013-2014 Report

    Science.gov (United States)

    Simon, Cory; Dunne, Lucy; Zeagler, Clint; Martin, Tom; Pailes-Friedman, Rebecca

    2014-01-01

    Wearable technology has the potential to revolutionize the way humans interact with one another, with information, and with the electronic systems that surround them. This change can already be seen in the dramatic increase in the availability and use of wearable health and activity monitors. These devices continuously monitor the wearer using on-­-body sensors and wireless communication. They provide feedback that can be used to improve physical health and performance. Smart watches and head mounted displays are also receiving a great deal of commercial attention, providing immediate access to information via graphical displays, as well as additional sensing features. For the purposes of the Wearable Technology CLUSTER, wearable technology is broadly defined as any electronic sensing, human interfaces, computing, or communication that is mounted on the body. Current commercially available wearable devices primarily house electronics in rigid packaging to provide protection from flexing, moisture, and other contaminants. NASA mentors are interested in this approach, but are also interested in direct integration of electronics into clothing to enable more comfortable systems. For human spaceflight, wearable technology holds a great deal of promise for significantly improving safety, efficiency, autonomy, and research capacity for the crew in space and support personnel on the ground. Specific capabilities of interest include: Continuous biomedical monitoring for research and detection of health problems. Environmental monitoring for individual exposure assessments and alarms. Activity monitoring for responsive robotics and environments. Multi-modal caution and warning using tactile, auditory, and visual alarms. Wireless, hands-free, on-demand voice communication. Mobile, on-demand access to space vehicle and robotic displays and controls. Many technical challenges must be overcome to realize these wearable technology applications. For example, to make a wearable

  16. Wearable Technology

    Science.gov (United States)

    Watson, Amanda

    2013-01-01

    Wearable technology projects, to be useful, in the future, must be seamlessly integrated with the Flight Deck of the Future (F.F). The lab contains mockups of space vehicle cockpits, habitat living quarters, and workstations equipped with novel user interfaces. The Flight Deck of the Future is one element of the Integrated Power, Avionics, and Software (IPAS) facility, which, to a large extent, manages the F.F network and data systems. To date, integration with the Flight Deck of the Future has been limited by a lack of tools and understanding of the Flight Deck of the Future data handling systems. To remedy this problem it will be necessary to learn how data is managed in the Flight Deck of the Future and to develop tools or interfaces that enable easy integration of WEAR Lab and EV3 products into the Flight Deck of the Future mockups. This capability is critical to future prototype integration, evaluation, and demonstration. This will provide the ability for WEAR Lab products, EV3 human interface prototypes, and technologies from other JSC organizations to be evaluated and tested while in the Flight Deck of the Future. All WEAR Lab products must be integrated with the interface that will connect them to the Flight Deck of the Future. The WEAR Lab products will primarily be programmed in Arduino. Arduino will be used for the development of wearable controls and a tactile communication garment. Arduino will also be used in creating wearable methane detection and warning system.

  17. Electromagnetics of body area networks antennas, propagation, and RF systems

    CERN Document Server

    Werner, Douglas H

    2016-01-01

    The book is a comprehensive treatment of the field, covering fundamental theoretical principles and new technological advancements, state-of-the-art device design, and reviewing examples encompassing a wide range of related sub-areas. In particular, the first area focuses on the recent development of novel wearable and implantable antenna concepts and designs including metamaterial-based wearable antennas, microwave circuit integrated wearable filtering antennas, and textile and/or fabric material enabled wearable antennas. The second set of topics covers advanced wireless propagation and the associated statistical models for on-body, in-body, and off-body modes. Other sub-areas such as efficient numerical human body modeling techniques, artificial phantom synthesis and fabrication, as well as low-power RF integrated circuits and related sensor technology are also discussed. These topics have been carefully selected for their transformational impact on the next generation of body-area network systems and beyo...

  18. Reliability of wireless monitoring using a wearable patch sensor in high-risk surgical patients at a step-down unit in the Netherlands: a clinical validation study.

    Science.gov (United States)

    Breteler, Martine J M; Huizinga, Erik; van Loon, Kim; Leenen, Luke P H; Dohmen, Daan A J; Kalkman, Cor J; Blokhuis, Taco J

    2018-02-27

    Intermittent vital signs measurements are the current standard on hospital wards, typically recorded once every 8 hours. Early signs of deterioration may therefore be missed. Recent innovations have resulted in 'wearable' sensors, which may capture patient deterioration at an earlier stage. The objective of this study was to determine whether a wireless 'patch' sensor is able to reliably measure respiratory and heart rate continuously in high-risk surgical patients. The secondary objective was to explore the potential of the wireless sensor to serve as a safety monitor. In an observational methods comparisons study, patients were measured with both the wireless sensor and bedside routine standard for at least 24 hours. University teaching hospital, single centre. Twenty-five postoperative surgical patients admitted to a step-down unit. Primary outcome measures were limits of agreement and bias of heart rate and respiratory rate. Secondary outcome measures were sensor reliability, defined as time until first occurrence of data loss. 1568 hours of vital signs data were analysed. Bias and 95% limits of agreement for heart rate were -1.1 (-8.8 to 6.5) beats per minute. For respiration rate, bias was -2.3 breaths per minute with wide limits of agreement (-15.8 to 11.2 breaths per minute). Median filtering over a 15 min period improved limits of agreement of both respiration and heart rate. 63% of the measurements were performed without data loss greater than 2 min. Overall data loss was limited (6% of time). The wireless sensor is capable of accurately measuring heart rate, but accuracy for respiratory rate was outside acceptable limits. Remote monitoring has the potential to contribute to early recognition of physiological decline in high-risk patients. Future studies should focus on the ability to detect patient deterioration on low care environments and at home after discharge. © Article author(s) (or their employer(s) unless otherwise stated in the text of

  19. Inkjet-/3D-/4D-printed autonomous wearable RF modules for biomonitoring, positioning and sensing applications

    Science.gov (United States)

    Bito, Jo; Bahr, Ryan; Hester, Jimmy; Kimionis, John; Nauroze, Abdullah; Su, Wenjing; Tehrani, Bijan; Tentzeris, Manos M.

    2017-05-01

    In this paper, numerous inkjet-/3D-/4D-printed wearable flexible antennas, RF electronics, modules and sensors fabricated on paper and other polymer (e.g. LCP) substrates are introduced as a system-level solution for ultra-low-cost mass production of autonomous Biomonitoring, Positioning and Sensing applications. This paper briefly discusses the state-of-the-art area of fully-integrated wearable wireless sensor modules on paper or flexible LCP and show the first ever 4D sensor module integration on paper, as well as numerous 3D and 4D multilayer paper-based and LCP-based RF/microwave, flexible and wearable structures, that could potentially set the foundation for the truly convergent wireless sensor ad-hoc "on-body networks of the future with enhanced cognitive intelligence and "rugged" packaging. Also, some challenges concerning the power sources of "nearperpetual" wearable RF modules, including flexible miniaturized batteries as well as power-scavenging approaches involving electromagnetic and solar energy forms are discuessed. The final step of the paper will involve examples from mmW wearable (e.g. biomonitoring) antennas and RF modules, as well as the first examples of the integration of inkjet-printed nanotechnology-based (e.g.CNT) sensors on paper and organic substrates for Internet of Things (IoT) applications. It has to be noted that the paper will review and present challenges for inkjetprinted organic active and nonlinear devices as well as future directions in the area of environmentally-friendly "green") wearable RF electronics and "smart-skin conformal sensors.

  20. BLIG: A New Approach for Sensor Identification, Grouping,and Authorisation in Body Sensor Networks

    DEFF Research Database (Denmark)

    Andersen, Jacob; Bardram, Jakob Eyvind

    2007-01-01

    Using body sensor networks (BSN) in critical clinical settings like emergency units in hospitals or in accidents requires that such a network can be deployed, configured, and started in a fast and easy way, while maintaining trust in the network. In this paper we present a novel approach called...... BLIG (Blinking Led Indicated Grouping) for easy deployment of BSNs on patients in critical situations, including mechanisms for uniquely identifying and grouping sensor nodes belonging to a patient in a secure and trusted way. This approach has been designed in close cooperation with users, and easy...

  1. IBE-Lite: a lightweight identity-based cryptography for body sensor networks.

    Science.gov (United States)

    Tan, Chiu C; Wang, Haodong; Zhong, Sheng; Li, Qun

    2009-11-01

    A body sensor network (BSN) is a network of sensors deployed on a person's body for health care monitoring. Since the sensors collect personal medical data, security and privacy are important components in a BSN. In this paper, we developed IBE-Lite, a lightweight identity-based encryption suitable for sensors in a BSN. We present protocols based on IBE-Lite that balance security and privacy with accessibility and perform evaluation using experiments conducted on commercially available sensors.

  2. Interactive balance training integrating sensor-based visual feedback of movement performance: a pilot study in older adults

    OpenAIRE

    Schwenk, Michael; Grewal, Gurtej S; Honarvar, Bahareh; Schwenk, Stefanie; Mohler, Jane; Khalsa, Dharma S; Najafi, Bijan

    2014-01-01

    Background Wearable sensor technology can accurately measure body motion and provide incentive feedback during exercising. The aim of this pilot study was to evaluate the effectiveness and user experience of a balance training program in older adults integrating data from wearable sensors into a human-computer interface designed for interactive training. Methods Senior living community residents (mean age 84.6) with confirmed fall risk were randomized to an intervention (IG, n?=?17) or contro...

  3. The Baetylus Theorem?the central disconnect driving consumer behavior and investment returns in Wearable Technologies

    OpenAIRE

    Levine, James A.

    2016-01-01

    The Wearable Technology market may increase fivefold by the end of the decade. There is almost no academic investigation as to what drives the investment hypothesis in wearable technologies. This paper seeks to examine this issue from an evidence-based perspective. There is a fundamental disconnect in how consumers view wearable sensors and how companies market them; this is called The Baetylus Theorem where people believe (falsely) that by buying a wearable sensor they will receive health be...

  4. Lightweight, Wearable Metal Rubber-Textile Sensor for In-Situ Lunar Autonomous Health Monitoring, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — This NASA Phase II SBIR program would develop comfortable garments with multiple integrated sensor functions for the monitoring of astronauts during long duration...

  5. Pilot Test of a New Personal Health System Integrating Environmental and Wearable Sensors for Telemonitoring and Care of Elderly People at Home (SMARTA Project).

    Science.gov (United States)

    Pigini, Lucia; Bovi, Gabriele; Panzarino, Claudia; Gower, Valerio; Ferratini, Maurizio; Andreoni, Giuseppe; Sassi, Roberto; Rivolta, Massimo W; Ferrarin, Maurizio

    2017-01-01

    The increase in life expectancy is accompanied by a growing number of elderly subjects affected by chronic comorbidities, a health issue which also implies important socioeconomic consequences. Shifting from hospital or community dwelling care towards a home personalized healthcare paradigm would promote active aging with a better quality of life, along with a reduction in healthcare-related costs. The aim of the SMARTA project was to develop and test an innovative personal health system integrating standard sensors as well as innovative wearable and environmental sensors to allow home telemonitoring of vital parameters and detection of anomalies in daily activities, thus supporting active aging through remote healthcare. A first phase of the project consisted in the definition of the health and environmental parameters to be monitored (electrocardiography and actigraphy, blood pressure and oxygen saturation, weight, ear temperature, glycemia, home interaction monitoring - water tap, refrigerator, and dishwasher), the feedbacks for the clinicians, and the reminders for the patients. It was followed by a technical feasibility analysis leading to an iterative process of prototype development, sensor integration, and testing. Once the prototype had reached an advanced stage of development, a group of 32 volunteers - including 15 healthy adult subjects, 13 elderly people with cardiac diseases, and 4 clinical operators - was recruited to test the system in a real home setting, in order to evaluate both technical reliability and user perception of the system in terms of effectiveness, usability, acceptance, and attractiveness. The testing in a real home setting showed a good perception of the SMARTA system and its functionalities both by the patients and by the clinicians, who appreciated the user interface and the clinical governance system. The moderate system reliability of 65-70% evidenced some technical issues, mainly related to sensor integration, while the patient

  6. Distance Based Method for Outlier Detection of Body Sensor Networks

    Directory of Open Access Journals (Sweden)

    Haibin Zhang

    2016-01-01

    Full Text Available We propose a distance based method for the outlier detection of body sensor networks. Firstly, we use a Kernel Density Estimation (KDE to calculate the probability of the distance to k nearest neighbors for diagnosed data. If the probability is less than a threshold, and the distance of this data to its left and right neighbors is greater than a pre-defined value, the diagnosed data is decided as an outlier. Further, we formalize a sliding window based method to improve the outlier detection performance. Finally, to estimate the KDE by training sensor readings with errors, we introduce a Hidden Markov Model (HMM based method to estimate the most probable ground truth values which have the maximum probability to produce the training data. Simulation results show that the proposed method possesses a good detection accuracy with a low false alarm rate.

  7. Recognizing Multi-user Activities using Body Sensor Networks

    DEFF Research Database (Denmark)

    Gu, Tao; Wang, Liang; Chen, Hanhua

    2011-01-01

    The advances of wireless networking and sensor technology open up an interesting opportunity to infer human activities in a smart home environment. Existing work in this paradigm focuses mainly on recognizing activities of a single user. In this work, we address the fundamental problem...... activity classes of data—for building activity models and design a scalable, noise-resistant, Emerging Pattern based Multi-user Activity Recognizer (epMAR) to recognize both single- and multi-user activities. We develop a multi-modal, wireless body sensor network for collecting real-world traces in a smart...... home environment, and conduct comprehensive empirical studies to evaluate our system. Results show that epMAR outperforms existing schemes in terms of accuracy, scalability and robustness....

  8. A Fast Multimodal Ectopic Beat Detection Method Applied for Blood Pressure Estimation Based on Pulse Wave Velocity Measurements in Wearable Sensors.

    Science.gov (United States)

    Pflugradt, Maik; Geissdoerfer, Kai; Goernig, Matthias; Orglmeister, Reinhold

    2017-01-14

    Automatic detection of ectopic beats has become a thoroughly researched topic, with literature providing manifold proposals typically incorporating morphological analysis of the electrocardiogram (ECG). Although being well understood, its utilization is often neglected, especially in practical monitoring situations like online evaluation of signals acquired in wearable sensors. Continuous blood pressure estimation based on pulse wave velocity considerations is a prominent example, which depends on careful fiducial point extraction and is therefore seriously affected during periods of increased occurring extrasystoles. In the scope of this work, a novel ectopic beat discriminator with low computational complexity has been developed, which takes advantage of multimodal features derived from ECG and pulse wave relating measurements, thereby providing additional information on the underlying cardiac activity. Moreover, the blood pressure estimations' vulnerability towards ectopic beats is closely examined on records drawn from the Physionet database as well as signals recorded in a small field study conducted in a geriatric facility for the elderly. It turns out that a reliable extrasystole identification is essential to unsupervised blood pressure estimation, having a significant impact on the overall accuracy. The proposed method further convinces by its applicability to battery driven hardware systems with limited processing power and is a favorable choice when access to multimodal signal features is given anyway.

  9. Quantitative Approach Based on Wearable Inertial Sensors to Assess and Identify Motion and Errors in Techniques Used during Training of Transfers of Simulated c-Spine-Injured Patients

    Directory of Open Access Journals (Sweden)

    Karina Lebel

    2018-01-01

    Full Text Available Patients with suspected spinal cord injuries undergo numerous transfers throughout treatment and care. Effective c-spine stabilization is crucial to minimize the impacts of the suspected injury. Healthcare professionals are trained to perform those transfers using simulation; however, the feedback on the manoeuvre is subjective. This paper proposes a quantitative approach to measure the efficacy of the c-spine stabilization and provide objective feedback during training. Methods. 3D wearable motion sensors are positioned on a simulated patient to capture the motion of the head and trunk during a training scenario. Spatial and temporal indicators associated with the motion can then be derived from the signals. The approach was developed and tested on data obtained from 21 paramedics performing the log-roll, a transfer technique commonly performed during prehospital and hospital care. Results. In this scenario, 55% of the c-spine motion could be explained by the difficulty of rescuers to maintain head and trunk alignment during the rotation part of the log-roll and their difficulty to initiate specific phases of the motion synchronously. Conclusion. The proposed quantitative approach has the potential to be used for personalized feedback during training sessions and could even be embedded into simulation mannequins to provide an innovative training solution.

  10. Design a Wearable Device for Blood Oxygen Concentration and Temporal Heart Beat Rate

    Science.gov (United States)

    Myint, Cho Zin; Barsoum, Nader; Ing, Wong Kiing

    2010-06-01

    The wireless network technology is increasingly important in healthcare as a result of the aging population and the tendency to acquire chronic disease such as heart attack, high blood pressure amongst the elderly. A wireless sensor network system that has the capability to monitor physiological sign such as SpO2 (Saturation of Arterial Oxygen) and heart beat rate in real-time from the human's body is highlighted in this study. This research is to design a prototype sensor network hardware, which consists of microcontroller PIC18F series and transceiver unit. The sensor is corporate into a wearable body sensor network which is small in size and easy to use. The sensor allows a non invasive, real time method to provide information regarding the health of the body. This enables a more efficient and economical means for managing the health care of the population.

  11. Emotion Recognition Through Body Language Using RGB-D Sensor

    DEFF Research Database (Denmark)

    Kiforenko, Lilita; Kraft, Dirk

    2016-01-01

    by various visual stimuli. We present the emotion dataset that is recorded using Microsoft Kinect for Windows sensor and body joints rotation angles that are extracted using Microsoft Kinect Software Development Kit 1.6. The classified emotions are curiosity, confusion, joy, boredom and disgust. We show...... joint rotation angles and meta-features that are fed into a Support Vector Machines classifier. The work of Gaber-Barron and Si (2012) is used as inspiration and many of their proposed meta-features are reimplemented or modified. In this work we try to identify ”basic” human emotions, that are triggered...

  12. A flexible, transparent and high-performance gas sensor based on layer-materials for wearable technology

    Science.gov (United States)

    Zheng, Zhaoqiang; Yao, Jiandong; Wang, Bing; Yang, Guowei

    2017-10-01

    Gas sensors play a vital role among a wide range of practical applications. Recently, propelled by the development of layered materials, gas sensors have gained much progress. However, the high operation temperature has restricted their further application. Herein, via a facile pulsed laser deposition (PLD) method, we demonstrate a flexible, transparent and high-performance gas sensor made of highly-crystalline indium selenide (In2Se3) film. Under UV-vis-NIR light or even solar energy activation, the constructed gas sensors exhibit superior properties for detecting acetylene (C2H2) gas at room temperature. We attribute these properties to the photo-induced charger transfer mechanism upon C2H2 molecule adsorption. Moreover, no apparent degradation in the device properties is observed even after 100 bending cycles. In addition, we can also fabricate this device on rigid substrates, which is also capable to detect gas molecules at room temperature. These results unambiguously distinguish In2Se3 as a new candidate for future application in monitoring C2H2 gas at room temperature and open up new opportunities for developing next generation full-spectrum activated gas sensors.

  13. Wearable Sensor/Device (Fitbit One) and SMS Text-Messaging Prompts to Increase Physical Activity in Overweight and Obese Adults: A Randomized Controlled Trial

    Science.gov (United States)

    Cadmus-Bertram, Lisa A.; Natarajan, Loki; White, Martha M.; Madanat, Hala; Nichols, Jeanne F.; Ayala, Guadalupe X.; Pierce, John P.

    2015-01-01

    Abstract Background: Studies have shown self-monitoring can modify health behaviors, including physical activity (PA). This study tested the utility of a wearable sensor/device (Fitbit® One™; Fitbit Inc., San Francisco, CA) and short message service (SMS) text-messaging prompts to increase PA in overweight and obese adults. Materials and Methods: Sixty-seven adults wore a Fitbit One tracker for 6 weeks; half were randomized to also receive three daily SMS-based PA prompts. The Fitbit One consisted of a wearable tracker for instant feedback on performance and a Web site/mobile application (app) for detailed summaries. Outcome measures were objectively measured steps and minutes of PA by intensity using two accelerometers: Actigraph™ (Pensacola, FL) GT3X+ (primary measure) at baseline and Week 6 and Fitbit One (secondary measure) at baseline and Weeks 1, 2, 3, 4, 5, and 6. Results: Mixed-model repeated-measures analysis of primary measures indicated a significant within-group increase of +4.3 (standard error [SE]=2.0) min/week of moderate- to vigorous-intensity PA (MVPA) at 6-week follow-up (p=0.04) in the comparison group (Fitbit only), but no study group differences across PA levels. Secondary measures indicated the SMS text-messaging effect lasted for only 1 week: the intervention group increased by +1,266 steps (SE=491; p=0.01), +17.8 min/week MVPA (SE=8.5; p=0.04), and +38.3 min/week total PA (SE=15.9; p=0.02) compared with no changes in the comparison group, and these between-group differences were significant for steps (p=0.01), fairly/very active minutes (p<0.01), and total active minutes (p=0.02). Conclusions: These data suggest that the Fitbit One achieved a small increase in MVPA at follow-up and that the SMS-based PA prompts were insufficient in increasing PA beyond 1 week. Future studies can test this intervention in those requiring less help and/or test strategies to increase participants' engagement levels. PMID:26431257

  14. Automatic identification of inertial sensor placement on human body segments during walking

    NARCIS (Netherlands)

    Weenk, D.; van Beijnum, Bernhard J.F.; Baten, Christian T.M.; Hermens, Hermanus J.; Veltink, Petrus H.

    2013-01-01

    We present a novel method for the automatic identification of inertial sensors on human body segments during walking. This method allows the user to place (wireless) inertial sensors on arbitrary body segments. Next, the user walks for just a few seconds and the segment to which each sensor is

  15. Wearable Health Monitoring Systems, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — The objective of this proposal is to demonstrate the feasibility of producing a wearable health monitoring system for the human body that is functional, comfortable,...

  16. Wearable Electro-Textiles for Battlefield Awareness

    National Research Council Canada - National Science Library

    Winterhalter, C. A; Teverovsky, Justyna; Horowitz, Wendy; Sharma, Vikram; Lee, Kang

    2004-01-01

    This summary describes efforts to develop wearable electronic textiles and connectors to support body worn networking, communications, and battlefield awareness for future service members of the U.S. Army...

  17. Wearable Health Monitoring Systems, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — The objective of this proposal is to demonstrate the feasibility of producing a wearable health monitoring system for the human body that is functional, comfortable,...

  18. Augmented reality som wearable technology

    DEFF Research Database (Denmark)

    Rahn, Annette

    “How Augmented reality can facilitate learning in visualizing human anatomy “ At this station I demonstrate how Augmented reality can be used to visualize the human lungs in situ and as a wearable technology which establish connection between body, image and technology in education. I will show...

  19. Flexible Sensing Electronics for Wearable/Attachable Health Monitoring.

    Science.gov (United States)

    Wang, Xuewen; Liu, Zheng; Zhang, Ting

    2017-07-01

    Wearable or attachable health monitoring smart systems are considered to be the next generation of personal portable devices for remote medicine practices. Smart flexible sensing electronics are components crucial in endowing health monitoring systems with the capability of real-time tracking of physiological signals. These signals are closely associated with body conditions, such as heart rate, wrist pulse, body temperature, blood/intraocular pressure and blood/sweat bio-information. Monitoring such physiological signals provides a convenient and non-invasive way for disease diagnoses and health assessments. This Review summarizes the recent progress of flexible sensing electronics for their use in wearable/attachable health monitoring systems. Meanwhile, we present an overview of different materials and configurations for flexible sensors, including piezo-resistive, piezo-electrical, capacitive, and field effect transistor based devices, and analyze the working principles in monitoring physiological signals. In addition, the future perspectives of wearable healthcare systems and the technical demands on their commercialization are briefly discussed. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  20. Wearable ballistocardiogram and seismocardiogram systems for health and performance.

    Science.gov (United States)

    Etemadi, Mozziyar; Inan, Omer T

    2018-02-01

    Cardiovascular diseases (CVDs) are prevalent in the US, and many forms of CVD primarily affect the mechanical aspects of heart function. Wearable technologies for monitoring the mechanical health of the heart and vasculature could enable proactive management of CVDs through titration of care based on physiological status as well as preventative wellness monitoring to help promote lifestyle choices that reduce the overall risk of developing CVDs. Additionally, such wearable technologies could be used to optimize human performance in austere environments. This review describes our progress in developing wearable ballistocardiogram (BCG)- and seismocardiogram-based systems for monitoring relative changes in cardiac output, contractility, and blood pressure. Our systems use miniature, low-noise accelerometers to measure the movements of the body in response to the heartbeat and novel machine learning algorithms to provide robustness against motion artifacts and sensor misplacement. Moreover, we have mathematically related wearable BCG signals-representing local, cardiogenic movements of a point on the body-to better understood whole body BCG signals, and thereby improved estimation of key health parameters. We validated these systems with experiments in healthy subjects, studies in patients with heart failure, and measurements in austere environments such as water immersion. The systems can be used in future work as a tool for clinicians and physiologists to measure the mechanical aspects of cardiovascular function outside of clinical settings, and to thereby titrate care for patients with CVDs, provide preventative screening, and optimize performance in austere environments by providing real-time in-depth information regarding performance and risk.

  1. A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings.

    Science.gov (United States)

    Carter, Robert; Carter, Kirtigandha Salwe; Holliday, John; Holliday, Alice; Harrison, Carlton Keith

    2018-01-22

    A pragmatic breath-based intervention to benefit human performance and stress management is timely and valuable to individuals seeking holistic approaches for emotional regulation and optimizing compensatory reserve mechanisms. This protocol is designed to not only teach mind-body awareness but also to provide feedback utilizing physiological data and survey results. The primary findings of this study showed that heart coherence and alpha variables were significantly correlated after four weeks of the breath-based meditation stress protocol. Meditation and rhythmic breathing produced significant increases in alpha brain activity. These brain physiological responses conformed to the Pleth Variability Index (PVI) changes, suggesting the ability of the human body to enter into a meditative state and effectively manage stress. When assessed after four weeks of daily practicing the techniques employed in the stress management protocol, based on the Five Facet Mindfulness Questionnaire, subjects improved in applying mindfulness skills. The overall mindfulness score, Pleth variability index (PVI), and perfusion index (PI) increased after the 4-week intervention period. Results from electroencephalography (brain waves) were consistent with a meditative state during the post-study follow-up session. This provides evidence that wearable devices are feasible for data collection during a breath-based stress management intervention. This protocol can be easily and efficiently implemented into any study design in which physiological data are desired in a non-laboratory-based setting.

  2. Dry Electrodes for ECG and Pulse Transit Time for Blood Pressure: A Wearable Sensor and Smartphone Communication Approach

    Science.gov (United States)

    Shyamkumar, Prashanth

    Cardiovascular Diseases (CVDs) have been a major cause for deaths in both men and women in United States. Cerebrovascular Diseases like Strokes are known to have origins in CVDs as well. Moreover, nearly 18 Million Americans have a history of myocardial infarction and are currently undergoing cardiac rehabilitation. Consequently, CVDs are the highest costing disease groups and cost more than all types of cancer combined. However, significant cost reduction is possible through the effective use of the vast advances in embedded and pervasive electronic devices for healthcare. These devices can automate and move a significant portion of disease management to the patient's home through cyber connectivity, a concept known as point-of-care (POC) diagnostics and healthcare services. POC can minimize hospital visits and potentially avoid admission altogether with prognostic tools that give advanced notice of any abnormalities or chronic illnesses so that the treatment can be planned in advance. The POC concept requires continuous remote health monitoring. Therefore, the various sensors needed for comprehensive monitoring need to be worn daily and throughout the day. Moreover, true "roaming" capability is necessary so that it does not restrict the user's travel or his/her quotidian activities. Two biomedical signals namely, Electrocardiogram (ECG) and Blood Pressure are important diagnostic tests in assessing the cardiac health of a person. To that end, the research presented in this thesis: First , describes the development of a remote monitoring solution based on Bluetooth(TM), smartphones and cyber infrastructure for cardiac care called e-nanoflex. Second, Sensors for ECG that are compatible with everyday life style namely, (a) dry, gel-less vertically aligned gold nanowire electrodes, (b) dry textile-based conductive sensor electrodes to address the need for this technology to monitor cardiovascular diseases in women are tested with e-nanoflex and discussed. Third, non

  3. Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review.

    Science.gov (United States)

    Camomilla, Valentina; Bergamini, Elena; Fantozzi, Silvia; Vannozzi, Giuseppe

    2018-03-15

    Recent technological developments have led to the production of inexpensive, non-invasive, miniature magneto-inertial sensors, ideal for obtaining sport performance measures during training or competition. This systematic review evaluates current evidence and the future potential of their use in sport performance evaluation. Articles published in English (April 2017) were searched in Web-of-Science, Scopus, Pubmed, and Sport-Discus databases. A keyword search of titles, abstracts and keywords which included studies using accelerometers, gyroscopes and/or magnetometers to analyse sport motor-tasks performed by athletes (excluding risk of injury, physical activity, and energy expenditure) resulted in 2040 papers. Papers and reference list screening led to the selection of 286 studies and 23 reviews. Information on sport, motor-tasks, participants, device characteristics, sensor position and fixing, experimental setting and performance indicators was extracted. The selected papers dealt with motor capacity assessment (51 papers), technique analysis (163), activity classification (19), and physical demands assessment (61). Focus was placed mainly on elite and sub-elite athletes (59%) performing their sport in-field during training (62%) and competition (7%). Measuring movement outdoors created opportunities in winter sports (8%), water sports (16%), team sports (25%), and other outdoor activities (27%). Indications on the reliability of sensor-based performance indicators are provided, together with critical considerations and future trends.

  4. Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review

    Directory of Open Access Journals (Sweden)

    Valentina Camomilla

    2018-03-01

    Full Text Available Recent technological developments have led to the production of inexpensive, non-invasive, miniature magneto-inertial sensors, ideal for obtaining sport performance measures during training or competition. This systematic review evaluates current evidence and the future potential of their use in sport performance evaluation. Articles published in English (April 2017 were searched in Web-of-Science, Scopus, Pubmed, and Sport-Discus databases. A keyword search of titles, abstracts and keywords which included studies using accelerometers, gyroscopes and/or magnetometers to analyse sport motor-tasks performed by athletes (excluding risk of injury, physical activity, and energy expenditure resulted in 2040 papers. Papers and reference list screening led to the selection of 286 studies and 23 reviews. Information on sport, motor-tasks, participants, device characteristics, sensor position and fixing, experimental setting and performance indicators was extracted. The selected papers dealt with motor capacity assessment (51 papers, technique analysis (163, activity classification (19, and physical demands assessment (61. Focus was placed mainly on elite and sub-elite athletes (59% performing their sport in-field during training (62% and competition (7%. Measuring movement outdoors created opportunities in winter sports (8%, water sports (16%, team sports (25%, and other outdoor activities (27%. Indications on the reliability of sensor-based performance indicators are provided, together with critical considerations and future trends.

  5. An Asynchronous Multi-Sensor Micro Control Unit for Wireless Body Sensor Networks (WBSNs

    Directory of Open Access Journals (Sweden)

    Ching-Hsing Luo

    2011-07-01

    Full Text Available In this work, an asynchronous multi-sensor micro control unit (MCU core is proposed for wireless body sensor networks (WBSNs. It consists of asynchronous interfaces, a power management unit, a multi-sensor controller, a data encoder (DE, and an error correct coder (ECC. To improve the system performance and expansion abilities, the asynchronous interface is created for handshaking different clock domains between ADC and RF with MCU. To increase the use time of the WBSN system, a power management technique is developed for reducing power consumption. In addition, the multi-sensor controller is designed for detecting various biomedical signals. To prevent loss error from wireless transmission, use of an error correct coding technique is important in biomedical applications. The data encoder is added for lossless compression of various biomedical signals with a compression ratio of almost three. This design is successfully tested on a FPGA board. The VLSI architecture of this work contains 2.68-K gate counts and consumes power 496-μW at 133-MHz processing rate by using TSMC 0.13-μm CMOS process. Compared with the previous techniques, this work offers higher performance, more functions, and lower hardware cost than other micro controller designs.

  6. A Low-Power CMOS Piezoelectric Transducer Based Energy Harvesting Circuit for Wearable Sensors for Medical Applications

    Directory of Open Access Journals (Sweden)

    Taeho Oh

    2017-12-01

    Full Text Available Piezoelectric vibration based energy harvesting systems have been widely utilized and researched as powering modules for various types of sensor systems due to their ease of integration and relatively high energy density compared to RF, thermal, and electrostatic based energy harvesting systems. In this paper, a low-power CMOS full-bridge rectifier is presented as a potential solution for an efficient energy harvesting system for piezoelectric transducers. The energy harvesting circuit consists of two n-channel MOSFETs (NMOS and two p-channel MOSFETs (PMOS devices implementing a full-bridge rectifier coupled with a switch control circuit based on a PMOS device driven by a comparator. With a load of 45 kΩ, the output rectifier voltage and the input piezoelectric transducer voltage are 694 mV and 703 mV, respectably, while the VOUT versus VIN conversion ratio is 98.7% with a PCE of 52.2%. The energy harvesting circuit has been designed using 130 nm standard CMOS process.

  7. Objective assessment of movement competence in children using wearable sensors: An instrumented version of the TGMD-2 locomotor subtest.

    Science.gov (United States)

    Bisi, Maria Cristina; Pacini Panebianco, G; Polman, R; Stagni, R

    2017-07-01

    Movement competence (MC) is defined as the development of sufficient skill to assure successful performance in different physical activities. Monitoring children MC during maturation is fundamental to detect early minor delays and define effective intervention. To this purpose, several MC assessment batteries are available. When evaluating movement strategies, with the aim of identifying specific skill components that may need improving, widespread MC assessment is limited by high time consumption for scoring and the need for trained operators to ensure reliability. This work aims to facilitate and support the assessment by designing, implementing and validating an instrumented version of the TGMD-2 locomotor subtest based on Inertial Measurement Units (IMUs) to quantify MC in children rapidly and objectively. 45 typically developing children, aged 6-10, performed the TGMD-2 locomotor subtest (six skills). During the tests, children wore five IMUs mounted on lower back, on ankles and on wrists. Sensor and video recordings of the tests were collected. Three expert evaluators performed the standard assessment of TGMD-2. Using theoretical and modelling approaches, algorithms were implemented to automatically score children tests based on IMUs' data. The automatic assessment, compared to the standard one, showed an agreement higher than 87% on average on the entire group for each skill and a reduction of time for scoring from 15 to 2min per participant. Results support the use of IMUs for MC assessment: this approach will allow improving the usability of MC assessment, supporting objectively evaluator decisions and reducing time requirement for the evaluation of large groups. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. A power supply design of body sensor networks for health monitoring of neonates

    NARCIS (Netherlands)

    Chen, W.; Sonntag, C.L.W.; Boesten, F.; Bambang Oetomo, S.; Feijs, L.M.G.

    2008-01-01

    Critically ill new born babies are extremely tiny and vulnerable to external disturbance. Non-invasive health monitoring with body sensor networks is crucial for the survival of these neonates and the quality of their life later on. A key question for health monitoring with body sensor networks is

  9. A Hierarchical Approach to Real-time Activity Recognition in Body Sensor Networks

    DEFF Research Database (Denmark)

    Wang, Liang; Gu, Tao; Tao, Xianping

    2012-01-01

    Real-time activity recognition in body sensor networks is an important and challenging task. In this paper, we propose a real-time, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this model, we rst use a fast and lightweight al...

  10. Real-time Human Activity Recognition using a Body Sensor Network

    DEFF Research Database (Denmark)

    Wang, Liang; Gu, Tao; Chen, Hanhua

    2010-01-01

    Real-time activity recognition using body sensor networks is an important and challenging task and it has many potential applications. In this paper, we propose a realtime, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this mo...

  11. The Use of Body Worn Sensors for Detecting the Vibrations Acting on the Lower Back in Alpine Ski Racing

    Directory of Open Access Journals (Sweden)

    Jörg Spörri

    2017-07-01

    Full Text Available This study explored the use of body worn sensors to evaluate the vibrations that act on the human body in alpine ski racing from a general and a back overuse injury prevention perspective. In the course of a biomechanical field experiment, six male European Cup-level athletes each performed two runs on a typical giant slalom (GS and slalom (SL course, resulting in a total of 192 analyzed turns. Three-dimensional accelerations were measured by six inertial measurement units placed on the right and left shanks, right and left thighs, sacrum, and sternum. Based on these data, power spectral density (PSD; i.e., the signal's power distribution over frequency was determined for all segments analyzed. Additionally, as a measure expressing the severity of vibration exposure, root-mean-square (RMS acceleration acting on the lower back was calculated based on the inertial acceleration along the sacrum's longitudinal axis. In both GS and SL skiing, the PSD values of the vibrations acting at the shank were found to be largest for frequencies below 30 Hz. While being transmitted through the body, these vibrations were successively attenuated by the knee and hip joint. At the lower back (i.e., sacrum sensor, PSD values were especially pronounced for frequencies between 4 and 10 Hz, whereas a corresponding comparison between GS and SL revealed higher PSD values and larger RMS values for GS. Because vibrations in this particular range (i.e., 4 to 10 Hz include the spine's resonant frequency and are known to increase the risk of structural deteriorations/abnormalities of the spine, they may be considered potential components of mechanisms leading to overuse injuries of the back in alpine ski racing. Accordingly, any measure to control and/or reduce such skiing-related vibrations to a minimum should be recognized and applied. In this connection, wearable sensor technologies might help to better monitor and manage the overall back overuse-relevant vibration

  12. Mobile App to Streamline the Development of Wearable Sensor-Based Exercise Biofeedback Systems: System Development and Evaluation.

    Science.gov (United States)

    O'Reilly, Martin; Duffin, Joe; Ward, Tomas; Caulfield, Brian

    2017-08-21

    Biofeedback systems that use inertial measurement units (IMUs) have been shown recently to have the ability to objectively assess exercise technique. However, there are a number of challenges in developing such systems; vast amounts of IMU exercise datasets must be collected and manually labeled for each exercise variation, and naturally occurring technique deviations may not be well detected. One method of combatting these issues is through the development of personalized exercise technique classifiers. We aimed to create a tablet app for physiotherapists and personal trainers that would automate the development of personalized multiple and single IMU-based exercise biofeedback systems for their clients. We also sought to complete a preliminary investigation of the accuracy of such individualized systems in a real-world evaluation. A tablet app was developed that automates the key steps in exercise technique classifier creation through synchronizing video and IMU data collection, automatic signal processing, data segmentation, data labeling of segmented videos by an exercise professional, automatic feature computation, and classifier creation. Using a personalized single IMU-based classification system, 15 volunteers (12 males, 3 females, age: 23.8 [standard deviation, SD 1.8] years, height: 1.79 [SD 0.07] m, body mass: 78.4 [SD 9.6] kg) then completed 4 lower limb compound exercises. The real-world accuracy of the systems was evaluated. The tablet app successfully automated the process of creating individualized exercise biofeedback systems. The personalized systems achieved 89.50% (1074/1200) accuracy, with 90.00% (540/600) sensitivity and 89.00% (534/600) specificity for assessing aberrant and acceptable technique with a single IMU positioned on the left thigh. A tablet app was developed that automates the process required to create a personalized exercise technique classification system. This tool can be applied to any cyclical, repetitive exercise. The

  13. Signal transmission in a human body medium-based body sensor network using a Mach-Zehnder electro-optical sensor.

    Science.gov (United States)

    Song, Yong; Hao, Qun; Zhang, Kai; Wang, Jingwen; Jin, Xuefeng; Sun, He

    2012-11-30

    The signal transmission technology based on the human body medium offers significant advantages in Body Sensor Networks (BSNs) used for healthcare and the other related fields. In previous works we have proposed a novel signal transmission method based on the human body medium using a Mach-Zehnder electro-optical (EO) sensor. In this paper, we present a signal transmission system based on the proposed method, which consists of a transmitter, a Mach-Zehnder EO sensor and a corresponding receiving circuit. Meanwhile, in order to verify the frequency response properties and determine the suitable parameters of the developed system, in-vivo measurements have been implemented under conditions of different carrier frequencies, baseband frequencies and signal transmission paths. Results indicate that the proposed system will help to achieve reliable and high speed signal transmission of BSN based on the human body medium.

  14. Distinguishing the causes of falls in humans using an array of wearable tri-axial accelerometers.

    Science.gov (United States)

    Aziz, Omar; Park, Edward J; Mori, Greg; Robinovitch, Stephen N

    2014-01-01

    Falls are the number one cause of injury in older adults. Lack of objective evidence on the cause and circumstances of falls is often a barrier to effective prevention strategies. Previous studies have established the ability of wearable miniature inertial sensors (accelerometers and gyroscopes) to automatically detect falls, for the purpose of delivering medical assistance. In the current study, we extend the applications of this technology, by developing and evaluating the accuracy of wearable sensor systems for determining the cause of falls. Twelve young adults participated in experimental trials involving falls due to seven causes: slips, trips, fainting, and incorrect shifting/transfer of body weight while sitting down, standing up from sitting, reaching and turning. Features (means and variances) of acceleration data acquired from four tri-axial accelerometers during the falling trials were input to a linear discriminant analysis technique. Data from an array of three sensors (left ankle+right ankle+sternum) provided at least 83% sensitivity and 89% specificity in classifying falls due to slips, trips, and incorrect shift of body weight during sitting, reaching and turning. Classification of falls due to fainting and incorrect shift during rising was less successful across all sensor combinations. Furthermore, similar classification accuracy was observed with data from wearable sensors and a video-based motion analysis system. These results establish a basis for the development of sensor-based fall monitoring systems that provide information on the cause and circumstances of falls, to direct fall prevention strategies at a patient or population level. Copyright © 2013 Elsevier B.V. All rights reserved.

  15. Convergence Revolution Comes to Wearables: Multiple Advances are Taking Biosensor Networks to the Next Level in Health Care.

    Science.gov (United States)

    Mertz, Leslie

    2016-01-01

    In the field of wearable biomedical sensors, the convergence revolution is more than a fanciful, utopian view of the way innovation should be done. Medical-grade wearable sensors rely on it. Their development requires technical know-how, computing expertise, clinical input, and collaboration-a true meeting of the minds to permit the conversion of wearables from neat gadgets into practical and proficient tools that will propel health care to new heights. Beyond the increasing miniaturization of hardware and the shift to wireless communication technology, flexible electronics and more powerful computing capabilities, including application specific integrated circuits (ASICs) and microelectromechanical systems (MEMS), have enabled new work on body sensor networks (BSNs) that monitor, analyze, and make sense of body signals for the prevention, diagnosis, and treatment of health disorders. Developments in processing, such as sensor-connected nodes combined with evolving algorithms and decreasing power requirements, have also contributed. In addition, new approaches to subjective measures (pain and emotion) have opened possibilities.

  16. Towards the development of a wearable Electrical Impedance Tomography system: A study about the suitability of a low power bioimpedance front-end.

    Science.gov (United States)

    Menolotto, Matteo; Rossi, Stefano; Dario, Paolo; Della Torre, Luigi

    2015-01-01

    Wearable systems for remote monitoring of physiological parameter are ready to evolve towards wearable imaging systems. The Electrical Impedance Tomography (EIT) allows the non-invasive investigation of the internal body structure. The characteristics of this low-resolution and low-cost technique match perfectly with the concept of a wearable imaging device. On the other hand low power consumption, which is a mandatory requirement for wearable systems, is not usually discussed for standard EIT applications. In this work a previously developed low power architecture for a wearable bioimpedance sensor is applied to EIT acquisition and reconstruction, to evaluate the impact on the image of the limited signal to noise ratio (SNR), caused by low power design. Some anatomical models of the chest, with increasing geometric complexity, were developed, in order to evaluate and calibrate, through simulations, the parameters of the reconstruction algorithms provided by Electrical Impedance Diffuse Optical Reconstruction Software (EIDORS) project. The simulation results were compared with experimental measurements taken with our bioimpedance device on a phantom reproducing chest tissues properties. The comparison was both qualitative and quantitative through the application of suitable figures of merit; in this way the impact of the noise of the low power front-end on the image quality was assessed. The comparison between simulation and measurement results demonstrated that, despite the limited SNR, the device is accurate enough to be used for the development of an EIT based imaging wearable system.

  17. Evaluating and improving the performance of thin film force sensors within body and device interfaces.

    Science.gov (United States)

    Likitlersuang, Jirapat; Leineweber, Matthew J; Andrysek, Jan

    2017-10-01

    Thin film force sensors are commonly used within biomechanical systems, and at the interface of the human body and medical and non-medical devices. However, limited information is available about their performance in such applications. The aims of this study were to evaluate and determine ways to improve the performance of thin film (FlexiForce) sensors at the body/device interface. Using a custom apparatus designed to load the sensors under simulated body/device conditions, two aspects were explored relating to sensor calibration and application. The findings revealed accuracy errors of 23.3±17.6% for force measurements at the body/device interface with conventional techniques of sensor calibration and application. Applying a thin rigid disc between the sensor and human body and calibrating the sensor using compliant surfaces was found to substantially reduce measurement errors to 2.9±2.0%. The use of alternative calibration and application procedures is recommended to gain acceptable measurement performance from thin film force sensors in body/device applications. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

  18. Early classification of pathological heartbeats on wireless body sensor nodes.

    Science.gov (United States)

    Braojos, Rubén; Beretta, Ivan; Ansaloni, Giovanni; Atienza, David

    2014-11-27

    Smart Wireless Body Sensor Nodes (WBSNs) are a novel class of unobtrusive, battery-powered devices allowing the continuous monitoring and real-time interpretation of a subject's bio-signals, such as the electrocardiogram (ECG). These low-power platforms, while able to perform advanced signal processing to extract information on heart conditions, are usually constrained in terms of computational power and transmission bandwidth. It is therefore essential to identify in the early stages which parts of an ECG are critical for the diagnosis and, only in these cases, activate on demand more detailed and computationally intensive analysis algorithms. In this work, we present a comprehensive framework for real-time automatic classification of normal and abnormal heartbeats, targeting embedded and resource-constrained WBSNs. In particular, we provide a comparative analysis of different strategies to reduce the heartbeat representation dimensionality, and therefore the required computational effort. We then combine these techniques with a neuro-fuzzy classification strategy, which effectively discerns normal and pathological heartbeats with a minimal run time and memory overhead. We prove that, by performing a detailed analysis only on the heartbeats that our classifier identifies as abnormal, a WBSN system can drastically reduce its overall energy consumption. Finally, we assess the choice of neuro-fuzzy classification by comparing its performance and workload with respect to other state-of-the-art strategies. Experimental results using the MIT-BIH Arrhythmia database show energy savings of as much as 60% in the signal processing stage, and 63% in the subsequent wireless transmission, when a neuro-fuzzy classification structure is employed, coupled with a dimensionality reduction technique based on random projections.

  19. Effects of Wearable Sensor-Based Balance and Gait Training on Balance, Gait, and Functional Performance in Healthy and Patient Populations: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

    Science.gov (United States)

    Gordt, Katharina; Gerhardy, Thomas; Najafi, Bijan; Schwenk, Michael

    2018-01-01

    Wearable sensors (WS) can accurately measure body motion and provide interactive feedback for supporting motor learning. This review aims to summarize current evidence for the effectiveness of WS training for improving balance, gait and functional performance. A systematic literature search was performed in PubMed, Cochrane, Web of Science, and CINAHL. Randomized controlled trials (RCTs) using a WS exercise program were included. Study quality was examined by the PEDro scale. Meta-analyses were conducted to estimate the effects of WS balance training on the most frequently reported outcome parameters. Eight RCTs were included (Parkinson n = 2, stroke n = 1, Parkinson/stroke n = 1, peripheral neuropathy n = 2, frail older adults n = 1, healthy older adults n = 1). The sample size ranged from n = 20 to 40. Three types of training paradigms were used: (1) static steady-state balance training, (2) dynamic steady-state balance training, which includes gait training, and (3) proactive balance training. RCTs either used one type of training paradigm (type 2: n = 1, type 3: n = 3) or combined different types of training paradigms within their intervention (type 1 and 2: n = 2; all types: n = 2). The meta-analyses revealed significant overall effects of WS training on static steady-state balance outcomes including mediolateral (eyes open: Hedges' g = 0.82, CI: 0.43-1.21; eyes closed: g = 0.57, CI: 0.14-0.99) and anterior-posterior sway (eyes open: g = 0.55, CI: 0.01-1.10; eyes closed: g = 0.44, CI: 0.02-0.86). No effects on habitual gait speed were found in the meta-analysis (g = -0.19, CI: -0.68 to 0.29). Two RCTs reported significant improvements for selected gait variables including single support time, and fast gait speed. One study identified effects on proactive balance (Alternate Step Test), but no effects were found for the Timed Up and Go test and the Berg Balance Scale. Two studies reported positive results on feasibility and usability. Only one study was

  20. Axial Permanent Magnet Generator for Wearable Energy Harvesting

    DEFF Research Database (Denmark)

    Högberg, Stig; Sødahl, Jakob Wagner; Mijatovic, Nenad

    2016-01-01

    An increasing demand for battery-free electronics is evident by the rapid increase of wearable devices, and the design of wearable energy harvesters follows accordingly. An axial permanent magnet generator was designed to harvest energy from human body motion and supplying it to a wearable......W, respectively) with an iron yoke is subject to losses that exceed the realistic input power, and was therefore deemed infeasible. A generator without the iron yoke was concluded to perform well as a wearable energy harvester. An experimental investigation of a prototype revealed an output power of almost 1 m...

  1. Noninvasive, three-dimensional full-field body sensor for surface deformation monitoring of human body in vivo

    Science.gov (United States)

    Chen, Zhenning; Shao, Xinxing; He, Xiaoyuan; Wu, Jialin; Xu, Xiangyang; Zhang, Jinlin

    2017-09-01

    Noninvasive, three-dimensional (3-D), full-field surface deformation measurements of the human body are important for biomedical investigations. We proposed a 3-D noninvasive, full-field body sensor based on stereo digital image correlation (stereo-DIC) for surface deformation monitoring of the human body in vivo. First, by applying an improved water-transfer printing (WTP) technique to transfer optimized speckle patterns onto the skin, the body sensor was conveniently and harmlessly fabricated directly onto the human body. Then, stereo-DIC was used to achieve 3-D noncontact and noninvasive surface deformation measurements. The accuracy and efficiency of the proposed body sensor were verified and discussed by considering different complexions. Moreover, the fabrication of speckle patterns on human skin, which has always been considered a challenging problem, was shown to be feasible, effective, and harmless as a result of the improved WTP technique. An application of the proposed stereo-DIC-based body sensor was demonstrated by measuring the pulse wave velocity of human carotid artery.

  2. Noninvasive, three-dimensional full-field body sensor for surface deformation monitoring of human body in vivo.

    Science.gov (United States)

    Chen, Zhenning; Shao, Xinxing; He, Xiaoyuan; Wu, Jialin; Xu, Xiangyang; Zhang, Jinlin

    2017-09-01

    Noninvasive, three-dimensional (3-D), full-field surface deformation measurements of the human body are important for biomedical investigations. We proposed a 3-D noninvasive, full-field body sensor based on stereo digital image correlation (stereo-DIC) for surface deformation monitoring of the human body in vivo. First, by applying an improved water-transfer printing (WTP) technique to transfer optimized speckle patterns onto the skin, the body sensor was conveniently and harmlessly fabricated directly onto the human body. Then, stereo-DIC was used to achieve 3-D noncontact and noninvasive surface deformation measurements. The accuracy and efficiency of the proposed body sensor were verified and discussed by considering different complexions. Moreover, the fabrication of speckle patterns on human skin, which has always been considered a challenging problem, was shown to be feasible, effective, and harmless as a result of the improved WTP technique. An application of the proposed stereo-DIC-based body sensor was demonstrated by measuring the pulse wave velocity of human carotid artery. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

  3. Power optimization in body sensor networks: the case of an autonomous wireless EMG sensor powered by PV-cells.

    Science.gov (United States)

    Penders, J; Pop, V; Caballero, L; van de Molengraft, J; van Schaijk, R; Vullers, R; Van Hoof, C

    2010-01-01

    Recent advances in ultra-low-power circuits and energy harvesters are making self-powered body sensor nodes a reality. Power optimization at the system and application level is crucial in achieving ultra-low-power consumption for the entire system. This paper reviews system-level power optimization techniques, and illustrates their impact on the case of autonomous wireless EMG monitoring. The resulting prototype, an Autonomous wireless EMG sensor power by PV-cells, is presented.

  4. Toward a Fault Tolerant Architecture for Vital Medical-Based Wearable Computing.

    Science.gov (United States)

    Abdali-Mohammadi, Fardin; Bajalan, Vahid; Fathi, Abdolhossein

    2015-12-01

    Advancements in computers and electronic technologies have led to the emergence of a new generation of efficient small intelligent systems. The products of such technologies might include Smartphones and wearable devices, which have attracted the attention of medical applications. These products are used less in critical medical applications because of their resource constraint and failure sensitivity. This is due to the fact that without safety considerations, small-integrated hardware will endanger patients' lives. Therefore, proposing some principals is required to construct wearable systems in healthcare so that the existing concerns are dealt with. Accordingly, this paper proposes an architecture for constructing wearable systems in critical medical applications. The proposed architecture is a three-tier one, supporting data flow from body sensors to cloud. The tiers of this architecture include wearable computers, mobile computing, and mobile cloud computing. One of the features of this architecture is its high possible fault tolerance due to the nature of its components. Moreover, the required protocols are presented to coordinate the components of this architecture. Finally, the reliability of this architecture is assessed by simulating the architecture and its components, and other aspects of the proposed architecture are discussed.

  5. Towards Implantable Body Sensor Networks - Performance of MICS Band Radio Communication in Animal Tissue

    NARCIS (Netherlands)

    Karuppiah Ramachandran, Vignesh Raja; Meratnia, Nirvana; Zhang, Kui; Havinga, Paul J.M.

    Reliable wireless communication inside the human body is crucial for the design of implantable body sensor networks (IBSN). The tissues in human body are heterogeneous and have dierent conductivity and permitivity, which make the modeling of the wireless channel challenging. The design of upper

  6. Wearable probes for service design

    DEFF Research Database (Denmark)

    Mullane, Aaron; Laaksolahti, Jarmo Matti; Svanæs, Dag

    2014-01-01

    Probes are used as a design method in user-centred design to allow end-users to inform design by collecting data from their lives. Probes are potentially useful in service innovation, but current probing methods require users to interrupt their activity and are consequently not ideal for use...... by service employees in reflecting on the delivery of a service. In this paper, we present the ‘wearable probe’, a probe concept that captures sensor data without distracting service employees. Data captured by the probe can be used by the service employees to reflect and co-reflect on the service journey......, helping to identify opportunities for service evolution and innovation....

  7. Implantable Body Sensor Network MAC Protocols Using Wake-up Radio – Evaluation in Animal Tissue

    NARCIS (Netherlands)

    Karuppiah Ramachandran, Vignesh Raja; van der Zwaag, B.J.; Meratnia, Nirvana; Havinga, Paul J.M.

    Applications of implantable sensor networks in the health-care industry have increased tremendously over the last decade. There are different types of medium access control (MAC) protocols that are designed for implantable body sensor networks, using different physical layer technologies such as

  8. WEARABLE ELECTRONICS IN THE NEXT YEARS

    Directory of Open Access Journals (Sweden)

    PRINIOTAKIS George

    2015-05-01

    Full Text Available The term ‘Wearable Technologies’, ‘Wearable Electronics’, or ‘Smart Garments’, is associated to those clothing and soft or hard accessories which integrate electronic components, or which are made of smart textiles. Smart textiles research represents a new model for generating creative and novel solutions for integrating electronics into unusual environments and will result in new discoveries that push the boundaries of science forward. Last few years there are several hundreds or maybe thousands of research teams that works and develop such materials and products. But the key driver of the success of the wearable electronics is the acceptance from the end user. It is estimates that only for the next three years the sales in the wearable will be almost multiply by ten times. The flexible wearable computer industry's patent applications arrived at 429 in the second quarter of 2014, up 27.7% year on year, and witnessed a record high in the report's tracking period starting from the first quarter of 2012. The market has already in the shelf commercial products as wristbands (Fitness/well-being/sports devices, smart jewels, smart watches, mobile health devices, tech clothing, and augmented reality glasses. The recently developed enabling technologies eliminates the barriers and help the scientists and developers to launch new types of "wearable". The life style of a large share of population, the low cost of 3D printing for rapid prototyping locally, the large available platforms, the lower cost of sensors and components give a an impetus for large scale of products. In the same time the direct ordering channels to manufacturers of components facilitates the small producers and the scientists for prototype development. In this article we identify key challenges for the success of the wearable and we provide an outlook over the field and a prediction for the near future.

  9. Embodying Soft Wearables Research

    DEFF Research Database (Denmark)

    Tomico, Oscar; Wilde, Danielle

    2016-01-01

    of soft wearables. Throughout, we will experiment with how embodied design research techniques might be shared, developed, and used as direct and unmediated vehicles for their own reporting. Rather than engage in oral presentations, participants will lead each other through a proven embodied method...... and knowledge transfer in the context of soft wearables....

  10. Wearable 3D measurement

    Science.gov (United States)

    Manabe, Yoshitsugu; Imura, Masataka; Tsuchiya, Masanobu; Yasumuro, Yoshihiro; Chihara, Kunihiro

    2003-01-01

    Wearable 3D measurement realizes to acquire 3D information of an objects or an environment using a wearable computer. Recently, we can send voice and sound as well as pictures by mobile phone in Japan. Moreover it will become easy to capture and send data of short movie by it. On the other hand, the computers become compact and high performance. And it can easy connect to Internet by wireless LAN. Near future, we can use the wearable computer always and everywhere. So we will be able to send the three-dimensional data that is measured by wearable computer as a next new data. This paper proposes the measurement method and system of three-dimensional data of an object with the using of wearable computer. This method uses slit light projection for 3D measurement and user"s motion instead of scanning system.

  11. A comprehensive survey of energy-aware routing protocols in wireless body area sensor networks.

    Science.gov (United States)

    Effatparvar, Mehdi; Dehghan, Mehdi; Rahmani, Amir Masoud

    2016-09-01

    Wireless body area sensor network is a special purpose wireless sensor network that, employing wireless sensor nodes in, on, or around the human body, makes it possible to measure biological parameters of a person for specific applications. One of the most fundamental concerns in wireless body sensor networks is accurate routing in order to send data promptly and properly, and therefore overcome some of the challenges. Routing protocols for such networks are affected by a large number of factors including energy, topology, temperature, posture, the radio range of sensors, and appropriate quality of service in sensor nodes. Since energy is highly important in wireless body area sensor networks, and increasing the network lifetime results in benefiting greatly from sensor capabilities, improving routing performance with reduced energy consumption presents a major challenge. This paper aims to study wireless body area sensor networks and the related routing methods. It also presents a thorough, comprehensive review of routing methods in wireless body area sensor networks from the perspective of energy. Furthermore, different routing methods affecting the parameter of energy will be classified and compared according to their advantages and disadvantages. In this paper, fundamental concepts of wireless body area sensor networks are provided, and then the advantages and disadvantages of these networks are investigated. Since one of the most fundamental issues in wireless body sensor networks is to perform routing so as to transmit data precisely and promptly, we discuss the same issue. As a result, we propose a classification of the available relevant literature with respect to the key challenge of energy in the routing process. With this end in view, all important papers published between 2000 and 2015 are classified under eight categories including 'Mobility-Aware', 'Thermal-Aware', 'Restriction of Location and Number of Relays', 'Link-aware', 'Cluster- and Tree

  12. Detecting gunshots using wearable accelerometers.

    Directory of Open Access Journals (Sweden)

    Charles E Loeffler

    Full Text Available Gun violence continues to be a staggering and seemingly intractable issue in many communities. The prevalence of gun violence among the sub-population of individuals under court-ordered community supervision provides an opportunity for intervention using remote monitoring technology. Existing monitoring systems rely heavily on location-based monitoring methods, which have incomplete geographic coverage and do not provide information on illegal firearm use. This paper presents the first results demonstrating the feasibility of using wearable inertial sensors to recognize wrist movements and other signals corresponding to firearm usage. Data were collected from accelerometers worn on the wrists of subjects shooting a number of different firearms, conducting routine daily activities, and participating in activities and tasks that could be potentially confused with firearm discharges. A training sample was used to construct a combined detector and classifier for individual gunshots, which achieved a classification accuracy of 99.4 percent when tested against a hold-out sample of observations. These results suggest the feasibility of using inexpensive wearable sensors to detect firearm discharges.

  13. Piezoresistive Carbon-based Hybrid Sensor for Body-Mounted Biomedical Applications

    Science.gov (United States)

    Melnykowycz, M.; Tschudin, M.; Clemens, F.

    2017-02-01

    For body-mounted sensor applications, the evolution of soft condensed matter sensor (SCMS) materials offer conformability andit enables mechanical compliance between the body surface and the sensing mechanism. A piezoresistive hybrid sensor and compliant meta-material sub-structure provided a way to engineer sensor physical designs through modification of the mechanical properties of the compliant design. A piezoresistive fiber sensor was produced by combining a thermoplastic elastomer (TPE) matrix with Carbon Black (CB) particles in 1:1 mass ratio. Feedstock was extruded in monofilament fiber form (diameter of 300 microns), resulting in a highly stretchable sensor (strain sensor range up to 100%) with linear resistance signal response. The soft condensed matter sensor was integrated into a hybrid design including a 3D printed metamaterial structure combined with a soft silicone. An auxetic unit cell was chosen (with negative Poisson’s Ratio) in the design in order to combine with the soft silicon, which exhibits a high Poisson’s Ratio. The hybrid sensor design was subjected to mechanical tensile testing up to 50% strain (with gauge factor calculation for sensor performance), and then utilized for strain-based sensing applications on the body including gesture recognition and vital function monitoring including blood pulse-wave and breath monitoring. A 10 gesture Natural User Interface (NUI) test protocol was utilized to show the effectiveness of a single wrist-mounted sensor to identify discrete gestures including finger and hand motions. These hand motions were chosen specifically for Human Computer Interaction (HCI) applications. The blood pulse-wave signal was monitored with the hand at rest, in a wrist-mounted. In addition different breathing patterns were investigated, including normal breathing and coughing, using a belt and chest-mounted configuration.

  14. Body-Worn Sensors in Parkinson's Disease: Evaluating Their Acceptability to Patients.

    Science.gov (United States)

    Fisher, James M; Hammerla, Nils Y; Rochester, Lynn; Andras, Peter; Walker, Richard W

    2016-01-01

    Remote monitoring of symptoms in Parkinson's disease (PD) using body-worn sensors would assist treatment decisions and evaluation of new treatments. To date, a rigorous, systematic evaluation of the acceptability of body-worn sensors in PD has not been undertaken. Thirty-four participants wore bilateral wrist-worn sensors for 4 h in a research facility and then for 1 week at home. Participants' experiences of wearing the sensors were evaluated using a Likert-style questionnaire after each phase. Qualitative data were collected through free-text responses. Differences in responses between phases were assessed by using the Wilcoxon rank-sum test. Content analysis of qualitative data was undertaken. "Non-wear time" was estimated via analysis of accelerometer data for periods when sensors were stationary. After prolonged wearing there was a negative shift in participants' views on the comfort of the sensor; problems with the sensor's strap were highlighted. However, accelerometer data demonstrated high patient concordance with wearing of the sensors. There was no evidence that participants were less likely to wear the sensors in public. Most participants preferred wearing the sensors to completing symptom diaries. The finding that participants were not less likely to wear the sensors in public provides reassurance regarding the ecological validity of the data captured. The validity of our findings was strengthened by "triangulation" of data sources, enabling patients to express their agenda and repeated assessment after prolonged wearing. Long-term monitoring with wrist-worn sensors is acceptable to this cohort of PD patients. Evaluation of the wearer's experience is critical to the development of remote monitoring technology.

  15. Review of Wearable Device Technology and Its Applications to the Mining Industry

    Directory of Open Access Journals (Sweden)

    Mokhinabonu Mardonova

    2018-03-01

    Full Text Available This paper reviews current trends in wearable device technology, and provides an overview of its prevalent and potential deployments in the mining industry. This review includes the classification of wearable devices with some examples of their utilization in various industrial fields as well as the features of sensors used in wearable devices. Existing applications of wearable device technology to the mining industry are reviewed. In addition, a wearable safety management system for miners and other possible applications are proposed. The findings of this review show that by introducing wearable device technology to mining sites, the safety of mining operations can be enhanced. Therefore, wearable devices should be further used in the mining industry.

  16. Adaptive cancellation of motion artifact in wearable biosensors.

    Science.gov (United States)

    Yousefi, Rasoul; Nourani, Mehrdad; Panahi, Issa

    2012-01-01

    The performance of wearable biosensors is highly influenced by motion artifact. In this paper, a model is proposed for analysis of motion artifact in wearable photoplethysmography (PPG) sensors. Using this model, we proposed a robust real-time technique to estimate fundamental frequency and generate a noise reference signal. A Least Mean Square (LMS) adaptive noise canceler is then designed and validated using our synthetic noise generator. The analysis and results on proposed technique for noise cancellation shows promising performance.

  17. A wireless pH sensor using magnetoelasticity for measurement of body fluid acidity.

    Science.gov (United States)

    Pang, Pengfei; Gao, Xianjuan; Xiao, Xilin; Yang, Wenyue; Cai, Qingyun; Yao, Shouzhuo

    2007-04-01

    The determination of body fluid acidity using a wireless magnetoelastic pH-sensitive sensor is described. The sensor was fabricated by casting a layer of pH-sensitive polymer on a magnetoelastic ribbon. In response to an externally applied time-varying magnetic field, the magnetoelastic sensor mechanically vibrates at a characteristic frequency that is inversely dependent upon the mass of the pH polymer film, which varies as the film swells and shrinks in response to pH. As the magnetoelastic sensor is magnetostrictive, the mechanical vibrations of the sensor launch magnetic flux that can be detected remotely using a pickup coil. The sensor can be used for direct measurements of body fluid acidity without a pretreatment of the sample by using a filtration membrane. A reversible and linear response was obtained between pH 5.0 and 8.0 with a measurement resolution of pH 0.1 and a slope of 0.2 kHz pH(-1). Since there are no physical connections between the sensor and the instrument, the sensor can be applied to in vivo and in situ monitoring of the physiological pH and its fluctuations.

  18. Design of Secure ECG-Based Biometric Authentication in Body Area Sensor Networks.

    Science.gov (United States)

    Peter, Steffen; Reddy, Bhanu Pratap; Momtaz, Farshad; Givargis, Tony

    2016-04-22

    Body area sensor networks (BANs) utilize wireless communicating sensor nodes attached to a human body for convenience, safety, and health applications. Physiological characteristics of the body, such as the heart rate or Electrocardiogram (ECG) signals, are promising means to simplify the setup process and to improve security of BANs. This paper describes the design and implementation steps required to realize an ECG-based authentication protocol to identify sensor nodes attached to the same human body. Therefore, the first part of the paper addresses the design of a body-area sensor system, including the hardware setup, analogue and digital signal processing, and required ECG feature detection techniques. A model-based design flow is applied, and strengths and limitations of each design step are discussed. Real-world measured data originating from the implemented sensor system are then used to set up and parametrize a novel physiological authentication protocol for BANs. The authentication protocol utilizes statistical properties of expected and detected deviations to limit the number of false positive and false negative authentication attempts. The result of the described holistic design effort is the first practical implementation of biometric authentication in BANs that reflects timing and data uncertainties in the physical and cyber parts of the system.

  19. Design of Secure ECG-Based Biometric Authentication in Body Area Sensor Networks

    Science.gov (United States)

    Peter, Steffen; Pratap Reddy, Bhanu; Momtaz, Farshad; Givargis, Tony

    2016-01-01

    Body area sensor networks (BANs) utilize wireless communicating sensor nodes attached to a human body for convenience, safety, and health applications. Physiological characteristics of the body, such as the heart rate or Electrocardiogram (ECG) signals, are promising means to simplify the setup process and to improve security of BANs. This paper describes the design and implementation steps required to realize an ECG-based authentication protocol to identify sensor nodes attached to the same human body. Therefore, the first part of the paper addresses the design of a body-area sensor system, including the hardware setup, analogue and digital signal processing, and required ECG feature detection techniques. A model-based design flow is applied, and strengths and limitations of each design step are discussed. Real-world measured data originating from the implemented sensor system are then used to set up and parametrize a novel physiological authentication protocol for BANs. The authentication protocol utilizes statistical properties of expected and detected deviations to limit the number of false positive and false negative authentication attempts. The result of the described holistic design effort is the first practical implementation of biometric authentication in BANs that reflects timing and data uncertainties in the physical and cyber parts of the system. PMID:27110785

  20. Design of Secure ECG-Based Biometric Authentication in Body Area Sensor Networks

    Directory of Open Access Journals (Sweden)

    Steffen Peter

    2016-04-01

    Full Text Available Body area sensor networks (BANs utilize wireless communicating sensor nodes attached to a human body for convenience, safety, and health applications. Physiological characteristics of the body, such as the heart rate or Electrocardiogram (ECG signals, are promising means to simplify the setup process and to improve security of BANs. This paper describes the design and implementation steps required to realize an ECG-based authentication protocol to identify sensor nodes attached to the same human body. Therefore, the first part of the paper addresses the design of a body-area sensor system, including the hardware setup, analogue and digital signal processing, and required ECG feature detection techniques. A model-based design flow is applied, and strengths and limitations of each design step are discussed. Real-world measured data originating from the implemented sensor system are then used to set up and parametrize a novel physiological authentication protocol for BANs. The authentication protocol utilizes statistical properties of expected and detected deviations to limit the number of false positive and false negative authentication attempts. The result of the described holistic design effort is the first practical implementation of biometric authentication in BANs that reflects timing and data uncertainties in the physical and cyber parts of the system.

  1. Design and implementation of a wearable healthcare monitoring system.

    Science.gov (United States)

    Sagahyroon, Assim; Raddy, Hazem; Ghazy, Ali; Suleman, Umair

    2009-01-01

    A wearable healthcare monitoring unit that integrates various technologies was developed to provide patients with the option of leading a healthy and independent life without risks or confinement to medical facilities. The unit consists of various sensors integrated to a microcontroller and attached to the patient's body, reading vital signs and transmitting these readings via a Bluetooth link to the patient's mobile phone. Short-Messaging-Service (SMS) is incorporated in the design to alert a physician in emergency cases. Additionally, an application program running on the mobile phone uses the internet to update (at regular intervals) the patient records in a hospital database with the most recent readings. To reduce development costs, the components used were both off-the-shelf and affordable.

  2. Reviving a medical wearable computer for teaching purposes.

    Science.gov (United States)

    Frenger, Paul

    2014-01-01

    In 1978 the author constructed a medical wearable computer using an early CMOS microprocessor and support chips. This device was targeted for use by health-conscious consumers and other early adopters. Its expandable functions included weight management, blood pressure control, diabetes care, medication reminders, smoking cessation, pediatric growth and development, simple medical database, digital communication with a doctor’s office and emergency alert system. Various physiological sensors could be plugged-into the calculator-sized chassis. The device was shown to investor groups but funding was not obtained; by 1992 the author ceased pursuing it. The Computing and Mathematics Chair at a local University, a NASA acquaintance, approached the author to mentor a CS capstone course for Summer 2012. With the author’s guidance, five students proceeded to convert this medical wearable computer design to an iPhone-based implementation using the Apple Xcode Developer Kit and other utilities. The final student device contained a body mass index (BMI) calculator, an emergency alert for 911 or other first responders, a medication reminder, a Doctor’s appointment feature, a medical database, medical Internet links, and a pediatric growth & development guide. The students’ final imple-mentation was successfully demonstrated on an actual iPhone 4 at the CS capstone meeting in mid-Summer.

  3. Wearable medical systems for p-Health.

    Science.gov (United States)

    Teng, Xiao-Fei; Zhang, Yuan-Ting; Poon, Carmen C Y; Bonato, Paolo

    2008-01-01

    Driven by the growing aging population, prevalence of chronic diseases, and continuously rising healthcare costs, the healthcare system is undergoing a fundamental transformation, from the conventional hospital-centered system to an individual-centered system. Current and emerging developments in wearable medical systems will have a radical impact on this paradigm shift. Advances in wearable medical systems will enable the accessibility and affordability of healthcare, so that physiological conditions can be monitored not only at sporadic snapshots but also continuously for extended periods of time, making early disease detection and timely response to health threats possible. This paper reviews recent developments in the area of wearable medical systems for p-Health. Enabling technologies for continuous and noninvasive measurements of vital signs and biochemical variables, advances in intelligent biomedical clothing and body area networks, approaches for motion artifact reduction, strategies for wearable energy harvesting, and the establishment of standard protocols for the evaluation of wearable medical devices are presented in this paper with examples of clinical applications of these technologies.

  4. Estimation of Joint Forces and Moments for the In-Run and Take-Off in Ski Jumping Based on Measurements with Wearable Inertial Sensors

    Directory of Open Access Journals (Sweden)

    Grega Logar

    2015-05-01

    Full Text Available This study uses inertial sensors to measure ski jumper kinematics and joint dynamics, which was until now only a part of simulation studies. For subsequent calculation of dynamics in the joints, a link-segment model was developed. The model relies on the recursive Newton–Euler inverse dynamics. This approach allowed the calculation of the ground reaction force at take-off. For the model validation, four ski jumpers from the National Nordic center performed a simulated jump in a laboratory environment on a force platform; in total, 20 jumps were recorded. The results fit well to the reference system, presenting small errors in the mean and standard deviation and small root-mean-square errors. The error is under 12% of the reference value. For field tests, six jumpers participated in the study; in total, 28 jumps were recorded. All of the measured forces and moments were within the range of prior simulated studies. The proposed system was able to indirectly provide the values of forces and moments in the joints of the ski-jumpers’ body segments, as well as the ground reaction force during the in-run and take-off phases in comparison to the force platform installed on the table. Kinematics assessment and estimation of dynamics parameters can be applied to jumps from any ski jumping hill.

  5. Estimation of joint forces and moments for the in-run and take-off in ski jumping based on measurements with wearable inertial sensors.

    Science.gov (United States)

    Logar, Grega; Munih, Marko

    2015-05-13

    This study uses inertial sensors to measure ski jumper kinematics and joint dynamics, which was until now only a part of simulation studies. For subsequent calculation of dynamics in the joints, a link-segment model was developed. The model relies on the recursive Newton-Euler inverse dynamics. This approach allowed the calculation of the ground reaction force at take-off. For the model validation, four ski jumpers from the National Nordic center performed a simulated jump in a laboratory environment on a force platform; in total, 20 jumps were recorded. The results fit well to the reference system, presenting small errors in the mean and standard deviation and small root-mean-square errors. The error is under 12% of the reference value. For field tests, six jumpers participated in the study; in total, 28 jumps were recorded. All of the measured forces and moments were within the range of prior simulated studies. The proposed system was able to indirectly provide the values of forces and moments in the joints of the ski-jumpers' body segments, as well as the ground reaction force during the in-run and take-off phases in comparison to the force platform installed on the table. Kinematics assessment and estimation of dynamics parameters can be applied to jumps from any ski jumping hill.

  6. Highly Stretchable and Transparent Microfluidic Strain Sensors for Monitoring Human Body Motions.

    Science.gov (United States)

    Yoon, Sun Geun; Koo, Hyung-Jun; Chang, Suk Tai

    2015-12-16

    We report a new class of simple microfluidic strain sensors with high stretchability, transparency, sensitivity, and long-term stability with no considerable hysteresis and a fast response to various deformations by combining the merits of microfluidic techniques and ionic liquids. The high optical transparency of the strain sensors was achieved by introducing refractive-index matched ionic liquids into microfluidic networks or channels embedded in an elastomeric matrix. The microfluidic strain sensors offer the outstanding sensor performance under a variety of deformations induced by stretching, bending, pressing, and twisting of the microfluidic strain sensors. The principle of our microfluidic strain sensor is explained by a theoretical model based on the elastic channel deformation. In order to demonstrate its capability of practical usage, the simple-structured microfluidic strain sensors were performed onto a finger, wrist, and arm. The highly stretchable and transparent microfluidic strain sensors were successfully applied as potential platforms for distinctively monitoring a wide range of human body motions in real time. Our novel microfluidic strain sensors show great promise for making future stretchable electronic devices.

  7. DTN routing in body sensor networks with dynamic postural partitioning.

    Science.gov (United States)

    Quwaider, Muhannad; Biswas, Subir

    2010-11-01

    This paper presents novel store-and-forward packet routing algorithms for Wireless Body Area Networks ( WBAN ) with frequent postural partitioning. A prototype WBAN has been constructed for experimentally characterizing on-body topology disconnections in the presence of ultra short range radio links, unpredictable RF attenuation, and human postural mobility. On-body DTN routing protocols are then developed using a stochastic link cost formulation, capturing multi-scale topological localities in human postural movements. Performance of the proposed protocols are evaluated experimentally and via simulation, and are compared with a number of existing single-copy DTN routing protocols and an on-body packet flooding mechanism that serves as a performance benchmark with delay lower-bound. It is shown that via multi-scale modeling of the spatio-temporal locality of on-body link disconnection patterns, the proposed algorithms can provide better routing performance compared to a number of existing probabilistic, opportunistic, and utility-based DTN routing protocols in the literature.

  8. Estimating the orientation of a rigid body moving in space using inertial sensors

    Energy Technology Data Exchange (ETDEWEB)

    He, Peng, E-mail: peng.he.1@ulaval.ca; Cardou, Philippe, E-mail: pcardou@gmc.ulaval.ca [Université Laval, Robotics Laboratory, Department of Mechanical Engineering (Canada); Desbiens, André, E-mail: andre.desbiens@gel.ulaval.ca [Université Laval, Department of Electrical and Computer Engineering (Canada); Gagnon, Eric, E-mail: Eric.Gagnon@drdc-rddc.gc.ca [RDDC Valcartier (Canada)

    2015-09-15

    This paper presents a novel method of estimating the orientation of a rigid body moving in space from inertial sensors, by discerning the gravitational and inertial components of the accelerations. In this method, both a rigid-body kinematics model and a stochastic model of the human-hand motion are formulated and combined in a nonlinear state-space system. The state equation represents the rigid body kinematics and stochastic model, and the output equation represents the inertial sensor measurements. It is necessary to mention that, since the output equation is a nonlinear function of the state, the extended Kalman filter (EKF) is applied. The absolute value of the error from the proposed method is shown to be less than 5 deg in simulation and in experiments. It is apparently stable, unlike the time-integration of gyroscope measurements, which is subjected to drift, and remains accurate under large accelerations, unlike the tilt-sensor method.

  9. Estimating the orientation of a rigid body moving in space using inertial sensors

    International Nuclear Information System (INIS)

    He, Peng; Cardou, Philippe; Desbiens, André; Gagnon, Eric

    2015-01-01

    This paper presents a novel method of estimating the orientation of a rigid body moving in space from inertial sensors, by discerning the gravitational and inertial components of the accelerations. In this method, both a rigid-body kinematics model and a stochastic model of the human-hand motion are formulated and combined in a nonlinear state-space system. The state equation represents the rigid body kinematics and stochastic model, and the output equation represents the inertial sensor measurements. It is necessary to mention that, since the output equation is a nonlinear function of the state, the extended Kalman filter (EKF) is applied. The absolute value of the error from the proposed method is shown to be less than 5 deg in simulation and in experiments. It is apparently stable, unlike the time-integration of gyroscope measurements, which is subjected to drift, and remains accurate under large accelerations, unlike the tilt-sensor method

  10. Efficient Security Mechanisms for mHealth Applications Using Wireless Body Sensor Networks

    Directory of Open Access Journals (Sweden)

    Prasan Kumar Sahoo

    2012-09-01

    Full Text Available Recent technological advances in wireless communications and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices, which can be integrated into a Wireless Body Sensor Network (WBSN for health monitoring. Physiological signals of humans such as heartbeats, temperature and pulse can be monitored from a distant location using tiny biomedical wireless sensors. Hence, it is highly essential to combine the ubiquitous computing with mobile health technology using wireless sensors and smart phones to monitor the well-being of chronic patients such as cardiac, Parkinson and epilepsy patients. Since physiological data of a patient are highly sensitive, maintaining its confidentiality is highly essential. Hence, security is a vital research issue in mobile health (mHealth applications, especially if a patient has an embarrassing disease. In this paper a three tier security architecture for the mHealth application is proposed, in which light weight data confidentiality and authentication protocols are proposed to maintain the privacy of a patient. Moreover, considering the energy and hardware constraints of the wireless body sensors, low complexity data confidential and authentication schemes are designed. Performance evaluation of the proposed architecture shows that they can satisfy the energy and hardware limitations of the sensors and still can maintain the secure fabrics of the wireless body sensor networks. Besides, the proposed schemes can outperform in terms of energy consumption, memory usage and computation time over standard key establishment security scheme.

  11. Efficient Security Mechanisms for mHealth Applications Using Wireless Body Sensor Networks

    Science.gov (United States)

    Sahoo, Prasan Kumar

    2012-01-01

    Recent technological advances in wireless communications and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices, which can be integrated into a Wireless Body Sensor Network (WBSN) for health monitoring. Physiological signals of humans such as heartbeats, temperature and pulse can be monitored from a distant location using tiny biomedical wireless sensors. Hence, it is highly essential to combine the ubiquitous computing with mobile health technology using wireless sensors and smart phones to monitor the well-being of chronic patients such as cardiac, Parkinson and epilepsy patients. Since physiological data of a patient are highly sensitive, maintaining its confidentiality is highly essential. Hence, security is a vital research issue in mobile health (mHealth) applications, especially if a patient has an embarrassing disease. In this paper a three tier security architecture for the mHealth application is proposed, in which light weight data confidentiality and authentication protocols are proposed to maintain the privacy of a patient. Moreover, considering the energy and hardware constraints of the wireless body sensors, low complexity data confidential and authentication schemes are designed. Performance evaluation of the proposed architecture shows that they can satisfy the energy and hardware limitations of the sensors and still can maintain the secure fabrics of the wireless body sensor networks. Besides, the proposed schemes can outperform in terms of energy consumption, memory usage and computation time over standard key establishment security scheme. PMID:23112734

  12. Efficient security mechanisms for mHealth applications using wireless body sensor networks.

    Science.gov (United States)

    Sahoo, Prasan Kumar

    2012-01-01

    Recent technological advances in wireless communications and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices, which can be integrated into a Wireless Body Sensor Network (WBSN) for health monitoring. Physiological signals of humans such as heartbeats, temperature and pulse can be monitored from a distant location using tiny biomedical wireless sensors. Hence, it is highly essential to combine the ubiquitous computing with mobile health technology using wireless sensors and smart phones to monitor the well-being of chronic patients such as cardiac, Parkinson and epilepsy patients. Since physiological data of a patient are highly sensitive, maintaining its confidentiality is highly essential. Hence, security is a vital research issue in mobile health (mHealth) applications, especially if a patient has an embarrassing disease. In this paper a three tier security architecture for the mHealth application is proposed, in which light weight data confidentiality and authentication protocols are proposed to maintain the privacy of a patient. Moreover, considering the energy and hardware constraints of the wireless body sensors, low complexity data confidential and authentication schemes are designed. Performance evaluation of the proposed architecture shows that they can satisfy the energy and hardware limitations of the sensors and still can maintain the secure fabrics of the wireless body sensor networks. Besides, the proposed schemes can outperform in terms of energy consumption, memory usage and computation time over standard key establishment security scheme.

  13. Human++: Wireless autonomous sensor technology for body area networks

    NARCIS (Netherlands)

    Pop, V.; Francisco, R. de; Pflug, H.; Santana, J.; Visser, H.; Vullers, R.; Groot, H. de; Gyselinckx, B.

    2011-01-01

    Recent advances in ultra-low-power circuits and energy harvesters are making self-powered body wireless autonomous transducer solutions (WATS) a reality. Power optimization at the system and application level is crucial in achieving ultra-low-power consumption for the entire system. This paper deals

  14. Human++ : wireless autonomous sensor technology for body area networks

    NARCIS (Netherlands)

    Pop, V.; Francisco, de R.; Pflug, H.; Santana, J.; Visser, H.J.; Vullers, R.J.M.; Groot, de H.; Gyselinckx, B.

    2011-01-01

    Recent advances in ultra-low-power circuits and energy harvesters are making self-powered body wireless autonomous transducer solutions (WATS) a reality. Power optimization at the system and application level is crucial in achieving ultra-low-power consumption for the entire system. This paper deals

  15. Wearable Photoplethysmographic Sensors—Past and Present

    OpenAIRE

    Toshiyo Tamura; Yuka Maeda; Masaki Sekine; Masaki Yoshida

    2014-01-01

    Photoplethysmography (PPG) technology has been used to develop small, wearable, pulse rate sensors. These devices, consisting of infrared light-emitting diodes (LEDs) and photodetectors, offer a simple, reliable, low-cost means of monitoring the pulse rate noninvasively. Recent advances in optical technology have facilitated the use of high-intensity green LEDs for PPG, increasing the adoption of this measurement technique. In this review, we briefly present the history of PPG and recent deve...

  16. A wearable point-of-care system for home use that incorporates plug-and-play and wireless standards.

    Science.gov (United States)

    Yao, Jianchu; Schmitz, Ryan; Warren, Steve

    2005-09-01

    A point-of-care system for continuous health monitoring should be wearable, easy to use, and affordable to promote patient independence and facilitate acceptance of new home healthcare technology. Reconfigurability, interoperability, and scalability are important. Standardization supports these requirements, and encourages an open market where lower product prices result from vendor competition. This paper first discusses candidate standards for wireless communication, plug-and-play device interoperability, and medical information exchange in point-of-care systems. It then addresses the design and implementation of a wearable, plug-and-play system for home care which adopts the IEEE 1073 Medical Information Bus (MIB) standards, and uses Bluetooth as the wireless communication protocol. This standards-based system maximizes user mobility by incorporating a three-level architecture populated by base stations, wearable data loggers, and wearable sensors. Design issues include the implementation of the MIB standards on microcontroller-driven embedded devices, low power consumption, wireless data exchange, and data storage and transmission in a reconfigurable body-area network.

  17. Discovery of a Sweet Spot on the Foot with a Smart Wearable Soccer Boot Sensor That Maximizes the Chances of Scoring a Curved Kick in Soccer.

    Science.gov (United States)

    Fuss, Franz Konstantin; Düking, Peter; Weizman, Yehuda

    2018-01-01

    This paper provides the evidence of a sweet spot on the boot/foot as well as the method for detecting it with a wearable pressure sensitive device. This study confirmed the hypothesized existence of sweet and dead spots on a soccer boot or foot when kicking a ball. For a stationary curved kick, kicking the ball at the sweet spot maximized the probability of scoring a goal (58-86%), whereas having the impact point at the dead zone minimized the probability (11-22%). The sweet spot was found based on hypothesized favorable parameter ranges (center of pressure in x/y-directions and/or peak impact force) and the dead zone based on hypothesized unfavorable parameter ranges. The sweet spot was rather concentrated, independent of which parameter combination was used (two- or three-parameter combination), whereas the dead zone, located 21 mm from the sweet spot, was more widespread.

  18. inertial orientation tracker having automatic drift compensation using an at rest sensor for tracking parts of a human body

    Science.gov (United States)

    Foxlin, Eric M. (Inventor)

    2004-01-01

    A self contained sensor apparatus generates a signal that corresponds to at least two of the three orientational aspects of yaw, pitch and roll of a human-scale body, relative to an external reference frame. A sensor generates first sensor signals that correspond to rotational accelerations or rates of the body about certain body axes. The sensor may be mounted to the body. Coupled to the sensor is a signal processor for generating orientation signals relative to the external reference frame that correspond to the angular rate or acceleration signals. The first sensor signals are impervious to interference from electromagnetic, acoustic, optical and mechanical sources. The sensors may be rate sensors. An integrator may integrate the rate signal over time. A drift compensator is coupled to the rate sensors and the integrator. The drift compensator may include a gravitational tilt sensor or a magnetic field sensor or both. A verifier periodically measures the orientation of the body by a means different from the drift sensitive sate sensors. The verifier may take into account characteristic features of human motion, such as stillness periods. The drift compensator may be, in part, a Kalman filter, which may utilize statistical data about human head motion.

  19. Observing the state of balance with a single upper-body sensor

    NARCIS (Netherlands)

    Paiman (student), Charlotte; Lemus Perez, D.S.; Short Sotero, D.M.; Vallery, H.

    2016-01-01

    The occurrence of falls is an urgent challenge in our aging society. For wearable devices that actively prevent falls or mitigate their consequences, a critical prerequisite is knowledge on the user’s current state of balance. To keep such wearable systems practical and to achieve high acceptance,

  20. Soft Sensors and Actuators based on Nanomaterials

    Science.gov (United States)

    Yao, Shanshan

    The focus of this research is using novel bottom-up synthesized nanomaterials and structures to build up devices for wearable sensors and soft actuators. The applications of the wearable sensors towards motion detection and health monitoring are investigated. In addition, flexible heaters for bimorph actuators and stretchable patches made of microgel depots containing drug-loaded nanoparticles (NPs) for stretch-triggered wearable drug delivery are studied. Considerable efforts have been made to achieve highly sensitive and wearable sensors that can simultaneously detect multiple stimuli such as stretch, pressure, temperature or touch. Highly stretchable multifunctional sensors that can detect strain (up to 50%), pressure (up to 1 MPa) and finger touch with good sensitivity, fast response time ( 40 ms) and good pressure mapping function were developed. The sensors were demonstrated for several wearable applications including monitoring thumb movements and knee motions, illustrating the potential utilities of such sensors in robotic systems, prosthetics, healthcare and flexible touch panels. In addition to mechanical sensors, a wearable skin hydration sensor made of silver nanowires (AgNWs) in a polydimethylsiloxane (PDMS) matrix was demonstrated based on skin impedance measurement. The hydration sensors were packaged into a flexible wristband for skin hydration monitoring and a chest patch consisting of a strain sensor, three electrocardiogram (ECG) electrodes and a skin hydration sensor for multimodal sensing. The wearable wristband and chest patch may be used for low-cost, wireless and continuous sensing of skin hydration and other health parameters. Two representative applications of the nanomaterials for soft actuators were investigated. In the first application on bimorph actuation, low-voltage and extremely flexible electrothermal bimorph actuators were fabricated in a simple, efficient and scalable process. The bimorph actuators were made of flexible Ag

  1. Interconnecting wearable devices with nano-biosensing implants through optical wireless communications

    Science.gov (United States)

    Johari, Pedram; Pandey, Honey; Jornet, Josep M.

    2018-02-01

    Major advancements in the fields of electronics, photonics and wireless communication have enabled the development of compact wearable devices, with applications in diverse domains such as fitness, wellness and medicine. In parallel, nanotechnology is enabling the development of miniature sensors that can detect events at the nanoscale with unprecedented accuracy. On this matter, in vivo implantable Surface Plasmon Resonance (SPR) nanosensors have been proposed to analyze circulating biomarkers in body fluids for the early diagnosis of a myriad of diseases, ranging from cardiovascular disorders to different types of cancer. In light of these results, in this paper, an architecture is proposed to bridge the gap between these two apparently disjoint paradigms, namely, the commercial wearable devices and the advanced nano-biosensing technologies. More specifically, this paper thoroughly assesses the feasibility of the wireless optical intercommunications of an SPR-based nanoplasmonic biochip -implanted subcutaneously in the wrist-, with a nanophotonic wearable smart band which is integrated by an array of nano-lasers and photon-detectors for distributed excitation and measurement of the nanoplasmonic biochip. This is done through a link budget analysis which captures the peculiarities of the intra-body optical channel at (sub) cellular level, the strength of the SPR nanosensor reflection, as well as the capabilities of the nanolasers (emission power, spectrum) and the nano photon-detectors (sensitivity and noise equivalent power). The proposed analysis guides the development of practical communication designs between the wearable devices and nano-biosensing implants, which paves the way through early-stage diagnosis of severe diseases.

  2. Flexible thermoelectric generator using bulk legs and liquid metal interconnects for wearable electronics

    International Nuclear Information System (INIS)

    Suarez, Francisco; Parekh, Dishit P.; Ladd, Collin; Vashaee, Daryoosh; Dickey, Michael D.; Öztürk, Mehmet C.

    2017-01-01

    Highlights: •Flexible thermoelectric generator (TEG) with bulk legs. •Flexible thermoelectric generator with liquid metal interconnects. •Flexible TEG with potential to match the performance of rigid TEGs. •Flexible TEG for wearable electronics. -- Abstract: Interest in wearable electronics for continuous, long-term health and performance monitoring is rapidly increasing. The reduction in power levels consumed by sensors and electronic circuits accompanied by the advances in energy harvesting methods allows for the realization of self-powered monitoring systems that do not have to rely on batteries. For wearable electronics, thermoelectric generators (TEGs) offer the unique ability to continuously convert body heat into usable energy. For body harvesting, it is preferable to have TEGs that are thin, soft and flexible. Unfortunately, the performances of flexible modules reported to date have been far behind those of their rigid counterparts. This is largely due to lower efficiencies of the thermoelectric materials, electrical or thermal parasitic losses and limitations on leg dimensions posed by the synthesis techniques. In this work, we present an entirely new approach and explore the possibility of using standard bulk legs in a flexible package. Bulk thermoelectric legs cut from solid ingots are far superior to thermoelectric materials synthesized using other techniques. A key enabler of the proposed technology is the use of EGaIn liquid metal interconnects, which not only provide extremely low interconnect resistance but also stretchability with self-healing, both of which are essential for flexible TE modules. The results suggest that this novel approach can finally produce flexible TEGs that have the potential to challenge the rigid TEGs and provide a pathway for the realization of self-powered wearable electronics.

  3. Measuring Accurate Body Parameters of Dressed Humans with Large-Scale Motion Using a Kinect Sensor

    Directory of Open Access Journals (Sweden)

    Sidan Du

    2013-08-01

    Full Text Available Non-contact human body measurement plays an important role in surveillance, physical healthcare, on-line business and virtual fitting. Current methods for measuring the human body without physical contact usually cannot handle humans wearing clothes, which limits their applicability in public environments. In this paper, we propose an effective solution that can measure accurate parameters of the human body with large-scale motion from a Kinect sensor, assuming that the people are wearing clothes. Because motion can drive clothes attached to the human body loosely or tightly, we adopt a space-time analysis to mine the information across the posture variations. Using this information, we recover the human body, regardless of the effect of clothes, and measure the human body parameters accurately. Experimental results show that our system can perform more accurate parameter estimation on the human body than state-of-the-art methods.

  4. Recent Progress on Flexible and Wearable Supercapacitors.

    Science.gov (United States)

    Xue, Qi; Sun, Jinfeng; Huang, Yan; Zhu, Minshen; Pei, Zengxia; Li, Hongfei; Wang, Yukun; Li, Na; Zhang, Haiyan; Zhi, Chunyi

    2017-12-01

    Recently, wearable electronic devices including electrical sensors, flexible displays, and health monitors have received considerable attention and experienced rapid progress. Wearable supercapacitors attract tremendous attention mainly due to their high stability, low cost, fast charging/discharging, and high efficiency; properties that render them value for developing fully flexible devices. In this Concept, the recent achievements and advances made in flexible and wearable supercapacitors are presented, especially highlighting the promising performances of yarn/fiber-shaped and planar supercapacitors. On the basis of their working mechanism, electrode materials including carbon-based materials, metal oxide-based materials, and conductive polymers with an emphasis on the performance-optimization method are introduced. The latest representative techniques and active materials of recently developed supercapacitors with superior performance are summarized. Furthermore, the designs of 1D and 2D electrodes are discussed according to their electrically conductive supporting materials. Finally, conclusions, challenges, and perspective in optimizing and developing the electrochemical performance and function of wearable supercapacitors for their practical utility are addressed. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. An Energy-Efficient ASIC for Wireless Body Sensor Networks in Medical Applications.

    Science.gov (United States)

    Xiaoyu Zhang; Hanjun Jiang; Lingwei Zhang; Chun Zhang; Zhihua Wang; Xinkai Chen

    2010-02-01

    An energy-efficient application-specific integrated circuit (ASIC) featured with a work-on-demand protocol is designed for wireless body sensor networks (WBSNs) in medical applications. Dedicated for ultra-low-power wireless sensor nodes, the ASIC consists of a low-power microcontroller unit (MCU), a power-management unit (PMU), reconfigurable sensor interfaces, communication ports controlling a wireless transceiver, and an integrated passive radio-frequency (RF) receiver with energy harvesting ability. The MCU, together with the PMU, provides quite flexible communication and power-control modes for energy-efficient operations. The always-on passive RF receiver with an RF energy harvesting block offers the sensor nodes the capability of work-on-demand with zero standby power. Fabricated in standard 0.18-¿m complementary metal-oxide semiconductor technology, the ASIC occupies a die area of 2 mm × 2.5 mm. A wireless body sensor network sensor-node prototype using this ASIC only consumes < 10-nA current under the passive standby mode, and < 10 ¿A under the active standby mode, when supplied by a 3-V battery.

  6. A wearable 3D motion sensing system integrated with a Bluetooth smart phone application: A system level overview

    KAUST Repository

    Karimi, Muhammad Akram; Shamim, Atif

    2018-01-01

    description of a wearable 3D motion sensor. The sensing mechanism is based upon well-established magnetic and inertial measurement unit (MIMU), which integrates accelerometer, gyroscope and magnetometer data. Two sensor boards have been integrated within a

  7. Research and development of smart wearable health applications: the challenge ahead.

    Science.gov (United States)

    Lymberis, Andreas

    2004-01-01

    Continuous monitoring of physiological and physical parameters is necessary for the assessment and management of personal health status. It can significantly contribute to the reduction of healthcare cost by avoiding unnecessary hospitalisations and ensuring that those who need urgent care get it sooner. In conjunction with cost-effective telemedicine platforms, ubiquitous health monitoring can significantly contribute to the enhancement of disease prevention and early diagnosis, disease management, treatment and home rehabilitation. Latest developments in the area of micro and nanotechnologies, information processing and wireless communication offer, today, the possibility for minimally (or non) invasive biomedical measurement but also wearable sensing, processing and data communication. Although the systems are being developed to satisfy specific user needs, a number of common critical issues have to be tackled to achieve reliable and acceptable smart health wearable applications e.g. biomedical sensors, user interface, clinical validation, data security and confidentiality, scenarios of use, decision support, user acceptance and business models. Major technological achievements have been realised the last few years. Cutting edge development combining functional clothing and integrated electronics open a new research area and possibilities for body sensing and communicating health parameters. This paper reviews the current status of research and development on smart wearable health systems and applications and discusses the outstanding issues and future challenges.

  8. Loose Coupling of Wearable-Based INSs with Automatic Heading Evaluation

    Directory of Open Access Journals (Sweden)

    Dina Bousdar Ahmed

    2017-11-01

    Full Text Available Position tracking of pedestrians by means of inertial sensors is a highly explored field of research. In fact, there are already many approaches to implement inertial navigation systems (INSs. However, most of them use a single inertial measurement unit (IMU attached to the pedestrian’s body. Since wearable-devices will be given items in the future, this work explores the implementation of an INS using two wearable-based IMUs. A loosely coupled approach is proposed to combine the outputs of wearable-based INSs. The latter are based on a pocket-mounted IMU and a foot-mounted IMU. The loosely coupled fusion combines the output of the two INSs not only when these outputs are least erroneous, but also automatically favoring the best output. This approach is named smart update. The main challenge is determining the quality of the heading estimation of each INS, which changes every time. In order to address this, a novel concept to determine the quality of the heading estimation is presented. This concept is subject to a patent application. The results show that the position error rate of the loosely coupled fusion is 10 cm/s better than either the foot INS’s or pocket INS’s error rate in 95% of the cases.

  9. Loose Coupling of Wearable-Based INSs with Automatic Heading Evaluation.

    Science.gov (United States)

    Bousdar Ahmed, Dina; Munoz Diaz, Estefania

    2017-11-03

    Position tracking of pedestrians by means of inertial sensors is a highly explored field of research. In fact, there are already many approaches to implement inertial navigation systems (INSs). However, most of them use a single inertial measurement unit (IMU) attached to the pedestrian's body. Since wearable-devices will be given items in the future, this work explores the implementation of an INS using two wearable-based IMUs. A loosely coupled approach is proposed to combine the outputs of wearable-based INSs. The latter are based on a pocket-mounted IMU and a foot-mounted IMU. The loosely coupled fusion combines the output of the two INSs not only when these outputs are least erroneous, but also automatically favoring the best output. This approach is named smart update. The main challenge is determining the quality of the heading estimation of each INS, which changes every time. In order to address this, a novel concept to determine the quality of the heading estimation is presented. This concept is subject to a patent application. The results show that the position error rate of the loosely coupled fusion is 10 cm/s better than either the foot INS's or pocket INS's error rate in 95% of the cases.

  10. An Adaptive Fault-Tolerant Communication Scheme for Body Sensor Networks

    Directory of Open Access Journals (Sweden)

    Zichuan Xu

    2010-10-01

    Full Text Available A high degree of reliability for critical data transmission is required in body sensor networks (BSNs. However, BSNs are usually vulnerable to channel impairments due to body fading effect and RF interference, which may potentially cause data transmission to be unreliable. In this paper, an adaptive and flexible fault-tolerant communication scheme for BSNs, namely AFTCS, is proposed. AFTCS adopts a channel bandwidth reservation strategy to provide reliable data transmission when channel impairments occur. In order to fulfill the reliability requirements of critical sensors, fault-tolerant priority and queue are employed to adaptively adjust the channel bandwidth allocation. Simulation results show that AFTCS can alleviate the effect of channel impairments, while yielding lower packet loss rate and latency for critical sensors at runtime.

  11. Piezoresistive Soft Condensed Matter Sensor for Body-Mounted Vital Function Applications

    Directory of Open Access Journals (Sweden)

    Mark Melnykowycz

    2016-03-01

    Full Text Available A soft condensed matter sensor (SCMS designed to measure strains on the human body is presented. The hybrid material based on carbon black (CB and a thermoplastic elastomer (TPE was bonded to a textile elastic band and used as a sensor on the human wrist to measure hand motion by detecting the movement of tendons in the wrist. Additionally it was able to track the blood pulse wave of a person, allowing for the determination of pulse wave peaks corresponding to the systole and diastole blood pressures in order to calculate the heart rate. Sensor characterization was done using mechanical cycle testing, and the band sensor achieved a gauge factor of 4–6.3 while displaying low signal relaxation when held at a strain levels. Near-linear signal performance was displayed when loading to successively higher strain levels up to 50% strain.

  12. Wearable Smart System for Visually Impaired People

    OpenAIRE

    Ali Jasim Ramadhan

    2018-01-01

    In this paper, we present a wearable smart system to help visually impaired persons (VIPs) walk by themselves through the streets, navigate in public places, and seek assistance. The main components of the system are a microcontroller board, various sensors, cellular communication and GPS modules, and a solar panel. The system employs a set of sensors to track the path and alert the user of obstacles in front of them. The user is alerted by a sound emitted through a buzzer and by vibrations o...

  13. A novel architectural concept for trustworthy and secure access to body sensor information

    NARCIS (Netherlands)

    Linnartz, J.P.M.G.; Groot, de J.A.; Lukkien, J.J.; Benz, H.; Vanhoof, K.; Ruan, D.; Li, T.; Wets, G.

    2009-01-01

    This work presents a Body Sensor Network (BSN) system architecture concept, which can be applied not only for medical purposes, but also for social applications,entertainment and lifestyle. Emphasises lies on keeping the user in control of his own, possible privacy-sensitive, data by offering a

  14. Body sensor networks for Mobile Health Monitoring: Experience in Europe and Australia

    NARCIS (Netherlands)

    Jones, Valerie M.; Gay, Valerie; Leijdekkers, Peter

    2009-01-01

    Remote ambulatory monitoring is widely seen as playing a key part in addressing the impending crisis in health care provision. We describe two mobile health solutions, one developed in the Netherlands and one in Australia. In both cases a patient’s biosignals are measured by means of a body sensor

  15. HACMAC: A reliable human activity-based medium access control for implantable body sensor networks

    NARCIS (Netherlands)

    Karuppiah Ramachandran, Vignesh Raja; Havinga, Paul J.M.; Meratnia, Nirvana

    Chronic care is an eminent application of implantable body sensor networks (IBSN). Performing physical activities such as walking, running, and sitting is unavoidable during the long-term monitoring of chronic-care patients. These physical activities cripple the radio frequency (RF) signal between

  16. Smart Sensors Enable Smart Air Conditioning Control

    Directory of Open Access Journals (Sweden)

    Chin-Chi Cheng

    2014-06-01

    Full Text Available In this study, mobile phones, wearable devices, temperature and human motion detectors are integrated as smart sensors for enabling smart air conditioning control. Smart sensors obtain feedback, especially occupants’ information, from mobile phones and wearable devices placed on human body. The information can be used to adjust air conditioners in advance according to humans’ intentions, in so-called intention causing control. Experimental results show that the indoor temperature can be controlled accurately with errors of less than ±0.1 °C. Rapid cool down can be achieved within 2 min to the optimized indoor capacity after occupants enter a room. It’s also noted that within two-hour operation the total compressor output of the smart air conditioner is 48.4% less than that of the one using On-Off control. The smart air conditioner with wearable devices could detect the human temperature and activity during sleep to determine the sleeping state and adjusting the sleeping function flexibly. The sleeping function optimized by the smart air conditioner with wearable devices could reduce the energy consumption up to 46.9% and keep the human health. The presented smart air conditioner could provide a comfortable environment and achieve the goals of energy conservation and environmental protection.

  17. Low-power secure body area network for vital sensors toward IEEE802.15.6.

    Science.gov (United States)

    Kuroda, Masahiro; Qiu, Shuye; Tochikubo, Osamu

    2009-01-01

    Many healthcare/medical services have started using personal area networks, such as Bluetooth and ZigBee; these networks consist of various types of vital sensors. These works focus on generalized functions for sensor networks that expect enough battery capacity and low-power CPU/RF (Radio Frequency) modules, but less attention to easy-to-use privacy protection. In this paper, we propose a commercially-deployable secure body area network (S-BAN) with reduced computational burden on a real sensor that has limited RAM/ROM sizes and CPU/RF power consumption under a light-weight battery. Our proposed S-BAN provides vital data ordering among sensors that are involved in an S-BAN and also provides low-power networking with zero-administration security by automatic private key generation. We design and implement the power-efficient media access control (MAC) with resource-constraint security in sensors. Then, we evaluate the power efficiency of the S-BAN consisting of small sensors, such as an accessory type ECG and ring-type SpO2. The evaluation of power efficiency of the S-BAN using real sensors convinces us in deploying S-BAN and will also help us in providing feedbacks to the IEEE802.15.6 MAC, which will be the standard for BANs.

  18. Wearable sensors for patient-specific boundary shape estimation to improve the forward model for electrical impedance tomography (EIT) of neonatal lung function.

    Science.gov (United States)

    Khor, Joo Moy; Tizzard, Andrew; Demosthenous, Andreas; Bayford, Richard

    2014-06-01

    Electrical impedance tomography (EIT) could be significantly advantageous to continuous monitoring of lung development in newborn and, in particular, preterm infants as it is non-invasive and safe to use within the intensive care unit. It has been demonstrated that accurate boundary form of the forward model is important to minimize artefacts in reconstructed electrical impedance images. This paper presents the outcomes of initial investigations for acquiring patient-specific thorax boundary information using a network of flexible sensors that imposes no restrictions on the patient's normal breathing and movements. The investigations include: (1) description of the basis of the reconstruction algorithms, (2) tests to determine a minimum number of bend sensors, (3) validation of two approaches to reconstruction and (4) an example of a commercially available bend sensor and its performance. Simulation results using ideal sensors show that, in the worst case, a total shape error of less than 6% with respect to its total perimeter can be achieved.

  19. Energy efficient medium access protocol for wireless medical body area sensor networks.

    Science.gov (United States)

    Omeni, O; Wong, A; Burdett, A J; Toumazou, C

    2008-12-01

    This paper presents a novel energy-efficient MAC Protocol designed specifically for wireless body area sensor networks (WBASN) focused towards pervasive healthcare applications. Wireless body area networks consist of wireless sensor nodes attached to the human body to monitor vital signs such as body temperature, activity or heart-rate. The network adopts a master-slave architecture, where the body-worn slave node periodically sends sensor readings to a central master node. Unlike traditional peer-to-peer wireless sensor networks, the nodes in this biomedical WBASN are not deployed in an ad hoc fashion. Joining a network is centrally managed and all communications are single-hop. To reduce energy consumption, all the sensor nodes are in standby or sleep mode until the centrally assigned time slot. Once a node has joined a network, there is no possibility of collision within a cluster as all communication is initiated by the central node and is addressed uniquely to a slave node. To avoid collisions with nearby transmitters, a clear channel assessment algorithm based on standard listen-before-transmit (LBT) is used. To handle time slot overlaps, the novel concept of a wakeup fallback time is introduced. Using single-hop communication and centrally controlled sleep/wakeup times leads to significant energy reductions for this application compared to more ldquoflexiblerdquo network MAC protocols such as 802.11 or Zigbee. As duty cycle is reduced, the overall power consumption approaches the standby power. The protocol is implemented in hardware as part of the Sensiumtrade system-on-chip WBASN ASIC, in a 0.13- mum CMOS process.

  20. DiNAMAC : A disruption tolerant, reinforcement learning-based Mac protocol for implantable body sensor networks

    NARCIS (Netherlands)

    Karuppiah Ramachandran, Vignesh Raja; Le Viet Duc, L Duc; Meratnia, Nirvana; Havinga, Paul J.M.

    Ongoing advancements in Body Sensor Networks (BSN) have enabled continuous health monitoring of chronically ill patients, with the use of implantable and body worn sensor nodes. Inevitable day-to-day activities such as walking, running, and sleeping cause severe disruptions in the wireless link

  1. A challenge for higher education: Wearable technology for fashion design departments

    Directory of Open Access Journals (Sweden)

    Elif Buğra Kuzu Demir

    2016-04-01

    Full Text Available As the technology is integrated into different disciplines, we witness how powerful it can be. Rather than standing in isolation, technology changes the nature of the field it arrives in. Wearable technologies in fashion design education is a good example for this. Wearable technology defined as lightweight, easy portable and wearable smart devices that have sensors and computing capabilities. The structure of wearable technologies has brought a new trend to fashion design area. Fashion design, as known to be a very dynamic application area, has already accepted the issue and started using the most powerful examples of wearable technologies already. However, although the stages are using wearable technologies, the schools that graduate fashion designers of the future are far beyond the capacity of the stages. It is therefore; this paper brings suggestions for the integration of technology into fashion design departments in Turkey and while doing this it tries to be country specific.

  2. A Survey on Temperature-Aware Routing Protocols in Wireless Body Sensor Networks

    Directory of Open Access Journals (Sweden)

    Sangman Moh

    2013-08-01

    Full Text Available The rapid growth of the elderly population in the world and the rising cost of healthcare impose big issues for healthcare and medical monitoring. A Wireless Body Sensor Network (WBSN is comprised of small sensor nodes attached inside, on or around a human body, the main purpose of which is to monitor the functions and surroundings of the human body. However, the heat generated by the node’s circuitry and antenna could cause damage to the human tissue. Therefore, in designing a routing protocol for WBSNs, it is important to reduce the heat by incorporating temperature into the routing metric. The main contribution of this paper is to survey existing temperature-aware routing protocols that have been proposed for WBSNs. In this paper, we present a brief overview of WBSNs, review the existing routing protocols comparatively and discuss challenging open issues in the design of routing protocols.

  3. Advances in wearable technology for rehabilitation.

    Science.gov (United States)

    Bonato, Paolo

    2009-01-01

    Assessing the impact of rehabilitation interventions on the real life of individuals is a key element of the decision-making process required to choose a rehabilitation strategy. In the past, therapists and physicians inferred the effectiveness of a given rehabilitation approach from observations performed in a clinical setting and self-reports by patients. Recent developments in wearable technology have provided tools to complement the information gathered by rehabilitation personnel via patient's direct observation and via interviews and questionnaires. A new generation of wearable sensors and systems has emerged that allows clinicians to gather measures in the home and community settings that capture patients' activity level and exercise compliance, the effectiveness of pharmacological interventions, and the ability of patients to perform efficiently specific motor tasks. Available unobtrusive sensors allow clinical personnel to monitor patients' movement and physiological data such as heart rate, respiratory rate, and oxygen saturation. Cell phone technology and the widespread access to the Internet provide means to implement systems designed to remotely monitor patients' status and optimize interventions based on individual responses to different rehabilitation approaches. This chapter summarizes recent advances in the field of wearable technology and presents examples of application of this technology in rehabilitation.

  4. Augmented reality som wearable

    DEFF Research Database (Denmark)

    Buhl, Mie; Rahn, Annette

    2015-01-01

    Artiklen omhandler design og implementering af Augmented Reality (AR) i form af en wearable i sygeplejerskeuddannelsens anatomiundervisning, mere specifikt undervisning i lungeanatomi og respiration, med fokus på potentialer for visuel læring. Projektet undersøger, hvordan en udviklet AR-applikat......Artiklen omhandler design og implementering af Augmented Reality (AR) i form af en wearable i sygeplejerskeuddannelsens anatomiundervisning, mere specifikt undervisning i lungeanatomi og respiration, med fokus på potentialer for visuel læring. Projektet undersøger, hvordan en udviklet AR...

  5. Battery-free, wireless sensors for full-body pressure and temperature mapping.

    Science.gov (United States)

    Han, Seungyong; Kim, Jeonghyun; Won, Sang Min; Ma, Yinji; Kang, Daeshik; Xie, Zhaoqian; Lee, Kyu-Tae; Chung, Ha Uk; Banks, Anthony; Min, Seunghwan; Heo, Seung Yun; Davies, Charles R; Lee, Jung Woo; Lee, Chi-Hwan; Kim, Bong Hoon; Li, Kan; Zhou, Yadong; Wei, Chen; Feng, Xue; Huang, Yonggang; Rogers, John A

    2018-04-04

    Thin, soft, skin-like sensors capable of precise, continuous measurements of physiological health have broad potential relevance to clinical health care. Use of sensors distributed over a wide area for full-body, spatiotemporal mapping of physiological processes would be a considerable advance for this field. We introduce materials, device designs, wireless power delivery and communication strategies, and overall system architectures for skin-like, battery-free sensors of temperature and pressure that can be used across the entire body. Combined experimental and theoretical investigations of the sensor operation and the modes for wireless addressing define the key features of these systems. Studies with human subjects in clinical sleep laboratories and in adjustable hospital beds demonstrate functionality of the sensors, with potential implications for monitoring of circadian cycles and mitigating risks for pressure-induced skin ulcers. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

  6. Human Body Parts Tracking and Kinematic Features Assessment Based on RSSI and Inertial Sensor Measurements

    Directory of Open Access Journals (Sweden)

    Gaddi Blumrosen

    2013-08-01

    Full Text Available Acquisition of patient kinematics in different environments plays an important role in the detection of risk situations such as fall detection in elderly patients, in rehabilitation of patients with injuries, and in the design of treatment plans for patients with neurological diseases. Received Signal Strength Indicator (RSSI measurements in a Body Area Network (BAN, capture the signal power on a radio link. The main aim of this paper is to demonstrate the potential of utilizing RSSI measurements in assessment of human kinematic features, and to give methods to determine these features. RSSI measurements can be used for tracking different body parts’ displacements on scales of a few centimeters, for classifying motion and gait patterns instead of inertial sensors, and to serve as an additional reference to other sensors, in particular inertial sensors. Criteria and analytical methods for body part tracking, kinematic motion feature extraction, and a Kalman filter model for aggregation of RSSI and inertial sensor were derived. The methods were verified by a set of experiments performed in an indoor environment. In the future, the use of RSSI measurements can help in continuous assessment of various kinematic features of patients during their daily life activities and enhance medical diagnosis accuracy with lower costs.

  7. Field-Based Optimal Placement of Antennas for Body-Worn Wireless Sensors

    Directory of Open Access Journals (Sweden)

    Łukasz Januszkiewicz

    2016-05-01

    Full Text Available We investigate a case of automated energy-budget-aware optimization of the physical position of nodes (sensors in a Wireless Body Area Network (WBAN. This problem has not been presented in the literature yet, as opposed to antenna and routing optimization, which are relatively well-addressed. In our research, which was inspired by a safety-critical application for firefighters, the sensor network consists of three nodes located on the human body. The nodes communicate over a radio link operating in the 2.4 GHz or 5.8 GHz ISM frequency band. Two sensors have a fixed location: one on the head (earlobe pulse oximetry and one on the arm (with accelerometers, temperature and humidity sensors, and a GPS receiver, while the position of the third sensor can be adjusted within a predefined region on the wearer’s chest. The path loss between each node pair strongly depends on the location of the nodes and is difficult to predict without performing a full-wave electromagnetic simulation. Our optimization scheme employs evolutionary computing. The novelty of our approach lies not only in the formulation of the problem but also in linking a fully automated optimization procedure with an electromagnetic simulator and a simplified human body model. This combination turns out to be a computationally effective solution, which, depending on the initial placement, has a potential to improve performance of our example sensor network setup by up to about 20 dB with respect to the path loss between selected nodes.

  8. The Baetylus Theorem-the central disconnect driving consumer behavior and investment returns in Wearable Technologies.

    Science.gov (United States)

    Levine, James A

    2016-08-01

    The Wearable Technology market may increase fivefold by the end of the decade. There is almost no academic investigation as to what drives the investment hypothesis in wearable technologies. This paper seeks to examine this issue from an evidence-based perspective. There is a fundamental disconnect in how consumers view wearable sensors and how companies market them; this is called The Baetylus Theorem where people believe (falsely) that by buying a wearable sensor they will receive health benefit; data suggest that this is not the case. This idea is grounded social constructs, psychological theories and marketing approaches. A marketing proposal that fails to recognize The Baetylus Theorem and how it can be integrated into a business offering has not optimized its competitive advantage. More importantly, consumers should not falsely believe that purchasing a wearable technology, improves health.

  9. The Baetylus Theorem—the central disconnect driving consumer behavior and investment returns in Wearable Technologies

    Science.gov (United States)

    Levine, James A.

    2016-01-01

    The Wearable Technology market may increase fivefold by the end of the decade. There is almost no academic investigation as to what drives the investment hypothesis in wearable technologies. This paper seeks to examine this issue from an evidence-based perspective. There is a fundamental disconnect in how consumers view wearable sensors and how companies market them; this is called The Baetylus Theorem where people believe (falsely) that by buying a wearable sensor they will receive health benefit; data suggest that this is not the case. This idea is grounded social constructs, psychological theories and marketing approaches. A marketing proposal that fails to recognize The Baetylus Theorem and how it can be integrated into a business offering has not optimized its competitive advantage. More importantly, consumers should not falsely believe that purchasing a wearable technology, improves health. PMID:27617162

  10. Human-Computer Interfaces for Wearable Computers: A Systematic Approach to Development and Evaluation

    OpenAIRE

    Witt, Hendrik

    2007-01-01

    The research presented in this thesis examines user interfaces for wearable computers.Wearable computers are a special kind of mobile computers that can be worn on the body. Furthermore, they integrate themselves even more seamlessly into different activities than a mobile phone or a personal digital assistant can.The thesis investigates the development and evaluation of user interfaces for wearable computers. In particular, it presents fundamental research results as well as supporting softw...

  11. Design of wearable health monitoring device

    Science.gov (United States)

    Devara, Kresna; Ramadhanty, Savira; Abuzairi, Tomy

    2018-02-01

    Wearable smart health monitoring devices have attracted considerable attention in both research community and industry. Some of the causes are the increasing healthcare costs, along with the growing technology. To address this demand, in this paper, design and evaluation of wearable health monitoring device integrated with smartphone were presented. This device was designed for patients in need of constant health monitoring. The performance of the proposed design has been tested by conducting measurement once in 2 minutes for 10 minutes to obtain heart rate and body temperature data. The comparation between data measured by the proposed device and that measured by the reference device yields only an average error of 1.45% for heart rate and 1.04% for body temperature.

  12. Wearables in Medicine.

    Science.gov (United States)

    Yetisen, Ali K; Martinez-Hurtado, Juan Leonardo; Ünal, Barış; Khademhosseini, Ali; Butt, Haider

    2018-06-11

    Wearables as medical technologies are becoming an integral part of personal analytics, measuring physical status, recording physiological parameters, or informing schedule for medication. These continuously evolving technology platforms do not only promise to help people pursue a healthier life style, but also provide continuous medical data for actively tracking metabolic status, diagnosis, and treatment. Advances in the miniaturization of flexible electronics, electrochemical biosensors, microfluidics, and artificial intelligence algorithms have led to wearable devices that can generate real-time medical data within the Internet of things. These flexible devices can be configured to make conformal contact with epidermal, ocular, intracochlear, and dental interfaces to collect biochemical or electrophysiological signals. This article discusses consumer trends in wearable electronics, commercial and emerging devices, and fabrication methods. It also reviews real-time monitoring of vital signs using biosensors, stimuli-responsive materials for drug delivery, and closed-loop theranostic systems. It covers future challenges in augmented, virtual, and mixed reality, communication modes, energy management, displays, conformity, and data safety. The development of patient-oriented wearable technologies and their incorporation in randomized clinical trials will facilitate the design of safe and effective approaches. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  13. Talking wearables exploit context

    NARCIS (Netherlands)

    Geldof, S.; Terken, J.M.B.

    2001-01-01

    This paper addresses the issue of how natural language generation technology can contribute to less intrusive wearable devices. Based on the investigation of how humans adapt the form of their utterances to the context of their hearer, we propose a strategy to relate (physical) context to the

  14. Quantification of whole-body bradykinesia in Parkinson's disease participants using multiple inertial sensors.

    Science.gov (United States)

    Memar, Sara; Delrobaei, Mehdi; Pieterman, Marcus; McIsaac, Kenneth; Jog, Mandar

    2018-04-15

    Bradykinesia (slowness of movement) is a common motor symptom of Parkinson's disease (PD) that can severely affect quality of life for those living with the disease. Assessment and treatment of PD motor symptoms largely depends on clinical scales such as the Unified Parkinson's Disease Rating Scale (UPDRS). However, such clinical scales rely on the visual assessment by a human observer, naturally resulting in inter-rater variability. Although previous studies have developed objective means for measuring bradykinesia in PD patients, their evaluation was restricted by the type of movement and number of joints assessed. These studies failed to provide a more comprehensive, whole-body evaluation capable of measuring multiple joints simultaneously. This study utilizes wearable inertial measurement units (IMUs) to quantify whole-body movements, providing novel bradykinesia indices for walking (WBI) and standing up from a chair (sit-to-stand; SBI). The proposed bradykinesia indices include the joint angles at both upper and lower limbs and trunk motion to compute a complete, objective score for whole body bradykinesia. Thirty PD and 11 age-matched healthy control participants were recruited for the study. The participants performed two standard walking tasks that involved multiple body joints in the upper and lower limbs. The WBI and SBI successfully identified differences between control and PD participants. The indices also effectively identified differences within the PD population, distinguishing participants assessed with (ON) and without (OFF) levodopa; the gold-standard of treatment for PD. The goal of this study is to provide health professionals with an objective score for whole body bradykinesia by simultaneously measuring the upper and lower extremities along with truncal movement. This method demonstrates potential to be used in conjunction with current clinical standards for motor symptom assessment, and may also be promising for the remote assessment of PD

  15. A CMOS-compatible large-scale monolithic integration of heterogeneous multi-sensors on flexible silicon for IoT applications

    KAUST Repository

    Nassar, Joanna M.

    2017-02-07

    We report CMOS technology enabled fabrication and system level integration of flexible bulk silicon (100) based multi-sensors platform which can simultaneously sense pressure, temperature, strain and humidity under various physical deformations. We also show an advanced wearable version for body vital monitoring which can enable advanced healthcare for IoT applications.

  16. A CMOS-compatible large-scale monolithic integration of heterogeneous multi-sensors on flexible silicon for IoT applications

    KAUST Repository

    Nassar, Joanna M.; Sevilla, Galo T.; Velling, Seneca J.; Cordero, Marlon D.; Hussain, Muhammad Mustafa

    2017-01-01

    We report CMOS technology enabled fabrication and system level integration of flexible bulk silicon (100) based multi-sensors platform which can simultaneously sense pressure, temperature, strain and humidity under various physical deformations. We also show an advanced wearable version for body vital monitoring which can enable advanced healthcare for IoT applications.

  17. Recyclable Nonfunctionalized Paper-Based Ultralow-Cost Wearable Health Monitoring System

    KAUST Repository

    Nassar, Joanna M.; Mishra, Kush; Lau, Kirklann; Aguirre-Pablo, Andres A.; Hussain, Muhammad Mustafa

    2017-01-01

    A wearable health monitor using low-cost and recyclable paper continuously supervises and assesses body vital conditions simultaneously and in real time, such as blood pressure, heart rate, body temperature, and skin hydration. The affordability

  18. Recent Progress of Self-Powered Sensing Systems for Wearable Electronics.

    Science.gov (United States)

    Lou, Zheng; Li, La; Wang, Lili; Shen, Guozhen

    2017-12-01

    Wearable/flexible electronic sensing systems are considered to be one of the key technologies in the next generation of smart personal electronics. To realize personal portable devices with mobile electronics application, i.e., wearable electronic sensors that can work sustainably and continuously without an external power supply are highly desired. The recent progress and advantages of wearable self-powered electronic sensing systems for mobile or personal attachable health monitoring applications are presented. An overview of various types of wearable electronic sensors, including flexible tactile sensors, wearable image sensor array, biological and chemical sensor, temperature sensors, and multifunctional integrated sensing systems is provided. Self-powered sensing systems with integrated energy units are then discussed, separated as energy harvesting self-powered sensing systems, energy storage integrated sensing systems, and all-in-on integrated sensing systems. Finally, the future perspectives of self-powered sensing systems for wearable electronics are discussed. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  19. Command Recognition of Robot with Low Dimension Whole-Body Haptic Sensor

    Science.gov (United States)

    Ito, Tatsuya; Tsuji, Toshiaki

    The authors have developed “haptic armor”, a whole-body haptic sensor that has an ability to estimate contact position. Although it is developed for safety assurance of robots in human environment, it can also be used as an interface. This paper proposes a command recognition method based on finger trace information. This paper also discusses some technical issues for improving recognition accuracy of this system.

  20. Flexible quality of service model for wireless body area sensor networks

    OpenAIRE

    Liao, Yangzhe; Leeson, Mark S.; Higgins, Matthew D.

    2016-01-01

    Wireless body area sensor networks (WBASNs) are becoming an increasingly significant breakthrough technology for smart healthcare systems, enabling improved clinical decision-making in daily medical care. Recently, radio frequency ultra-wideband technology has developed substantially for physiological signal monitoring due to its advantages such as low-power consumption, high transmission data rate, and miniature antenna size. Applications of future ubiquitous healthcare systems offer the pro...