WorldWideScience

Sample records for wearable body sensor

  1. 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.

  2. A Study of Implanted and Wearable Body Sensor Networks

    CERN Document Server

    Ullah, Sana; Siddiqui, M Arif; Kwak, Kyung Sup; 10.1007/978-3-540-78582-8_47

    2009-01-01

    Recent advances in intelligent sensors, microelectronics and integrated circuit, system-on-chip design and low power wireless communication introduced the development of miniaturised and autonomous sensor nodes. These tiny sensor nodes can be deployed to develop a proactive Body Sensor Network (BSN). The rapid advancement in ultra low-power RF (radio frequency) technology enables invasive and non-invasive devices to communicate with a remote station. This communication revolutionizes healthcare system by enabling long term health monitoring of a patient and providing real time feedback to the medical experts. In this paper, we present In-body and On-body communication networks with a special focus on the methodologies of wireless communication between implanted medical devices with external monitoring equipment and recent technological growth in both areas. We also discuss open issues and challenges in a BSN.

  3. A wearable respiratory biofeedback system based on generalized body sensor network.

    Science.gov (United States)

    Liu, Guan-Zheng; Huang, Bang-Yu; Wang, Lei

    2011-06-01

    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.

  4. 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

  5. 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.

  6. Design and Implementation of a Wearable Body Area Sensor Network for Distributed FES System

    Institute of Scientific and Technical Information of China (English)

    XIE Yong-ji; LIU Xiao-xuan; QU Hong-en; LAN Ning

    2014-01-01

    A wearable body area sensor network (WBASN) was designed and implemented to monitor movement information of stroke patients in real time. The sensor system was combined with a previously developed distributed functional electrical stimulation (dFES) system, which is a promising technology for motor rehabilitation of stroke patients. Movement information could be useful in outcome assessment of rehabilitation, or for closed-loop adaptive stimulation during rehabilitation. In addition, a short-latency, low-power communication protocol was developed to meet the clinical requirements of energy efficiency and high rate of data feed-through. The prototype of the WBASN was tested in preliminary human experiments. Experimental results demonstrate the feasibility of the proposed wearable body area sensor network in monitoring arm movements on healthy subjects.

  7. 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....../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...

  8. Context Aware Similarity Measure Selection: Mining of Wearable Implantable Body Sensor Network Data with Logical Reasoning

    Directory of Open Access Journals (Sweden)

    Y Indu

    2016-04-01

    Full Text Available Wireless sensor networks monitor the environment with various types of sensors. Environment in its broader terms can be the geographic environment or it can be our human body. One such type of network is Wearable and Implantable Body Sensor Network (WIBSN. This paper focuses on processing of data generated from WIBSN. WIBSN includes a network of sensors that generate different type of values. This paper treats each sensor as a dimension in the whole dataset. In this case, data may have both continuous and discrete values. Hence; proposed work can be applicable for both of those data values. By identifying nature of the sensor data model, underlying similarity or dissimilarity measure is selected. A novel Crisp clustering technique is used to simulate the proposed work.

  9. Monitoring activities of daily living based on wearable wireless body sensor network.

    Science.gov (United States)

    Kańtoch, E; Augustyniak, P; Markiewicz, M; Prusak, D

    2014-01-01

    With recent advances in microprocessor chip technology, wireless communication, and biomedical engineering it is possible to develop miniaturized ubiquitous health monitoring devices that are capable of recording physiological and movement signals during daily life activities. The aim of the research is to implement and test the prototype of health monitoring system. The system consists of the body central unit with Bluetooth module and wearable sensors: the custom-designed ECG sensor, the temperature sensor, the skin humidity sensor and accelerometers placed on the human body or integrated with clothes and a network gateway to forward data to a remote medical server. The system includes custom-designed transmission protocol and remote web-based graphical user interface for remote real time data analysis. Experimental results for a group of humans who performed various activities (eg. working, running, etc.) showed maximum 5% absolute error compared to certified medical devices. The results are promising and indicate that developed wireless wearable monitoring system faces challenges of multi-sensor human health monitoring during performing daily activities and opens new opportunities in developing novel healthcare services.

  10. Wearable Sensor Systems for Infants

    OpenAIRE

    Zhihua Zhu; Tao Liu; Guangyi Li; Tong Li; Yoshio Inoue

    2015-01-01

    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 sys...

  11. 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.

  12. Wearable sensor systems for infants.

    Science.gov (United States)

    Zhu, Zhihua; Liu, Tao; Li, Guangyi; Li, Tong; Inoue, Yoshio

    2015-02-05

    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.

  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. Wearable Sensor Systems for Infants

    Science.gov (United States)

    Zhu, Zhihua; Liu, Tao; Li, Guangyi; Li, Tong; Inoue, Yoshio

    2015-01-01

    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. PMID:25664432

  15. Remote monitoring of soldier safety through body posture identification using wearable sensor networks

    Science.gov (United States)

    Biswas, Subir; Quwaider, Muhannad

    2008-04-01

    The physical safety and well being of the soldiers in a battlefield is the highest priority of Incident Commanders. Currently, the ability to track and monitor soldiers rely on visual and verbal communication which can be somewhat limited in scenarios where the soldiers are deployed inside buildings and enclosed areas that are out of visual range of the commanders. Also, the need for being stealth can often prevent a battling soldier to send verbal clues to a commander about his or her physical well being. Sensor technologies can remotely provide various data about the soldiers including physiological monitoring and personal alert safety system functionality. This paper presents a networked sensing solution in which a body area wireless network of multi-modal sensors can monitor the body movement and other physiological parameters for statistical identification of a soldier's body posture, which can then be indicative of the physical conditions and safety alerts of the soldier in question. The specific concept is to leverage on-body proximity sensing and a Hidden Markov Model (HMM) based mechanism that can be applied for stochastic identification of human body postures using a wearable sensor network. The key idea is to collect relative proximity information between wireless sensors that are strategically placed over a subject's body to monitor the relative movements of the body segments, and then to process that using HMM in order to identify the subject's body postures. The key novelty of this approach is a departure from the traditional accelerometry based approaches in which the individual body segment movements, rather than their relative proximity, is used for activity monitoring and posture detection. Through experiments with body mounted sensors we demonstrate that while the accelerometry based approaches can be used for differentiating activity intensive postures such as walking and running, they are not very effective for identification and

  16. 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

    2014-01-01

    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. PMID:25302808

  17. Synchronous Wearable Wireless Body Sensor Network Composed of Autonomous Textile Nodes

    Directory of Open Access Journals (Sweden)

    Peter Vanveerdeghem

    2014-10-01

    Full Text Available 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.

  18. An All-Elastomeric Transparent and Stretchable Temperature Sensor for Body-Attachable Wearable Electronics.

    Science.gov (United States)

    Trung, Tran Quang; Ramasundaram, Subramaniyan; Hwang, Byeong-Ung; Lee, Nae-Eung

    2016-01-20

    A transparent stretchable (TS) gated sensor array with high optical transparency, conformality, and high stretchability of up to 70% is demonstrated. The TS-gated sensor array has high responsivity to temperature changes in objects and human skin. This unprecedented TS-gated sensor array, as well as the integrated platform of the TS-gated sensor with a transparent and stretchable strain sensor, show great potential for application to wearable skin electronics for recognition of human activity.

  19. 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.

  20. A Review of Wearable Sensor Systems for Monitoring Body Movements of Neonates

    Directory of Open Access Journals (Sweden)

    Hongyu Chen

    2016-12-01

    Full Text Available Characteristics of physical movements are indicative of infants’ neuro-motor development and brain dysfunction. For instance, infant seizure, a clinical signal of brain dysfunction, could be identified and predicted by monitoring its physical movements. With the advance of wearable sensor technology, including the miniaturization of sensors, and the increasing broad application of micro- and nanotechnology, and smart fabrics in wearable sensor systems, it is now possible to collect, store, and process multimodal signal data of infant movements in a more efficient, more comfortable, and non-intrusive way. This review aims to depict the state-of-the-art of wearable sensor systems for infant movement monitoring. We also discuss its clinical significance and the aspect of system design.

  1. A Review of Wearable Sensor Systems for Monitoring Body Movements of Neonates.

    Science.gov (United States)

    Chen, Hongyu; Xue, Mengru; Mei, Zhenning; Bambang Oetomo, Sidarto; Chen, Wei

    2016-12-14

    Characteristics of physical movements are indicative of infants' neuro-motor development and brain dysfunction. For instance, infant seizure, a clinical signal of brain dysfunction, could be identified and predicted by monitoring its physical movements. With the advance of wearable sensor technology, including the miniaturization of sensors, and the increasing broad application of micro- and nanotechnology, and smart fabrics in wearable sensor systems, it is now possible to collect, store, and process multimodal signal data of infant movements in a more efficient, more comfortable, and non-intrusive way. This review aims to depict the state-of-the-art of wearable sensor systems for infant movement monitoring. We also discuss its clinical significance and the aspect of system design.

  2. 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.

  3. 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.

  4. Wearable Optical Chemical Sensors

    Science.gov (United States)

    Lobnik, Aleksandra

    Wearable sensors can be used to provide valuable information about the wearer's health and/or monitor the wearer's surroundings, identify safety concerns and detect threats, during the wearer's daily routine within his or her natural environment. The "sensor on a textile", an integrated sensor capable of analyzing data, would enable early many forms of detection. Moreover, a sensor connected with a smart delivery system could simultaneously provide comfort and monitoring (for safety and/or health), non-invasive measurements, no laboratory sampling, continuous monitoring during the daily activity of the person, and possible multi-parameter analysis and monitoring. However, in order for the technology to be accessible, it must remain innocuous and impose a minimal intrusion on the daily activities of the wearer. Therefore, such wearable technologies should be soft, flexible, and washable in order to meet the expectations of normal clothing. Optical chemical sensors (OCSs) could be used as wearable technology since they can be embedded into textile structures by using conventional dyeing, printing processes and coatings, while fiber-optic chemical sensors (FOCSs) as well as nanofiber sensors (NFSs) can be incorporated by weaving, knitting or laminating. The interest in small, robust and sensitive sensors that can be embedded into textile structures is increasing and the research activity on this topic is an important issue.

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

    Science.gov (United States)

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

    2010-07-01

    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.

  6. Towards a Wearable Inertial Sensor Network

    OpenAIRE

    Van Laerhoven, Kristof; Gellersen, Hans; Kern, Nicky; Schiele, Bernt

    2003-01-01

    Abstract. Wearable inertial sensors have become an inexpensive option to measure the movements and positions of a person. Other techniques that use environmental sensors such as ultrasound trackers or vision-based methods need full line of sight or a local setup, and it is complicated to access this data from a wearable computer’s perspective. However, a body-centric approach where sensor data is acquired and processed locally, has a need for appropriate algorithms that have to operate under ...

  7. Wearable CO2 sensor

    OpenAIRE

    Radu, Tanja; Fay, Cormac; Lau, King-Tong; Waite, Rhys; Diamond, Dermot

    2009-01-01

    High concentrations of CO2 may develop particularly in the closed spaces during fires and can endanger the health of emergency personnel by causing serious physiological effects. The proposed prototype provides real-time continuous monitoring of CO2 in a wearable configuration sensing platform. A commercially available electrochemical CO2 sensor was selected due to its selectivity, sensitivity and low power demand. This was integrated onto an electronics platform that performed signal capture...

  8. 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.

  9. Wearable sensors in syncope management

    National Research Council Canada - National Science Library

    Meyer, Christian; Carvalho, Paulo; Brinkmeyer, Christoph; Kelm, Malte; Couceiro, Ricardo; Mühlsteff, Jens

    2015-01-01

    .... Wearable sensors might overcome some limitations, including misdiagnosis and inappropriate defibrillator shocks, because a variety of physiological measures can now be easily acquired by a single non...

  10. Wireless power delivery for wearable sensors and implants in Body Sensor Networks.

    Science.gov (United States)

    Zhang, Fei; Hackwoth, Steve A; Liu, Xiaoyu; Li, Chengliu; Sun, Mingui

    2010-01-01

    A recent study on witricity (wireless electricity) has demonstrated that wireless energy can be delivered over a moderate distance using strongly coupled magnetic resonance. The objective of this work is to apply the witricity technology to the problem of powering a wireless Body Sensor Network (wBSN). The theory of witricity is investigated using coupled mode theory. Compact witricity resonators are designed, and a working prototype of witricity powered wBSN is built and evaluated. An energy transfer efficiency of about 80 % over a distance of 15 cm is achieved. Besides the high efficiency, it has been observed that a certain misalignment between the transmitter and receiver has little effect on the power transfer. Our experimental results indicate that witricity provides a powerful solution to the energy supply problem of wBSN.

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

    Science.gov (United States)

    Özdemir, Ahmet Turan

    2016-01-01

    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. PMID:27463719

  12. 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.

  13. 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.

  14. Wearable Sensor Networks for Motion Capture

    OpenAIRE

    Dennis Arsenault; Anthony Whitehead

    2015-01-01

    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 ga...

  15. 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.

  16. Activity recognition with wearable sensors on loose clothing

    National Research Council Canada - National Science Library

    Brendan Michael; Matthew Howard

    2017-01-01

    .... However, wearable sensors suffer from motion artefacts introduced by the non-rigid attachment of sensors to the body, and the prevailing view is that it is necessary to eliminate these artefacts...

  17. 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

  18. Detecting vital signs with wearable wireless sensors.

    Science.gov (United States)

    Yilmaz, Tuba; Foster, Robert; Hao, Yang

    2010-01-01

    The emergence of wireless technologies and advancements in on-body sensor design can enable change in the conventional health-care system, replacing it with wearable health-care systems, centred on the individual. Wearable monitoring systems can provide continuous physiological data, as well as better information regarding the general health of individuals. Thus, such vital-sign monitoring systems will reduce health-care costs by disease prevention and enhance the quality of life with disease management. In this paper, recent progress in non-invasive monitoring technologies for chronic disease management is reviewed. In particular, devices and techniques for monitoring blood pressure, blood glucose levels, cardiac activity and respiratory activity are discussed; in addition, on-body propagation issues for multiple sensors are presented.

  19. Detecting Vital Signs with Wearable Wireless Sensors

    Directory of Open Access Journals (Sweden)

    Yang Hao

    2010-12-01

    Full Text Available The emergence of wireless technologies and advancements in on-body sensor design can enable change in the conventional health-care system, replacing it with wearable health-care systems, centred on the individual. Wearable monitoring systems can provide continuous physiological data, as well as better information regarding the general health of individuals. Thus, such vital-sign monitoring systems will reduce health-care costs by disease prevention and enhance the quality of life with disease management. In this paper, recent progress in non-invasive monitoring technologies for chronic disease management is reviewed. In particular, devices and techniques for monitoring blood pressure, blood glucose levels, cardiac activity and respiratory activity are discussed; in addition, on-body propagation issues for multiple sensors are presented.

  20. Physical Activities Monitoring Using Wearable Acceleration Sensors Attached to the Body.

    Science.gov (United States)

    Arif, Muhammad; Kattan, Ahmed

    2015-01-01

    Monitoring physical activities by using wireless sensors is helpful for identifying postural orientation and movements in the real-life environment. A simple and robust method based on time domain features to identify the physical activities is proposed in this paper; it uses sensors placed on the subjects' wrist, chest and ankle. A feature set based on time domain characteristics of the acceleration signal recorded by acceleration sensors is proposed for the classification of twelve physical activities. Nine subjects performed twelve different types of physical activities, including sitting, standing, walking, running, cycling, Nordic walking, ascending stairs, descending stairs, vacuum cleaning, ironing clothes and jumping rope, and lying down (resting state). Their ages were 27.2 ± 3.3 years and their body mass index (BMI) is 25.11 ± 2.6 Kg/m2. Classification results demonstrated a high validity showing precision (a positive predictive value) and recall (sensitivity) of more than 95% for all physical activities. The overall classification accuracy for a combined feature set of three sensors is 98%. The proposed framework can be used to monitor the physical activities of a subject that can be very useful for the health professional to assess the physical activity of healthy individuals as well as patients.

  1. Physical Activities Monitoring Using Wearable Acceleration Sensors Attached to the Body.

    Directory of Open Access Journals (Sweden)

    Muhammad Arif

    Full Text Available Monitoring physical activities by using wireless sensors is helpful for identifying postural orientation and movements in the real-life environment. A simple and robust method based on time domain features to identify the physical activities is proposed in this paper; it uses sensors placed on the subjects' wrist, chest and ankle. A feature set based on time domain characteristics of the acceleration signal recorded by acceleration sensors is proposed for the classification of twelve physical activities. Nine subjects performed twelve different types of physical activities, including sitting, standing, walking, running, cycling, Nordic walking, ascending stairs, descending stairs, vacuum cleaning, ironing clothes and jumping rope, and lying down (resting state. Their ages were 27.2 ± 3.3 years and their body mass index (BMI is 25.11 ± 2.6 Kg/m2. Classification results demonstrated a high validity showing precision (a positive predictive value and recall (sensitivity of more than 95% for all physical activities. The overall classification accuracy for a combined feature set of three sensors is 98%. The proposed framework can be used to monitor the physical activities of a subject that can be very useful for the health professional to assess the physical activity of healthy individuals as well as patients.

  2. Physical Activities Monitoring Using Wearable Acceleration Sensors Attached to the Body

    Science.gov (United States)

    2015-01-01

    Monitoring physical activities by using wireless sensors is helpful for identifying postural orientation and movements in the real-life environment. A simple and robust method based on time domain features to identify the physical activities is proposed in this paper; it uses sensors placed on the subjects’ wrist, chest and ankle. A feature set based on time domain characteristics of the acceleration signal recorded by acceleration sensors is proposed for the classification of twelve physical activities. Nine subjects performed twelve different types of physical activities, including sitting, standing, walking, running, cycling, Nordic walking, ascending stairs, descending stairs, vacuum cleaning, ironing clothes and jumping rope, and lying down (resting state). Their ages were 27.2 ± 3.3 years and their body mass index (BMI) is 25.11 ± 2.6 Kg/m2. Classification results demonstrated a high validity showing precision (a positive predictive value) and recall (sensitivity) of more than 95% for all physical activities. The overall classification accuracy for a combined feature set of three sensors is 98%. The proposed framework can be used to monitor the physical activities of a subject that can be very useful for the health professional to assess the physical activity of healthy individuals as well as patients. PMID:26203909

  3. Wireless Sensor Network for Wearable Physiological Monitoring

    OpenAIRE

    P. S. Pandian; K. P. Safeer; Pragati Gupta; D. T. Shakunthala; B. S. Sundersheshu; V. C. Padaki

    2008-01-01

    Wearable physiological monitoring system consists of an array of sensors embedded into the fabric of the wearer to continuously monitor the physiological parameters and transmit wireless to a remote monitoring station. At the remote monitoring station the data is correlated to study the overall health status of the wearer. In the conventional wearable physiological monitoring system, the sensors are integrated at specific locations on the vest and are interconnected to the wearable data acqui...

  4. Wearable optical sensor

    OpenAIRE

    Pereira, Maurício Neves Rodrigues da Silva

    2008-01-01

    Neste trabalho foi desenvolvido um sensor para medição do ângulo de flexão do cotovelo de um indivíduo. Este sensor é uma ajuda na aferição da recuperação de uma pessoa que sofreu um acidente cardiovascular e que tenha perdido mobilidade no conjunto ombro-braço. Embora o sensor por si só não desempenhe uma função vital na recuperação de um paciente com as características referidas, espera-se que se torne uma ajuda na motivação da pessoa bem como uma maneira de quantificar o ...

  5. Wearable electrochemical sensors for monitoring performance athletes

    Science.gov (United States)

    Fraser, Kevin J.; Curto, Vincenzo F.; Coyle, Shirley; Schazmann, Benjamin; Byrne, Robert; Benito-Lopez, Fernando; Owens, Róisín M.; Malliaras, George G.; Diamond, Dermot

    2011-10-01

    Nowadays, wearable sensors such as heart rate monitors and pedometers are in common use. The use of wearable systems such as these for personalized exercise regimes for health and rehabilitation is particularly interesting. In particular, the true potential of wearable chemical sensors, which for the real-time ambulatory monitoring of bodily fluids such as tears, sweat, urine and blood has not been realized. Here we present a brief introduction into the fields of ionogels and organic electrochemical transistors, and in particular, the concept of an OECT transistor incorporated into a sticking-plaster, along with a printable "ionogel" to provide a wearable biosensor platform.

  6. Wireless Sensor Network for Wearable Physiological Monitoring

    Directory of Open Access Journals (Sweden)

    P. S. Pandian

    2008-05-01

    Full Text Available Wearable physiological monitoring system consists of an array of sensors embedded into the fabric of the wearer to continuously monitor the physiological parameters and transmit wireless to a remote monitoring station. At the remote monitoring station the data is correlated to study the overall health status of the wearer. In the conventional wearable physiological monitoring system, the sensors are integrated at specific locations on the vest and are interconnected to the wearable data acquisition hardware by wires woven into the fabric. The drawbacks associated with these systems are the cables woven in the fabric pickup noise such as power line interference and signals from nearby radiating sources and thereby corrupting the physiological signals. Also repositioning the sensors in the fabric is difficult once integrated. The problems can be overcome by the use of physiological sensors with miniaturized electronics to condition, process, digitize and wireless transmission integrated into the single module. These sensors are strategically placed at various locations on the vest. Number of sensors integrated into the fabric form a network (Personal Area Network and interacts with the human system to acquire and transmit the physiological data to a wearable data acquisition system. The wearable data acquisition hardware collects the data from various sensors and transmits the processed data to the remote monitoring station. The paper discusses wireless sensor network and its application to wearable physiological monitoring and its applications. Also the problems associated with conventional wearable physiological monitoring are discussed.

  7. Energy-aware activity classification using wearable sensor networks

    Science.gov (United States)

    Dong, Bo; Montoye, Alexander; Moore, Rebecca; Pfeiffer, Karin; Biswas, Subir

    2013-05-01

    This paper presents implementation details, system characterization, and the performance of a wearable sensor network that was designed for human activity analysis. Specific machine learning mechanisms are implemented for recognizing a target set of activities with both out-of-body and on-body processing arrangements. Impacts of energy consumption by the on-body sensors are analyzed in terms of activity detection accuracy for out-of-body processing. Impacts of limited processing abilities for the on-body scenario are also characterized in terms of detection accuracy, by varying the background processing load in the sensor units. Impacts of varying number of sensors in terms of activity classification accuracy are also evaluated. Through a rigorous systems study, it is shown that an efficient human activity analytics system can be designed and operated even under energy and processing constraints of tiny on-body wearable sensors.

  8. 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.

  9. Wearable Sensor System for Human Dynamics Analysis

    OpenAIRE

    Liu, Tao; Inoue, Yoshio; Shibata, Kyoko; Zheng, Rencheng

    2010-01-01

    A new wearable sensor system was developed for measuring tri-directional ground reaction force (GRF) and segment orientations. A stationary force plate can not measure more than one stride; moreover, in studies of stair ascent and descent measurements, a complex system consisting of many stationary force plates and a data fusion method must be constructed (Stacoff et al., 2005; Della and Bonato, 2007). The wearable sensor system proposed in this chapter can be applied to successive walking tr...

  10. 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.

  11. Textile-based wearable sensors for assisting sports performance

    OpenAIRE

    Coyle, Shirley; Morris, Deirdre; Lau, King-Tong; Moyna, Niall; Diamond, Dermot

    2009-01-01

    There is a need for wearable sensors to assess physiological signals and body kinematics during exercise. Such sensors need to be straightforward to use, and ideally the complete system integrated fully within a garment. This would allow wearers to monitor their progress as they undergo an exercise training programme without the need to attach external devices. This takes physiological monitoring into a more natural setting. By developing textile sensors the intelligence is integrated int...

  12. Wearable Sensors May Spot Illness Before Symptoms Start

    Science.gov (United States)

    ... page: https://medlineplus.gov/news/fullstory_163021.html Wearable Sensors May Spot Illness Before Symptoms Start New technology ... 12, 2017 THURSDAY, Jan. 12, 2017 (HealthDay News) -- Wearable sensors to track things such as heart rate, activity ...

  13. Collaborative Processing of Wearable and Ambient Sensor System for Blood Pressure Monitoring

    Science.gov (United States)

    Nakamura, Masayuki; Nakamura, Jiro; Lopez, Guillaume; Shuzo, Masaki; Yamada, Ichiro

    2011-01-01

    This paper describes wireless wearable and ambient sensors that cooperate to monitor a person’s vital signs such as heart rate and blood pressure during daily activities. Each wearable sensor is attached on different parts of the body. The wearable sensors require a high sampling rate and time synchronization to provide a precise analysis of the received signals. The trigger signal for synchronization is provided by the ambient sensors, which detect the user’s presence. The Bluetooth and IEEE 802.15.4 wireless technologies are used for real-time sensing and time synchronization. Thus, this wearable health-monitoring sensor response is closely related to the context in which it is being used. Experimental results indicate that the system simultaneously provides information about the user’s location and vital signs, and the synchronized wearable sensors successfully measures vital signs with a 1 ms resolution. PMID:22163984

  14. Collaborative Processing of Wearable and Ambient Sensor System for Blood Pressure Monitoring

    Directory of Open Access Journals (Sweden)

    Ichiro Yamada

    2011-06-01

    Full Text Available This paper describes wireless wearable and ambient sensors that cooperate to monitor a person’s vital signs such as heart rate and blood pressure during daily activities. Each wearable sensor is attached on different parts of the body. The wearable sensors require a high sampling rate and time synchronization to provide a precise analysis of the received signals. The trigger signal for synchronization is provided by the ambient sensors, which detect the user’s presence. The Bluetooth and IEEE 802.15.4 wireless technologies are used for real-time sensing and time synchronization. Thus, this wearable health-monitoring sensor response is closely related to the context in which it is being used. Experimental results indicate that the system simultaneously provides information about the user’s location and vital signs, and the synchronized wearable sensors successfully measures vital signs with a 1 ms resolution.

  15. 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.

  16. Gait Recognition Using Wearable Motion Recording Sensors

    Science.gov (United States)

    Gafurov, Davrondzhon; Snekkenes, Einar

    2009-12-01

    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.

  17. Pulmonary disease management system with distributed wearable sensors.

    Science.gov (United States)

    Fu, Yongji; Ayyagari, Deepak; Colquitt, Nhedti

    2009-01-01

    A pulmonary disease management system with on-body and near-body sensors is introduced in this presentation. The system is wearable for continuous ambulatory monitoring. Distributed sensor data is transferred through a wireless body area network (BAN) to a central controller for real time analysis. Physiological and environmental parameters are monitored and analyzed using prevailing clinical guidelines for self-management of environmentally-linked pulmonary ailments. The system provides patients with reminders, warnings, and instructions to reduce emergency room and physician visits, and improve clinical outcomes.

  18. 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

    2015-11-24

    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.

  19. 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.

  20. A wireless sensor network compatible wearable u-healthcare monitoring system using integrated ECG, accelerometer and SpO2.

    Science.gov (United States)

    Chung, Wan-Young; Lee, Young-Dong; Jung, Sang-Joong

    2008-01-01

    This paper presents the design and development of a wearable ubiquitous healthcare monitoring system using integrated electrocardiogram (ECG), accelerometer and oxygen saturation (SpO(2)) sensors. In this design, non-intrusive healthcare system was designed based on wireless sensor network (WSN) for wide area coverage with minimum battery power to support RF transmission. We have developed various devices such as wearable ubiquitous sensor network (USN) node, wearable chest sensor belt and wrist pulse oximeter for this system. Low power ECG, accelerometer and SpO(2) sensors board was integrated to the wearable USN node for user's health monitoring. The wearable ubiquitous healthcare monitoring system allows physiological data to be transmitted in wireless sensor network using IEEE 802.15.4 from on-body wearable sensor devices to a base-station which is connected to a server PC. Physiological data can be displayed and stored in the server PC continuously.

  1. A wearable sensor based on CLYC scintillators

    Science.gov (United States)

    McDonald, Benjamin S.; Myjak, Mitchell J.; Zalavadia, Mital A.; Smart, John E.; Willett, Jesse A.; Landgren, Peter C.; Greulich, Christopher R.

    2016-06-01

    We have developed a wearable radiation sensor using Cs2LiYCl6:Ce (CLYC) for simultaneous gamma-ray and neutron detection. The system includes two ∅ 2.5 × 2.5cm3 crystals coupled to small, metal-body photomultiplier tubes. A custom, low-power electronics base digitizes the output signal at three time points and enables both pulse height and pulse shape discrimination of gamma rays and neutrons. The total counts, anomaly detection metrics, and identified isotopes are displayed on a small screen. Users may leave the device in unattended mode to collect long-dwell energy spectra. The system stores up to 18 h of one-second data, including energy spectra, and may transfer the data to a remote computer via a wired or wireless connection. The prototype is 18 × 13 × 7.5cm3, weighs 1.3 kg, not including the protective pouch, and runs on six AA alkaline batteries for 29 h with the wireless link active, or 41 h with the wireless link disabled. In this paper, we summarize the system design and present characterization results from the detector modules. The energy resolution is about 6.5% full width at half maximum at 662 keV due to the small photomultiplier tube selected, and the linearity and pulse shape discrimination performance are very good.

  2. A wearable sensor based on CLYC scintillators

    Energy Technology Data Exchange (ETDEWEB)

    McDonald, Benjamin S. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Myjak, Mitchell J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Zalavadia, Mital A. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Smart, John E. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Willett, Jesse A. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Landgren, Peter C. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Greulich, Christopher R. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2016-06-01

    We developed a wearable radiation sensor using Cs2LiYCl6:Ce (CLYC) for simultaneous gamma-ray and neutron detection. The system includes two ø2.5×2.5 cm3 crystals coupled to small, metal-body photomultiplier tubes. A custom, low-power electronics base digitizes the output signal at three time points and enables both pulse height and pulse shape discrimination of neutrons and gamma-rays. Data, including spectra, can be transferred via a wired or wireless connection. The total gamma-ray and neutron counts, anomaly detection metrics, and identified isotopes are displayed on a small screen on the device. Users may leave the system in unattended mode to collect long-dwell energy spectra. The prototype system has overall dimensions of 13×7.5×18 cm3 and weight of 1.3 kg, not including the protective pouch, and runs on six AA alkaline batteries for 29 hours with a 1% wireless transmission duty cycle and 41 hours with the wireless turned off . In this paper, we summarize the system design and present characterization results from the detector modules. The energy resolution is about 6.5% full width at half maximum at 662 keV due to the small photomultiplier tube selected, and the linearity and pulse shape discrimination performance are very good.

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

    Science.gov (United States)

    Patel, Shyamal; Park, Hyung; Bonato, Paolo; Chan, Leighton; Rodgers, Mary

    2012-04-20

    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. 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.

  5. Lightweight, Wearable, Metal Rubber Sensor

    Science.gov (United States)

    Hill, Andrea

    2015-01-01

    For autonomous health monitoring. NanoSonic, Inc., has developed comfortable garments with multiple integrated sensors designed to monitor astronaut health throughout long-duration space missions. The combined high electrical conductivity, low mechanical modulus, and environmental robustness of the sensors make them an effective, lightweight, and comfortable alternative to conventional use of metal wiring and cabling.

  6. What Does Big Data Mean for Wearable Sensor Systems?

    Science.gov (United States)

    Lovell, N. H.; Yang, G. Z.; Horsch, A.; Lukowicz, P.; Murrugarra, L.; Marschollek, M.

    2014-01-01

    Summary Objectives The aim of this paper is to discuss how recent developments in the field of big data may potentially impact the future use of wearable sensor systems in healthcare. Methods The article draws on the scientific literature to support the opinions presented by the IMIA Wearable Sensors in Healthcare Working Group. Results The following is discussed: the potential for wearable sensors to generate big data; how complementary technologies, such as a smartphone, will augment the concept of a wearable sensor and alter the nature of the monitoring data created; how standards would enable sharing of data and advance scientific progress. Importantly, attention is drawn to statistical inference problems for which big datasets provide little assistance, or may hinder the identification of a useful solution. Finally, a discussion is presented on risks to privacy and possible negative consequences arising from intensive wearable sensor monitoring. Conclusions Wearable sensors systems have the potential to generate datasets which are currently beyond our capabilities to easily organize and interpret. In order to successfully utilize wearable sensor data to infer wellbeing, and enable proactive health management, standards and ontologies must be developed which allow for data to be shared between research groups and between commercial systems, promoting the integration of these data into health information systems. However, policy and regulation will be required to ensure that the detailed nature of wearable sensor data is not misused to invade privacies or prejudice against individuals. PMID:25123733

  7. 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.  .

  8. Flexible heartbeat sensor for wearable device.

    Science.gov (United States)

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

    2017-03-08

    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.

  9. Learning Predictive Movement Models From Fabric-Mounted Wearable Sensors.

    Science.gov (United States)

    Michael, Brendan; Howard, Matthew

    2016-12-01

    The measurement and analysis of human movement for applications in clinical diagnostics or rehabilitation is often performed in a laboratory setting using static motion capture devices. A growing interest in analyzing movement in everyday environments (such as the home) has prompted the development of "wearable sensors", with the most current wearable sensors being those embedded into clothing. A major issue however with the use of these fabric-embedded sensors is the undesired effect of fabric motion artefacts corrupting movement signals. In this paper, a nonparametric method is presented for learning body movements, viewing the undesired motion as stochastic perturbations to the sensed motion, and using orthogonal regression techniques to form predictive models of the wearer's motion that eliminate these errors in the learning process. Experiments in this paper show that standard nonparametric learning techniques underperform in this fabric motion context and that improved prediction accuracy can be made by using orthogonal regression techniques. Modelling this motion artefact problem as a stochastic learning problem shows an average 77% decrease in prediction error in a body pose task using fabric-embedded sensors, compared to a kinematic model.

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. Wearable Sensors for Remote Health Monitoring

    Science.gov (United States)

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

    2017-01-01

    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. PMID:28085085

  16. Wearable technology for bio-chemical analysis of body fluids during exercise.

    Science.gov (United States)

    Morris, Deirdre; Schazmann, Benjamin; Wu, Yangzhe; Coyle, Shirley; Brady, Sarah; Fay, Cormac; Hayes, Jer; Lau, King Tong; Wallace, Gordon; Diamond, Dermot

    2008-01-01

    This paper details the development of a textile based fluid handling system with integrated wireless biochemical sensors. Such research represents a new advancement in the area of wearable technologies. The system contains pH, sodium and conductivity sensors. It has been demonstrated during on-body trials that the pH sensor has close agreement with measurements obtained using a reference pH probe. Initial investigations into the sodium and conductivity sensors have shown their suitability for integration into the wearable system. It is thought that applications exist in personal health and sports performance and training.

  17. Wearable Potentiometric Chloride Sweat Sensor: The Critical Role of the Salt Bridge.

    Science.gov (United States)

    Choi, Dong-Hoon; Kim, Jin Seob; Cutting, Garry R; Searson, Peter C

    2016-12-20

    The components of sweat provide an array of potential biomarkers for health and disease. Sweat chloride is of interest as a biomarker for cystic fibrosis, electrolyte metabolism disorders, electrolyte balance, and electrolyte loss during exercise. Developing wearable sensors for biomarkers in sweat is a major technological challenge. Potentiometric sensors provide a relatively simple technology for on-body sweat chloride measurement, however, equilibration between reference and test solutions has limited the time over which accurate measurements can be made. Here, we report on a wearable potentiometric chloride sweat sensor. We performed parametric studies to show how the salt bridge geometry determines equilibration between the reference and test solutions. From these results, we show a sweat chloride sensor can be designed to provide accurate measurements over extended times. We then performed on-body tests on healthy subjects while exercising to establish the feasibility of using this technology as a wearable device.

  18. Wireless wearable network and wireless body-centric network for future wearable computer

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The wireless wearable network and wireless body-centric network can assistant to the user anywhere at anytime communicating with wireless components seamlessly. In this paper, the wireless wearable network and wireless body-centric network have been discussed, and the frequency band and human body effect has been estimated. The bluetooth and UWB technology can be used to construct the narrow band and the broad band wireless wearable network and wireless body-centric network separately. Further, the narrow band wireless wearable network and wireless body-centric network based on bluetooth technology has been constructed by integrated planar inverted-F antenna and the communication channel character has been studied by measurement. The results can provide the possibility of producing a prototype radio system that can be integrated with the wearable computers by suitable wireless technologies developed and applied to facilitate a reliable and continuous connectivity between the system units.

  19. 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.

  20. Recognition of human activities with wearable sensors

    Science.gov (United States)

    He, Weihua; Guo, Yongcai; Gao, Chao; Li, Xinke

    2012-12-01

    A novel approach for recognizing human activities with wearable sensors is investigated in this article. The key techniques of this approach include the generalized discriminant analysis (GDA) and the relevance vector machines (RVM). The feature vectors extracted from the measured signal are processed by GDA, with its dimension remarkably reduced from 350 to 12 while fully maintaining the most discriminative information. The reduced feature vectors are then classified by the RVM technique according to an extended multiclass model, which shows good convergence characteristic. Experimental results on the Wearable Action Recognition Dataset demonstrate that our approach achieves an encouraging recognition rate of 99.2%, true positive rate of 99.18% and false positive rate of 0.07%. Although in most cases, the support vector machines model has more than 70 support vectors, the number of relevance vectors related to different activities is always not more than 4, which implies a great simplicity in the classifier structure. Our approach is expected to have potential in real-time applications or solving problems with large-scale datasets, due to its perfect recognition performance, strong ability in feature reduction, and simple classifier structure.

  1. Measurements of Generated Energy/Electrical Quantities from Locomotion Activities Using Piezoelectric Wearable Sensors for Body Motion Energy Harvesting

    Directory of Open Access Journals (Sweden)

    Antonino Proto

    2016-04-01

    Full Text Available In this paper, two different piezoelectric transducers—a ceramic piezoelectric, lead zirconate titanate (PZT, and a polymeric piezoelectric, polyvinylidene fluoride (PVDF—were compared in terms of energy that could be harvested during locomotion activities. The transducers were placed into a tight suit in proximity of the main body joints. Initial testing was performed by placing the transducers on the neck, shoulder, elbow, wrist, hip, knee and ankle; then, five locomotion activities—walking, walking up and down stairs, jogging and running—were chosen for the tests. The values of the power output measured during the five activities were in the range 6 µW–74 µW using both transducers for each joint.

  2. Measurements of Generated Energy/Electrical Quantities from Locomotion Activities Using Piezoelectric Wearable Sensors for Body Motion Energy Harvesting.

    Science.gov (United States)

    Proto, Antonino; Penhaker, Marek; Bibbo, Daniele; Vala, David; Conforto, Silvia; Schmid, Maurizio

    2016-04-12

    In this paper, two different piezoelectric transducers-a ceramic piezoelectric, lead zirconate titanate (PZT), and a polymeric piezoelectric, polyvinylidene fluoride (PVDF)-were compared in terms of energy that could be harvested during locomotion activities. The transducers were placed into a tight suit in proximity of the main body joints. Initial testing was performed by placing the transducers on the neck, shoulder, elbow, wrist, hip, knee and ankle; then, five locomotion activities-walking, walking up and down stairs, jogging and running-were chosen for the tests. The values of the power output measured during the five activities were in the range 6 µW-74 µW using both transducers for each joint.

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

    National Research Council Canada - National Science Library

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

    2015-01-01

    .... 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...

  4. 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

  5. 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 pr

  6. 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.

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

    Science.gov (United States)

    Lorwongtragool, Panida; Sowade, Enrico; Watthanawisuth, Natthapol; Baumann, Reinhard R.; Kerdcharoen, Teerakiat

    2014-01-01

    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. PMID:25340447

  8. 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.

  9. An Adaptive Sensor Data Segments Selection Method for Wearable Health Care Services.

    Science.gov (United States)

    Chen, Shih-Yeh; Lai, Chin-Feng; Hwang, Ren-Hung; Lai, Ying-Hsun; Wang, Ming-Shi

    2015-12-01

    As cloud computing and wearable devices technologies mature, relevant services have grown more and more popular in recent years. The healthcare field is one of the popular services for this technology that adopts wearable devices to sense signals of negative physiological events, and to notify users. The development and implementation of long-term healthcare monitoring that can prevent or quickly respond to the occurrence of disease and accidents present an interesting challenge for computing power and energy limits. This study proposed an adaptive sensor data segments selection method for wearable health care services, and considered the sensing frequency of the various signals from human body, as well as the data transmission among the devices. The healthcare service regulates the sensing frequency of devices by considering the overall cloud computing environment and the sensing variations of wearable health care services. The experimental results show that the proposed service can effectively transmit the sensing data and prolong the overall lifetime of health care services.

  10. Online phase detection using wearable sensors for walking with a robotic prosthesis

    NARCIS (Netherlands)

    Gorsic, M.; Kamnik, R.; Ambrozic, L.; Vitiello, N.; Lefeber, D.J.; Pasquini, G.; Munih, M.

    2014-01-01

    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 princi

  11. Online phase detection using wearable sensors for walking with a robotic prosthesis

    NARCIS (Netherlands)

    Gorsic, M.; Kamnik, R.; Ambrozic, L.; Vitiello, N.; Lefeber, D.J.; Pasquini, G.; Munih, M.

    2014-01-01

    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

  12. A Wearable Capacitive Sensor for Monitoring Human Respiratory Rate

    Science.gov (United States)

    Kundu, Subrata Kumar; Kumagai, Shinya; Sasaki, Minoru

    2013-04-01

    Realizing an untethered, low-cost, and comfortably wearable respiratory rate sensor for long-term breathing monitoring application still remains a challenge. In this paper, a conductive-textile-based wearable respiratory rate sensing technique based on the capacitive sensing approach is proposed. The sensing unit consists of two conductive textile electrodes that can be easily fabricated, laminated, and integrated in garments. Respiration cycle is detected by measuring the capacitance of two electrodes placed on the inner anterior and posterior sides of a T-shirt at either the abdomen or chest position. A convenient wearable respiratory sensor setup with a capacitance-to-voltage converter has been devised. Respiratory rate as well as breathing mode can be accurately identified using the designed sensor. The sensor output provides significant information on respiratory flow. The effectiveness of the proposed system for different breathing patterns has been evaluated by experiments.

  13. Carbon nanotube strain sensors for wearable patient monitoring applications

    Science.gov (United States)

    Abraham, Jose K.; Aryasomayajula, Lavanya; Whitchurch, Ashwin; Varadan, Vijay K.

    2008-03-01

    Wearable health monitoring systems have recently attracted widespread interest for their application in long term patient monitoring. Wireless wearable technology enables continuous observation of patients while they perform their normal everyday activities. This involves the development of flexible and conformable sensors that could be easily integrated to the smart fabrics. Carbon nanotubes are found to be one of the ideal candidate materials for the design of multifunctional e-textiles because of their capability to change conductance based on any mechanical deformation as well as surface functionalization. This paper presents the development and characterization of a carbon nanotube (CNT)-polymer nanocomposite flexible strain sensor for wearable health monitoring applications. These strain sensors can be used to measure the respiration rhythm which is a vital signal required in health monitoring. A number of strain sensor prototypes with different CNT compositions have been fabricated and their characteristics for both static as well as dynamic strain have been measured.

  14. A Method of Data Aggregation for Wearable Sensor Systems.

    Science.gov (United States)

    Shen, Bo; Fu, Jun-Song

    2016-06-23

    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.

  15. 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.

  16. Estimation of Center of Mass Trajectory using Wearable Sensors during Golf Swing.

    Science.gov (United States)

    Najafi, Bijan; Lee-Eng, Jacqueline; Wrobel, James S; Goebel, Ruben

    2015-06-01

    This study suggests a wearable sensor technology to estimate center of mass (CoM) trajectory during a golf swing. Groups of 3, 4, and 18 participants were recruited, respectively, for the purpose of three validation studies. Study 1 examined the accuracy of the system to estimate a 3D body segment angle compared to a camera-based motion analyzer (Vicon®). Study 2 assessed the accuracy of three simplified CoM trajectory models. Finally, Study 3 assessed the accuracy of the proposed CoM model during multiple golf swings. A relatively high agreement was observed between wearable sensors and the reference (Vicon®) for angle measurement (r > 0.99, random error 0.93 v. r = 0.52, respectively). On the same note, the proposed two-link model estimated CoM trajectory during golf swing with relatively good accuracy (r > 0.9, A-P random error wearable technology based on inertial sensors are accurate to estimate center of mass trajectory in complex athletic task (e.g., golf swing)This study suggests that two-link model of human body provides optimum tradeoff between accuracy and minimum number of sensor module for estimation of center of mass trajectory in particular during fast movements.Wearable technologies based on inertial sensors are viable option for assessing dynamic postural control in complex task outside of gait laboratory and constraints of cameras, surface, and base of support.

  17. 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.

  18. Wearable PPG Sensor Matrix for Cardiovascular Assessment

    OpenAIRE

    Mečņika, V; Kviesis-Kipge, E; Krieviņš, I; Marcikevics, Z; Schwarz, A.

    2014-01-01

    Wearable biomonitoring systems and smart textiles for healthcare are gaining more importance and significance in the R&D sphere due to their potentials in healthcare and sports. Such biomonitoring systems offer a number of advantages in comparison to the conventional equipment proving mobility of the wearer during a long-term monitoring of vital parameters. There are different options to set up the physiological monitoring using wireless and wearable technologies. One of ...

  19. 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.

  20. Wearable biosensors for medical applications

    OpenAIRE

    Crean, C; Mcgeough, C; O'Kennedy, R

    2012-01-01

    Over the past decade, the design and development of wearable sensors for biomedical applications has garnered considerable attention in the scientifi c community and in industry. This chapter aims to review research conducted into wearable sensors for healthcare monitoring. Acceptance of this approach in observation of physiological data depends strongly on the cost, wearability, usability and performance of such devices. An outline of body sensor network systems (and applications of wearable...

  1. A fiber-optic powered wireless sensor module made on elastomeric substrate for wearable sensors.

    Science.gov (United States)

    Lien, V; Lin, H; Chuang, J; Sailor, M; Lo, Y

    2004-01-01

    We demonstrate an integrated sensor module that combines a photonic nano-porous sensor and a bias-free optical powered RF transducer. The sensor signal is encoded in the RF frequency ready for transmission. The entire sensor module does not include battery and is constructed with the flexible and biocompatible elastomeric polymer, PDMS. This technology holds promise for wearable sensors.

  2. A Wearable Hydration Sensor with Conformal Nanowire Electrodes.

    Science.gov (United States)

    Yao, Shanshan; Myers, Amanda; Malhotra, Abhishek; Lin, Feiyan; Bozkurt, Alper; Muth, John F; Zhu, Yong

    2017-01-27

    A wearable skin hydration sensor in the form of a capacitor is demonstrated based on skin impedance measurement. The capacitor consists of two interdigitated or parallel electrodes that are made of silver nanowires (AgNWs) in a polydimethylsiloxane (PDMS) matrix. The flexible and stretchable nature of the AgNW/PDMS electrode allows conformal contact to the skin. The hydration sensor is insensitive to the external humidity change and is calibrated against a commercial skin hydration system on an artificial skin over a wide hydration range. The hydration sensor is packaged into a flexible wristband, together with a network analyzer chip, a button cell battery, and an ultralow power microprocessor with Bluetooth. In addition, a chest patch consisting of a strain sensor, three electrocardiography electrodes, and a skin hydration sensor is developed for multimodal sensing. The wearable wristband and chest patch may be used for low-cost, wireless, and continuous monitoring of skin hydration and other health parameters.

  3. 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. .

  4. Triboelectric generators and sensors for self-powered wearable electronics.

    Science.gov (United States)

    Ha, Minjeong; Park, Jonghwa; Lee, Youngoh; Ko, Hyunhyub

    2015-04-28

    In recent years, the field of wearable electronics has evolved at a rapid pace, requiring continued innovation in technologies in the fields of electronics, energy devices, and sensors. In particular, wearable devices have multiple applications in healthcare monitoring, identification, and wireless communications, and they are required to perform well while being lightweight and having small size, flexibility, low power consumption, and reliable sensing performances. In this Perspective, we introduce two recent reports on the triboelectric generators with high-power generation achieved using flexible and lightweight textiles or miniaturized and hybridized device configurations. In addition, we present a brief overview of recent developments and future prospects of triboelectric energy harvesters and sensors, which may enable fully self-powered wearable devices with significantly improved sensing capabilities.

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

    OpenAIRE

    I. Orha; S. Oniga

    2015-01-01

    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. ...

  6. Wearable Sensors for Chemical & Biological Detection

    Energy Technology Data Exchange (ETDEWEB)

    Ozanich, Richard M.

    2017-08-31

    One of PNNL’s strengths is the ability to conduct comprehensive technology foraging and objective assessments of various technology areas. The following examples highlight leading research by others in the area of chemical and biological (chem/bio) detection that could be further developed into a robust, highly integrated wearables to aid preparedness, response and recovery.

  7. Towards Mental Stress Detection Using Wearable Physiological Sensors

    NARCIS (Netherlands)

    Wijsman, J.L.P; Grundlehner, Bernard; Liu, Hao; Liu, H.; Hermens, Hermanus J.; Penders, Julien

    2011-01-01

    Early mental stress detection can prevent many stress related health problems. This study aimed at using a wearable sensor system to measure physiological signals and detect mental stress. Three different stress conditions were presented to a healthy subject group. During the procedure, ECG,

  8. Towards Mental Stress Detection Using Wearable Physiological Sensors

    NARCIS (Netherlands)

    Wijsman, Jacqueline; Grundlehner, Bernard; Liu, Hao; Hermens, Hermie; Penders, Julien

    2011-01-01

    Early mental stress detection can prevent many stress related health problems. This study aimed at using a wearable sensor system to measure physiological signals and detect mental stress. Three different stress conditions were presented to a healthy subject group. During the procedure, ECG, respira

  9. Manufacturing of Wearable Sensors for Human Health and Performance Monitoring

    Science.gov (United States)

    Alizadeh, Azar

    2015-03-01

    Continuous monitoring of physiological and biological parameters is expected to improve performance and medical outcomes by assessing overall health status and alerting for life-saving interventions. Continuous monitoring of these parameters requires wearable devices with an appropriate form factor (lightweight, comfortable, low energy consuming and even single-use) to avoid disrupting daily activities thus ensuring operation relevance and user acceptance. Many previous efforts to implement remote and wearable sensors have suffered from high cost and poor performance, as well as low clinical and end-use acceptance. New manufacturing and system level design approaches are needed to make the performance and clinical benefits of these sensors possible while satisfying challenging economic, regulatory, clinical, and user-acceptance criteria. In this talk we will review several recent design and manufacturing efforts aimed at designing and building prototype wearable sensors. We will discuss unique opportunities and challenges provided by additive manufacturing, including 3D printing, to drive innovation through new designs, faster prototyping and manufacturing, distributed networks, and new ecosystems. We will also show alternative hybrid self-assembly based integration techniques for low cost large scale manufacturing of single use wearable devices. Coauthors: Prabhjot Singh and Jeffrey Ashe.

  10. Assessment of Wearable Sensor Technologies for Biosurveillance

    Science.gov (United States)

    2014-11-01

    intelligence (AI) and biometric data, the device captures electrodermal activity in real time to assess emotional states. Using the technique of...Biometric smartwear Hexoskin Breathing rate, volume, cadence, ECG, sleep position, heart rate, and other physiological data Wearable Wellnes...watches. Google Fit’s fitness tracking will display data such as heart rate, or detect whether its wearer has been physically active . Google’s

  11. 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

  12. 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

  13. Beyond activity tracking: next-generation wearable and implantable sensor technologies (Conference Presentation)

    Science.gov (United States)

    Mercier, Patrick

    2017-05-01

    Current-generation wearable devices have had success continuously measuring the activity and heart rate of subjects during exercise and daily life activities, resulting in interesting new data sets that can, though machine learning algorithms, predict a small subset of health conditions. However, this information is only very peripherally related to most health conditions, and thus offers limited utility to a wide range of the population. In this presentation, I will discuss emerging sensor technologies capable of measuring new and interesting parameters that can potentially offer much more meaningful and actionable data sets. Specifically, I will present recent work on wearable chemical sensors that can, for the first time, continuously monitor a suite of parameters like glucose, alcohol, lactate, and electrolytes, all while wirelessly delivering these results to a smart phone in real time. Demonstration platforms featuring patch, temporary tattoo, and mouthguard form factors will be described, in addition to the corresponding electronics necessary to perform sensor conditioning and wireless readout. Beyond chemical sensors, I will also discuss integration strategies with more conventional electrophysiological and physical parameters like ECG and strain gauges for cardiac and respiration rate monitoring, respectively. Finally, I will conclude the talk by introducing a new form of wireless communications in body-area networks that utilize the body itself as a channel for magnetic energy. Since the power consumption of conventional RF circuits often dominates the power of wearable devices, this new magnetic human body communication technique is specifically architected to dramatically reduce the path loss compared to conventional RF and capacitive human body communication techniques, thereby enabling ultra-low-power body area networks for next-generation wearable devices.

  14. Accelerometry-based Recognition of the Placement Sites of a Wearable Sensor.

    Science.gov (United States)

    Mannini, Andrea; Sabatini, Angelo M; Intille, Stephen S

    2015-08-01

    This work describes an automatic method to recognize the position of an accelerometer worn on five different parts of the body: ankle, thigh, hip, arm and wrist from raw accelerometer data. Automatic detection of body position of a wearable sensor would enable systems that allow users to wear sensors flexibly on different body parts or permit systems that need to automatically verify sensor placement. The two-stage location detection algorithm works by first detecting time periods during which candidates are walking (regardless of where the sensor is positioned). Then, assuming that the data refer to walking, the algorithm detects the position of the sensor. Algorithms were validated on a dataset that is substantially larger than in prior work, using a leave-one-subject-out cross-validation approach. Correct walking and placement recognition were obtained for 97.4% and 91.2% of classified data windows, respectively.

  15. Monitoring stage fright outside the laboratory: an example in a professional musician using wearable sensors.

    Science.gov (United States)

    Kusserow, Martin; Candia, Victor; Amft, Oliver; Hildebrandt, Horst; Folkers, Gerd; Tröster, Gerhard

    2012-03-01

    We implemented and tested a wearable sensor system to measure patterns of stress responses in a professional musician under public performance conditions. Using this sensor system, we monitored the cellist's heart activity, the motion of multiple body parts, and their gradual changes during three repeated performances of a skill-demanding piece in front of a professional audience. From the cellist and her teachers, we collected stage fright self-reports and performance ratings that were related to our sensor data analysis results. Concomitant to changes in body motion and heart rate, the cellist perceived a reduction in stage fright. Performance quality was objectively improved, as technical playing errors decreased throughout repeated renditions. In particular, from performance 1 to 3, the wearable sensors measured a significant increase in the cellist's bowing motion dynamics of approximately 6% and a decrease in heart rate. Bowing motion showed a marginal correlation to the observed heart rate patterns during playing. The wearable system did not interfere with the cellist's performance, thereby allowing investigation of stress responses during natural public performances.

  16. Wearable Sensors in Healthcare and Sensor-Enhanced Health Information Systems: All Our Tomorrows?

    Science.gov (United States)

    Gietzelt, Matthias; Schulze, Mareike; Kohlmann, Martin; Song, Bianying; Wolf, Klaus-Hendrik

    2012-01-01

    Wearable sensor systems which allow for remote or self-monitoring of health-related parameters are regarded as one means to alleviate the consequences of demographic change. This paper aims to summarize current research in wearable sensors as well as in sensor-enhanced health information systems. Wearable sensor technologies are already advanced in terms of their technical capabilities and are frequently used for cardio-vascular monitoring. Epidemiologic predictions suggest that neuropsychiatric diseases will have a growing impact on our health systems and thus should be addressed more intensively. Two current project examples demonstrate the benefit of wearable sensor technologies: long-term, objective measurement under daily-life, unsupervised conditions. Finally, up-to-date approaches for the implementation of sensor-enhanced health information systems are outlined. Wearable sensors are an integral part of future pervasive, ubiquitous and person-centered health care delivery. Future challenges include their integration into sensor-enhanced health information systems and sound evaluation studies involving measures of workload reduction and costs. PMID:22844645

  17. 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.

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

    Science.gov (United States)

    Chen, Kun-Hui; Chen, Po-Chao; Liu, Kai-Chun; Chan, Chia-Tai

    2015-01-01

    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. PMID:25686308

  19. Radio-frequency energy harvesting for wearable sensors.

    Science.gov (United States)

    Borges, Luís M; Chávez-Santiago, Raul; Barroca, Norberto; Velez, Fernando José; Balasingham, Ilangko

    2015-02-01

    The use of wearable biomedical sensors for the continuous monitoring of physiological signals will facilitate the involvement of the patients in the prevention and management of chronic diseases. The fabrication of small biomedical sensors transmitting physiological data wirelessly is possible as a result of the tremendous advances in ultra-low power electronics and radio communications. However, the widespread adoption of these devices depends very much on their ability to operate for long periods of time without the need to frequently change, recharge or even use batteries. In this context, energy harvesting (EH) is the disruptive technology that can pave the road towards the massive utilisation of wireless wearable sensors for patient self-monitoring and daily healthcare. Radio-frequency (RF) transmissions from commercial telecommunication networks represent reliable ambient energy that can be harvested as they are ubiquitous in urban and suburban areas. The state-of-the-art in RF EH for wearable biomedical sensors specifically targeting the global system of mobile 900/1800 cellular and 700 MHz digital terrestrial television networks as ambient RF energy sources are showcased. Furthermore, guidelines for the choice of the number of stages for the RF energy harvester are presented, depending on the requirements from the embedded system to power supply, which is useful for other researchers that work in the same area. The present authors' recent advances towards the development of an efficient RF energy harvester and storing system are presented and thoroughly discussed too.

  20. Recognizing the intensity of strength training exercises with wearable sensors.

    Science.gov (United States)

    Pernek, Igor; Kurillo, Gregorij; Stiglic, Gregor; Bajcsy, Ruzena

    2015-12-01

    In this paper we propose a system based on a network of wearable accelerometers and an off-the-shelf smartphone to recognize the intensity of stationary activities, such as strength training exercises. The system uses a hierarchical algorithm, consisting of two layers of Support Vector Machines (SVMs), to first recognize the type of exercise being performed, followed by recognition of exercise intensity. The first layer uses a single SVM to recognize the type of the performed exercise. Based on the recognized type a corresponding intensity prediction SVM is selected on the second layer, specializing in intensity prediction for the recognized type of exercise. We evaluate the system for a set of upper-body exercises using different weight loads. Additionally, we compare the most important features for exercise and intensity recognition tasks and investigate how different sliding window combinations, sensor configurations and number of training subjects impact the algorithm performance. We perform all of the experiments for two different types of features to evaluate the feasibility of implementation on resource constrained hardware. The results show the algorithm is able to recognize exercise types with approximately 85% accuracy and 6% intensity prediction error. Furthermore, due to similar performance using different types of features, the algorithm offers potential for implementation on resource constrained hardware.

  1. 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.

  2. Cost-effective wearable sensor to detect EMF

    OpenAIRE

    Paradiso, Joseph A.; Vaucelle, Catherine Nicole; Ishii, Hiroshi

    2009-01-01

    In this paper we present the design of a cost-effective wearable sensor to detect and indicate the strength and other characteristics of the electric field emanating from a laptop display. Our Electromagnetic Field Detector Bracelet can provide an immediate awareness of electric fields radiated from an object used frequently. Our technology thus supports awareness of ambient background emanation beyond human perception. We discuss how detection of such radiation mig...

  3. Wearable sensors and systems. From enabling technology to clinical applications.

    Science.gov (United States)

    Bonato, Paolo

    2010-01-01

    It is now more than 50 years since the time when clinical monitoring of individuals in the home and community settings was first envisioned. Until recently, technologies to enable such vision were lacking. However, wearable sensors and systems developed over the past decade have provided the tools to finally implement and deploy technology with the capabilities required by researchers in the field of patients' home monitoring. As discussed, potential applications of these technologies include the early diagnosis of diseases such as congestive heart failure, the prevention of chronic conditions such as diabetes, improved clinical management of neurodegenerative conditions such as Parkinson's disease, and the ability to promptly respond to emergency situations such as seizures in patients with epilepsy and cardiac arrest in subjects undergoing cardiovascular monitoring. Current research efforts are now focused on the development of more complex systems for home monitoring of individuals with a variety of preclinical and clinical conditions. Recent research on the clinical assessment of wearable technology promises to deliver methodologies that are expected to lead to clinical adoption within the next five to ten years. In particular, combining home robots and wearable technology is likely to be a key step toward achieving the goal of effectively monitoring patients in the home. These efforts to merge home robots and wearable technology are expected to enable a new generation of complex systems with the ability to monitor subjects' status, facilitate the administration of interventions, and provide an invaluable tool to respond to emergency situations.

  4. 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.

  5. 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.

  6. Location tracking system using wearable on-body GPS antenna

    Directory of Open Access Journals (Sweden)

    Sabapathy Thennarasan

    2017-01-01

    Full Text Available An on-body location tracking system is developed and integrated with a wearable GPS antenna. Such system is beneficial in human location tracking of patients and elderly within a radius of 1 km. The system consists of a wearable antenna, a GPS module, a low cost microcontroller, two RF modules and a local monitoring system. A user equipped with the GPS antenna, GPS module and a RF transmitter is able send his/her location to the local monitoring system via a RF receiver. The proposed wearable antenna is validated to be safe for human use in terms of specific absorption rate (SAR. This antenna was then incorporated into the complete prototype and tested. Several suggestions for future improvements are also proposed and discussed.

  7. Skin-mountable stretch sensor for wearable health monitoring.

    Science.gov (United States)

    Pegan, Jonathan D; Zhang, Jasmine; Chu, Michael; Nguyen, Thao; Park, Sun-Jun; Paul, Akshay; Kim, Joshua; Bachman, Mark; Khine, Michelle

    2016-10-06

    This work presents a wrinkled Platinum (wPt) strain sensor with tunable strain sensitivity for applications in wearable health monitoring. These stretchable sensors show a dynamic range of up to 185% strain and gauge factor (GF) of 42. This is believed to be the highest reported GF of any metal thin film strain sensor over a physiologically relevant dynamic range to date. Importantly, sensitivity and dynamic range are tunable to the application by adjusting wPt film thickness. Performance is reliable over 1000 cycles with low hysteresis after sensor conditioning. The possibility of using such a sensor for real-time respiratory monitoring by measuring chest wall displacement and correlating with lung volume is demonstrated.

  8. Development of Compact Flexible Displacement Sensors Using Ultrasonic Sensor for Wearable Actuators

    Directory of Open Access Journals (Sweden)

    Akagi Tetsuya

    2016-01-01

    Full Text Available In position control of wearable actuator such as a rubber artificial muscle, a compact flexible displacement sensor is much attractive and required. In this paper, two types of flexible displacement sensor using the ultrasonic sensor were introduced. One is a built-in displacement sensor for rubber artificial muscle. Another is a sensor that can measure the sliding displacement on a flexible tube for flexible robot. Both sensors use ultrasonic displacement sensors. The construction, operating principle and measuring performance of two sensors were also described.

  9. CALIBRATION OF A WEARABLE GLUCOSE SENSOR

    NARCIS (Netherlands)

    SCHMIDT, FJ; AALDERS, AL; SCHOONEN, AJM; DOORENBOS, H

    1992-01-01

    Calibration of glucose sensors proved difficult for electrodes with immobilized glucose-oxidase. The correlation between the sensitivity of the electrodes in vitro and in vivo appeared to be poor. We developed a new type of glucose sensor, based on a microdialysis system, in which an oxygen electrod

  10. 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

  11. Hand-arm vibration exposure monitoring with wearable sensor module.

    Science.gov (United States)

    Austad, Hanne O; Røed, Morten H; Liverud, Anders E; Dalgard, Steffen; Seeberg, Trine M

    2013-01-01

    Vibration exposure is a serious risk within work physiology for several work groups. Combined with cold artic climate, the risk for permanent harm is even higher. Equipment that can monitor the vibration exposure and warn the user when at risk will provide a safer work environment for these work groups. This study evaluates whether data from a wearable wireless multi-parameter sensor module can be used to estimate vibration exposure and exposure time. This work has been focused on the characterization of the response from the accelerometer in the sensor module and the optimal location of the module in the hand-arm configuration.

  12. Assessment of Lower Limb Prosthesis through Wearable Sensors and Thermography

    Science.gov (United States)

    Cutti, Andrea Giovanni; Perego, Paolo; Fusca, Marcello C.; Sacchetti, Rinaldo; Andreoni, Giuseppe

    2014-01-01

    This study aimed to explore the application of infrared thermography in combination with ambulatory wearable monitoring of temperature and relative humidity, to assess the residual limb-to-liner interface in lower-limb prosthesis users. Five male traumatic transtibial amputees were involved, who reported no problems or discomfort while wearing the prosthesis. A thermal imaging camera was used to measure superficial thermal distribution maps of the stump. A wearable system for recording the temperature and relative humidity in up to four anatomical points was developed, tested in vitro and integrated with the measurement set. The parallel application of an infrared camera and wearable sensors provided complementary information. Four main Regions of Interest were identified on the stump (inferior patella, lateral/medial epicondyles, tibial tuberosity), with good inter-subject repeatability. An average increase of 20% in hot areas (P < 0.05) is shown after walking compared to resting conditions. The sensors inside the cuff did not provoke any discomfort during recordings and provide an inside of the thermal exchanges while walking and recording the temperature increase (a regime value is ∼+1.1 ± 0.7 °C) and a more significant one (∼+4.1 ± 2.3%) in humidity because of the sweat produced. This study has also begun the development of a reference data set for optimal socket/liner-stump construction. PMID:24618782

  13. Assessment of Lower Limb Prosthesis through Wearable Sensors and Thermography

    Directory of Open Access Journals (Sweden)

    Andrea Giovanni Cutti

    2014-03-01

    Full Text Available This study aimed to explore the application of infrared thermography in combination with ambulatory wearable monitoring of temperature and relative humidity, to assess the residual limb-to-liner interface in lower-limb prosthesis users. Five male traumatic transtibial amputees were involved, who reported no problems or discomfort while wearing the prosthesis. A thermal imaging camera was used to measure superficial thermal distribution maps of the stump. A wearable system for recording the temperature and relative humidity in up to four anatomical points was developed, tested in vitro and integrated with the measurement set. The parallel application of an infrared camera and wearable sensors provided complementary information. Four main Regions of Interest were identified on the stump (inferior patella, lateral/medial epicondyles, tibial tuberosity, with good inter-subject repeatability. An average increase of 20% in hot areas (P < 0.05 is shown after walking compared to resting conditions. The sensors inside the cuff did not provoke any discomfort during recordings and provide an inside of the thermal exchanges while walking and recording the temperature increase (a regime value is ~+1.1 ± 0.7 °C and a more significant one (~+4.1 ± 2.3% in humidity because of the sweat produced. This study has also begun the development of a reference data set for optimal socket/liner-stump construction.

  14. Wearable Conductive Fiber Sensors for Multi-Axis Human Joint Angle Measurements

    Directory of Open Access Journals (Sweden)

    Asada H Harry

    2005-03-01

    Full Text Available Abstract Background The practice of continuous, long-term monitoring of human joint motion is one that finds many applications, especially in the medical and rehabilitation fields. There is a lack of acceptable devices available to perform such measurements in the field in a reliable and non-intrusive way over a long period of time. The purpose of this study was therefore to develop such a wearable joint monitoring sensor capable of continuous, day-to-day monitoring. Methods A novel technique of incorporating conductive fibers into flexible, skin-tight fabrics surrounding a joint is developed. Resistance changes across these conductive fibers are measured, and directly related to specific single or multi-axis joint angles through the use of a non-linear predictor after an initial, one-time calibration. Because these sensors are intended for multiple uses, an automated registration algorithm has been devised using a sensitivity template matched to an array of sensors spanning the joints of interest. In this way, a sensor array can be taken off and put back on an individual for multiple uses, with the sensors automatically calibrating themselves each time. Results The wearable sensors designed are comfortable, and acceptable for long-term wear in everyday settings. Results have shown the feasibility of this type of sensor, with accurate measurements of joint motion for both a single-axis knee joint and a double axis hip joint when compared to a standard goniometer used to measure joint angles. Self-registration of the sensors was found to be possible with only a few simple motions by the patient. Conclusion After preliminary experiments involving a pants sensing garment for lower body monitoring, it has been seen that this methodology is effective for monitoring joint motion of the hip and knee. This design therefore produces a robust, comfortable, truly wearable joint monitoring device.

  15. Wearable tactile sensor based on flexible microfluidics.

    Science.gov (United States)

    Yeo, Joo Chuan; Yu, Jiahao; Koh, Zhao Ming; Wang, Zhiping; Lim, Chwee Teck

    2016-08-16

    In this work, we develop a liquid-based thin film microfluidic tactile sensor of high flexibility, robustness and sensitivity. The microfluidic elastomeric structure comprises a pressure sensitive region and parallel arcs that interface with screen-printed electrodes. The microfluidic sensor is functionalized with a highly conductive metallic liquid, eutectic gallium indium (eGaIn). Microdeformation on the pressure sensor results in fluid displacement which corresponds to a change in electrical resistance. By emulating parallel electrical circuitry in our microchannel design, we reduced the overall electrical resistance of the sensor, therefore enhancing its device sensitivity. Correspondingly, we report a device workable within a range of 4 to 100 kPa and sensitivity of up to 0.05 kPa(-1). We further demonstrate its robustness in withstanding >2500 repeated loading and unloading cycles. Finally, as a proof of concept, we demonstrate that the sensors may be multiplexed to detect forces at multiple regions of the hand. In particular, our sensors registered unique electronic signatures in object grasping, which could provide better assessment of finger dexterity.

  16. Wearable sensor network for health monitoring: the case of Parkinson disease

    Science.gov (United States)

    Pastorino, M.; Arredondo, M. T.; Cancela, J.; Guillen, S.

    2013-06-01

    The aim of this paper is to show how wearable sensors can be useful in health solutions, improving the continuous monitoring and management of patients. This paper is focused on the available solution for motion analysis, providing a description of human motion features which can be measured through the use of wearable sensors. Moreover, this paper presents an example of wearable solution used for the objective assessment of Parkinson's disease symptoms. Results indicate that wearable sensors are useful for the objective evaluation of motor fluctuation and clinicians can benefit from these tools in order to adjust and personalise the treatment.

  17. Zero-Effort Camera-Assisted Calibration Techniques for Wearable Motion Sensors

    Science.gov (United States)

    Wu, Jian; Jafari, Roozbeh

    2017-01-01

    Activity recognition using wearable motion sensors plays an important role in pervasive wellness and healthcare monitoring applications. The activity recognition algorithms are often designed to work with a known orientation of sensors on the body. In the case of accidental displacement of the motion sensors, it is important to identify the new sensor location and orientation. This step, often called calibration or recalibration, requires extra effort from the user to either perform a set of known movements, or enter information about the placement of the sensors manually. In this paper, we propose a camera-assisted calibration approach that does not require any extra effort from the user. The calibration is done seamlessly when the user appears in front of the camera (in our case, a Kinect camera) and performs an arbitrary activity of choice (e.g., walking in front of the camera). We provide experimental results supporting the effectiveness of our approach.

  18. Zero-Effort Camera-Assisted Calibration Techniques for Wearable Motion Sensors.

    Science.gov (United States)

    Wu, Jian; Jafari, Roozbeh

    2014-10-01

    Activity recognition using wearable motion sensors plays an important role in pervasive wellness and healthcare monitoring applications. The activity recognition algorithms are often designed to work with a known orientation of sensors on the body. In the case of accidental displacement of the motion sensors, it is important to identify the new sensor location and orientation. This step, often called calibration or recalibration, requires extra effort from the user to either perform a set of known movements, or enter information about the placement of the sensors manually. In this paper, we propose a camera-assisted calibration approach that does not require any extra effort from the user. The calibration is done seamlessly when the user appears in front of the camera (in our case, a Kinect camera) and performs an arbitrary activity of choice (e.g., walking in front of the camera). We provide experimental results supporting the effectiveness of our approach.

  19. A Wearable Sensor System for Monitoring Cigarette Smoking

    Science.gov (United States)

    Sazonov, Edward; Lopez-Meyer, Paulo; Tiffany, Stephen

    2013-01-01

    Objective: Available methods of smoking assessment (e.g., self-report, portable puff-topography instruments) do not permit the collection of accurate measures of smoking behavior while minimizing reactivity to the assessment procedure. This article suggests a new method for monitoring cigarette smoking based on a wearable sensor system (Personal Automatic Cigarette Tracker [PACT]) that is completely transparent to the end user and does not require any conscious effort to achieve reliable monitoring of smoking in free-living individuals. Method: The proposed sensor system consists of a respiratory inductance plethysmograph for monitoring of breathing and a hand gesture sensor for detecting a cigarette at the mouth. The wearable sensor system was tested in a laboratory study of 20 individuals who performed 12 different activities including cigarette smoking. Signal processing was applied to evaluate the uniqueness of breathing patterns and their correlation with hand gestures. Results: The results indicate that smoking manifests unique breathing patterns that are highly correlated with hand-to-mouth cigarette gestures and suggest that these signals can potentially be used to identify and characterize individual smoke inhalations. Conclusions: With the future development of signal processing and pattern-recognition methods, PACT can be used to automatically assess the frequency of smoking and inhalation patterns (such as depth of inhalation and smoke holding) throughout the day and provide an objective method of assessing the effectiveness of behavioral and pharmacological smoking interventions. PMID:24172124

  20. Wearable carbon nanotube-based fabric sensors for monitoring human physiological performance

    Science.gov (United States)

    Wang, Long; Loh, Kenneth J.

    2017-05-01

    A target application of wearable sensors is to detect human motion and to monitor physical activity for improving athletic performance and for delivering better physical therapy. In addition, measuring human vital signals (e.g., respiration rate and body temperature) provides rich information that can be used to assess a subject’s physiological or psychological condition. This study aims to design a multifunctional, wearable, fabric-based sensing system. First, carbon nanotube (CNT)-based thin films were fabricated by spraying. Second, the thin films were integrated with stretchable fabrics to form the fabric sensors. Third, the strain and temperature sensing properties of sensors fabricated using different CNT concentrations were characterized. Furthermore, the sensors were demonstrated to detect human finger bending motions, so as to validate their practical strain sensing performance. Finally, to monitor human respiration, the fabric sensors were integrated with a chest band, which was directly worn by a human subject. Quantification of respiration rates were successfully achieved. Overall, the fabric sensors were characterized by advantages such as flexibility, ease of fabrication, lightweight, low-cost, noninvasiveness, and user comfort.

  1. A Preliminary Test of Measurement of Joint Angles and Stride Length with Wireless Inertial Sensors for Wearable Gait Evaluation System

    Directory of Open Access Journals (Sweden)

    Takashi Watanabe

    2011-01-01

    Full Text Available The purpose of this study is to develop wearable sensor system for gait evaluation using gyroscopes and accelerometers for application to rehabilitation, healthcare and so on. In this paper, simultaneous measurement of joint angles of lower limbs and stride length was tested with a prototype of wearable sensor system. The system measured the joint angles using the Kalman filter. Signals from the sensor attached on the foot were used in the stride length estimation detecting foot movement automatically. Joint angles of the lower limbs were measured with stable and reasonable accuracy compared to those values measured with optical motion measurement system with healthy subjects. It was expected that the stride length measurement with the wearable sensor system would be practical by realizing more stable measurement accuracy. Sensor attachment position was suggested not to affect significantly measurement of slow and normal speed movements in a test with the rigid body model. Joint angle patterns measured in 10 m walking with a healthy subject were similar to common patterns. High correlation between joint angles at some characteristic points and stride velocity were also found adequately. These results suggested that the wireless wearable inertial sensor system could detect characteristics of gait.

  2. Comparing Metabolic Energy Expenditure Estimation Using Wearable Multi-Sensor Network and Single Accelerometer

    Science.gov (United States)

    Dong, Bo; Biswas, Subir; Montoye, Alexander; Pfeiffer, Karin

    2014-01-01

    This paper presents the implementation details, system architecture and performance of a wearable sensor network that was designed for human activity recognition and energy expenditure estimation. We also included ActiGraph GT3X+ as a popular single sensor solution for detailed comparison with the proposed wearable sensor network. Linear regression and Artificial Neural Network are implemented and tested. Through a rigorous system study and experiment, it is shown that the wearable multi-sensor network outperforms the single sensor solution in terms of energy expenditure estimation. PMID:24110325

  3. Integration of biochemical sensors into wearable biomaterial platforms

    Science.gov (United States)

    Jandhyala, Sidhartha; Walper, Scott A.; Cargill, Allison A.; Ozual, Abigail; Daniele, Michael A.

    2016-05-01

    With rapidly inflating healthcare costs, a limited supply of physicians and an alarming surge in lifestyle diseases, radical changes must be made to improve preventative medicine and ensure a sustainable healthcare system. A compelling solution is to equip the population with wearable health monitors to continuously record representative and actionable physiological data. Herein, we present a preliminary design and evaluation of a biochemical sensor node enabled by a substrate comprised of a nanocellulose thin-film that conforms to the skin and carries a printed sensor element. The nanocellulose layer ensures conformal and biocompatible contact with the skin, while a printed layer provides a high surface-area electrode. While the recognition/transduction element can be exchanged for many different sensing motifs, we utilize the general structure of an electrochemical glucose sensor.

  4. Wearable sensor network to study laterality of brain functions.

    Science.gov (United States)

    Postolache, Gabriela B; Girao, Pedro S; Postolache, Octavian A

    2015-08-01

    In the last decade researches on laterality of brain functions have been reinvigorated. New models of lateralization of brain functions were proposed and new methods for understanding mechanisms of asymmetry between right and left brain functions were described. We design a system to study laterality of motor and autonomic nervous system based on wearable sensors network. A mobile application was developed for analysis of upper and lower limbs movements, cardiac and respiratory function. The functionalities and experience gained with deployment of the system are described.

  5. 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.

  6. 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.

  7. Wearable sensor for heart rate detection

    Science.gov (United States)

    Shi, Cong; Liu, Xiaohua; Kong, Lingqin; Wu, Jizhe; Liu, Ming; Dong, Liquan; Hui, Mei; Zhao, Yuejin

    2015-08-01

    In recent years heart and blood vessel diseases kill more people than everything else combined. The daily test of heart rate for the prevention and treatment of the heart head blood-vessel disease has the vital significance. In order to adapt the transformation of medical model and solve the low accuracy problem of the traditional method of heart rate measuring, we present a new method to monitor heart rate in this paper. The heart rate detection is designed for daily heart rate detection .The heart rate signal is collected by the heart rate sensor. The signal through signal processing circuits converts into sine wave and square wave in turn. And then the signal is transmitted to the computer by data collection card. Finally, we use LABVIEW and MATLAB to show the heart rate wave and calculate the heart rate. By doing contrast experiment with medical heart rate product, experimental results show that the system can realize rapidly and accurately measure the heart rate value. A measurement can be completed within 10 seconds and the error is less than 3beat/min. And the result shows that the method in this paper has a strong anti-interference ability. It can effectively suppress the movement interference. Beyond that the result is insensitive to light.

  8. WearDY: Wearable dynamics. A prototype for human whole-body force and motion estimation

    Science.gov (United States)

    Latella, Claudia; Kuppuswamy, Naveen; Nori, Francesco

    2016-06-01

    Motion capture is a powerful tool used in a large range of applications towards human movement analysis. Although it is a well-established technique, its main limitation is the lack of dynamic information such as forces and torques during the motion capture. In this paper, we present a novel approach for human wearable dynamic (WearDY) motion capture for the simultaneous estimation of whole-body forces along with the motion. Our conceptual framework encompasses traditional passive markers based methods, inertial and contact force sensor modalities and harnesses a probabilistic computational framework for estimating dynamic quantities originally proposed in the domain of humanoid robot control. We present preliminary experimental analysis of our framework on subjects performing a two Degrees-of-Freedom bowing task and we estimate the motion and dynamic quantities. We discuss the implication of our proposal towards the design of a novel wearable force and motion capture suit and its applications.

  9. 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.

  10. A shoe-embedded piezoelectric energy harvester for wearable sensors.

    Science.gov (United States)

    Zhao, Jingjing; You, Zheng

    2014-07-11

    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.

  11. Impact of Sensor Misplacement on Dynamic Time Warping Based Human Activity Recognition using Wearable Computers.

    Science.gov (United States)

    Kale, Nimish; Lee, Jaeseong; Lotfian, Reza; Jafari, Roozbeh

    2012-10-01

    Daily living activity monitoring is important for early detection of the onset of many diseases and for improving quality of life especially in elderly. A wireless wearable network of inertial sensor nodes can be used to observe daily motions. Continuous stream of data generated by these sensor networks can be used to recognize the movements of interest. Dynamic Time Warping (DTW) is a widely used signal processing method for time-series pattern matching because of its robustness to variations in time and speed as opposed to other template matching methods. Despite this flexibility, for the application of activity recognition, DTW can only find the similarity between the template of a movement and the incoming samples, when the location and orientation of the sensor remains unchanged. Due to this restriction, small sensor misplacements can lead to a decrease in the classification accuracy. In this work, we adopt DTW distance as a feature for real-time detection of human daily activities like sit to stand in the presence of sensor misplacement. To measure this performance of DTW, we need to create a large number of sensor configurations while the sensors are rotated or misplaced. Creating a large number of closely spaced sensors is impractical. To address this problem, we use the marker based optical motion capture system and generate simulated inertial sensor data for different locations and orientations on the body. We study the performance of the DTW under these conditions to determine the worst-case sensor location variations that the algorithm can accommodate.

  12. 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.

  13. Development of a Wearable-Sensor-Based Fall Detection System

    Directory of Open Access Journals (Sweden)

    Falin Wu

    2015-01-01

    Full Text Available Fall detection is a major challenge in the public healthcare domain, especially for the elderly as the decline of their physical fitness, and timely and reliable surveillance is necessary to mitigate the negative effects of falls. This paper develops a novel fall detection system based on a wearable device. The system monitors the movements of human body, recognizes a fall from normal daily activities by an effective quaternion algorithm, and automatically sends request for help to the caregivers with the patient’s location.

  14. Development of a Wearable-Sensor-Based Fall Detection System

    Science.gov (United States)

    Zhao, Hengyang; Zhao, Yan; Zhong, Haibo

    2015-01-01

    Fall detection is a major challenge in the public healthcare domain, especially for the elderly as the decline of their physical fitness, and timely and reliable surveillance is necessary to mitigate the negative effects of falls. This paper develops a novel fall detection system based on a wearable device. The system monitors the movements of human body, recognizes a fall from normal daily activities by an effective quaternion algorithm, and automatically sends request for help to the caregivers with the patient's location. PMID:25784933

  15. 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.

  16. Wearable smart sensor systems integrated on soft contact lenses for wireless ocular diagnostics

    Science.gov (United States)

    Kim, Joohee; Kim, Minji; Lee, Mi-Sun; Kim, Kukjoo; Ji, Sangyoon; Kim, Yun-Tae; Park, Jihun; Na, Kyungmin; Bae, Kwi-Hyun; Kyun Kim, Hong; Bien, Franklin; Young Lee, Chang; Park, Jang-Ung

    2017-04-01

    Wearable contact lenses which can monitor physiological parameters have attracted substantial interests due to the capability of direct detection of biomarkers contained in body fluids. However, previously reported contact lens sensors can only monitor a single analyte at a time. Furthermore, such ocular contact lenses generally obstruct the field of vision of the subject. Here, we developed a multifunctional contact lens sensor that alleviates some of these limitations since it was developed on an actual ocular contact lens. It was also designed to monitor glucose within tears, as well as intraocular pressure using the resistance and capacitance of the electronic device. Furthermore, in-vivo and in-vitro tests using a live rabbit and bovine eyeball demonstrated its reliable operation. Our developed contact lens sensor can measure the glucose level in tear fluid and intraocular pressure simultaneously but yet independently based on different electrical responses.

  17. 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......We present the robot art and how it may inspire to create a new type of wearable termed modular robotic wearable. Differently from the related works, modular robotic wearable aims at making no use of mechatronic devices (as, for example, in Cyberpunk and related research branches) and mostly relies...

  18. Non-invasive physiological wearable sensor real time monitoring

    OpenAIRE

    Alharbi, Samah

    2015-01-01

    This project presents the implementation of reflectance Photoplethysmography (PPG) and thermo-chip sensor-¬based wireless architecture for a human health monitoring system. The thermo-¬‐chip sensor is used to continuously monitor the body temperature, while the reflectance PPG sensor is used to measure the heart rate by an optical technique that senses the blood volume change in the tissues and vessels. The sensors outputs are then given to the signal conditioning circuit used to filter the n...

  19. Three Dimensional Gait Analysis Using Wearable Acceleration and Gyro Sensors Based on Quaternion Calculations

    Directory of Open Access Journals (Sweden)

    Hiroaki Miyagawa

    2013-07-01

    Full Text Available This paper proposes a method for three dimensional gait analysis using wearable sensors and quaternion calculations. Seven sensor units consisting of a tri-axial acceleration and gyro sensors, were fixed to the lower limbs. The acceleration and angular velocity data of each sensor unit were measured during level walking. The initial orientations of the sensor units were estimated using acceleration data during upright standing position and the angular displacements were estimated afterwards using angular velocity data during gait. Here, an algorithm based on quaternion calculation was implemented for orientation estimation of the sensor units. The orientations of the sensor units were converted to the orientations of the body segments by a rotation matrix obtained from a calibration trial. Body segment orientations were then used for constructing a three dimensional wire frame animation of the volunteers during the gait. Gait analysis was conducted on five volunteers, and results were compared with those from a camera-based motion analysis system. Comparisons were made for the joint trajectory in the horizontal and sagittal plane. The average RMSE and correlation coefficient (CC were 10.14 deg and 0.98, 7.88 deg and 0.97, 9.75 deg and 0.78 for the hip, knee and ankle flexion angles, respectively.

  20. Wearable Sensors in Huntington Disease: A Pilot Study.

    Science.gov (United States)

    Andrzejewski, Kelly L; Dowling, Ariel V; Stamler, David; Felong, Timothy J; Harris, Denzil A; Wong, Cynthia; Cai, Hang; Reilmann, Ralf; Little, Max A; Gwin, Joseph T; Biglan, Kevin M; Dorsey, E Ray

    2016-06-18

    The Unified Huntington's Disease Rating Scale (UHDRS) is the principal means of assessing motor impairment in Huntington disease but is subjective and generally limited to in-clinic assessments. To evaluate the feasibility and ability of wearable sensors to measure motor impairment in individuals with Huntington disease in the clinic and at home. Participants with Huntington disease and controls were asked to wear five accelerometer-based sensors attached to the chest and each limb for standardized, in-clinic assessments and for one day at home. A second chest sensor was worn for six additional days at home. Gait measures were compared between controls, participants with Huntington disease, and participants with Huntington disease grouped by UHDRS total motor score using Cohen's d values. Fifteen individuals with Huntington disease and five controls completed the study. Sensor data were successfully captured from 18 of the 20 participants at home. In the clinic, the standard deviation of step time (time between consecutive steps) was increased in Huntington disease (p Huntington disease, and participants with Huntington disease grouped by motor impairment.

  1. Quantitative wearable sensors for objective assessment of Parkinson's disease

    NARCIS (Netherlands)

    Maetzler, W.; Domingos, J.; Srulijes, K.; Ferreira, J.J.; Bloem, B.R.

    2013-01-01

    There is a rapidly growing interest in the quantitative assessment of Parkinson's disease (PD)-associated signs and disability using wearable technology. Both persons with PD and their clinicians see advantages in such developments. Specifically, quantitative assessments using wearable technology

  2. 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.

  3. Wearable technology for biomechanics: e-textile or micromechanical sensors?

    Science.gov (United States)

    De Rossi, Danilo; Veltink, Peter

    2010-01-01

    The possibility of gathering reliable information about movement characteristics during activities of daily living holds particular appeal for researchers. Data such as this could be used to analyze the performance of individuals undergoing rehabilitation and to provide vital information on whether or not there is an improvement during a neurorehabilitation protocol. Wearable devices are particularly promising toward this aim, because they can be used in unstructured environments (e.g., at home). Recently, two different approaches in this area have become very popular and show promising performance: the use of inertial sensors together with advanced algorithms (e.g., Kalman filters) and the development of e-textile, in which the sensing technology is directly embroidered into the garment worn by the user.

  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. Self-Healable Sensors Based Nanoparticles for Detecting Physiological Markers via Skin and Breath: Toward Disease Prevention via Wearable Devices.

    Science.gov (United States)

    Jin, Han; Huynh, Tan-Phat; Haick, Hossam

    2016-07-13

    Flexible and wearable electronic sensors are useful for the early diagnosis and monitoring of an individual's health state. Sampling of volatile organic compounds (VOCs) derived from human breath/skin or monitoring abrupt changes in heart-beat/breath rate should allow noninvasive monitoring of disease states at an early stage. Nevertheless, for many reported wearable sensing devices, interaction with the human body leads incidentally to unavoidable scratches and/or mechanical cuts and bring about malfunction of these devices. We now offer proof-of-concept of nanoparticle-based flexible sensor arrays with fascinating self-healing abilities. By integrating a self-healable polymer substrate with 5 kinds of functionalized gold nanoparticle films, a sensor array gives a fast self-healing (device, with a desirable performance in the possible detection and/or clinical application for a number of different purposes.

  6. 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.

  7. Gait Kinematic Analysis in Water Using Wearable Inertial Magnetic Sensors: e0138105

    National Research Council Canada - National Science Library

    Silvia Fantozzi; Andrea Giovanardi; Davide Borra; Giorgio Gatta

    2015-01-01

    .... 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...

  8. A low power wearable transceiver for human body communication.

    Science.gov (United States)

    Huang, Jin; Chen, Lian-Kang; Zhang, Yuan-Ting

    2009-01-01

    This paper reports a low power transceiver designed for wearable medical healthcare system. Based on a novel energy-efficient wideband wireless communication scheme that uses human body as a transmission medium, the transceiver can achieve a maximum 15 Mbps data rate with total receiver sensitivity of -30 dBm. The chip measures only 0.56 mm(2) and was fabricated in the SMIC 0.18um 1P6M RF CMOS process. The RX consumes 5mW and TX dissipates 1mW with delivering power up to 10uW, which is suitable for the body area network short range application. Real-time medical information collecting through the human body is fully simulated. Architecture of the chip together with the detail characterizes from its wireless analog front-end are presented.

  9. Wearable chemical sensing – sensor design and sampling techniques for real-time sweat analysis

    OpenAIRE

    2014-01-01

    Wearable chemical sensors have the potential to provide new methods of non-invasive physiological measurement. The nature of chemical sensors involves an active surface where a chemical reaction must occur to elicit a response. This adds complexity to a wearable system which creates challenges in the design of a reliable long-term working system. This work presents the design of a real-time sweat sensing platform to analyse sweat loss and composition. Sampling methods have an impact on...

  10. Gait analysis using gravitational acceleration measured by wearable sensors.

    Science.gov (United States)

    Takeda, Ryo; Tadano, Shigeru; Todoh, Masahiro; Morikawa, Manabu; Nakayasu, Minoru; Yoshinari, Satoshi

    2009-02-09

    A novel method for measuring human gait posture using wearable sensor units is proposed. The sensor units consist of a tri-axial acceleration sensor and three gyro sensors aligned on three axes. The acceleration and angular velocity during walking were measured with seven sensor units worn on the abdomen and the lower limb segments (both thighs, shanks and feet). The three-dimensional positions of each joint are calculated from each segment length and joint angle. Joint angle can be estimated mechanically from the gravitational acceleration along the anterior axis of the segment. However, the acceleration data during walking includes three major components; translational acceleration, gravitational acceleration and external noise. Therefore, an optimization analysis was represented to separate only the gravitational acceleration from the acceleration data. Because the cyclic patterns of acceleration data can be found during constant walking, a FFT analysis was applied to obtain some characteristic frequencies in it. A pattern of gravitational acceleration was assumed using some parts of these characteristic frequencies. Every joint position was calculated from the pattern under the condition of physiological motion range of each joint. An optimized pattern of the gravitational acceleration was selected as a solution of an inverse problem. Gaits of three healthy volunteers were measured by walking for 20s on a flat floor. As a result, the acceleration data of every segment was measured simultaneously. The characteristic three-dimensional walking could be shown by the expression using a stick figure model. In addition, the trajectories of the knee joint in the horizontal plane could be checked by visual imaging on a PC. Therefore, this method provides important quantitive information for gait diagnosis.

  11. A level-crossing based QRS-detection algorithm for wearable ECG sensors.

    Science.gov (United States)

    Ravanshad, Nassim; Rezaee-Dehsorkh, Hamidreza; Lotfi, Reza; Lian, Yong

    2014-01-01

    In this paper, an asynchronous analog-to-information conversion system is introduced for measuring the RR intervals of the electrocardiogram (ECG) signals. The system contains a modified level-crossing analog-to-digital converter and a novel algorithm for detecting the R-peaks from the level-crossing sampled data in a compressed volume of data. Simulated with MIT-BIH Arrhythmia Database, the proposed system delivers an average detection accuracy of 98.3%, a sensitivity of 98.89%, and a positive prediction of 99.4%. Synthesized in 0.13 μm CMOS technology with a 1.2 V supply voltage, the overall system consumes 622 nW with core area of 0.136 mm (2), which make it suitable for wearable wireless ECG sensors in body-sensor networks.

  12. 一种用于癫痫发作预测的可穿戴无线传感器%A Wearable Wireless Body Sensor for Epileptic Seizure Prediction

    Institute of Scientific and Technical Information of China (English)

    张根选; 张莉; 石波; 曹阳; 刘开放

    2016-01-01

    该文设计了一种贴片可穿戴式心电传感器(Wearable Patch-type ECG Sensor.WPES),主要由电极,心电采集模块、蓝牙模块和电源模块等部分组成.WPES以BMD101芯片为核心进行心电采集,通过低功耗蓝牙实现与手机之间的通信.WPES具有电路简单、体积小、重量轻、功耗低、穿戴方便、舒适度高等特点,可以作为传感器节点用于网络化癫痫发作预测.

  13. Monitoring of posture allocations and activities by a shoe-based wearable sensor.

    Science.gov (United States)

    Sazonov, Edward S; Fulk, George; Hill, James; Schutz, Yves; Browning, Raymond

    2011-04-01

    Monitoring of posture allocations and activities enables accurate estimation of energy expenditure and may aid in obesity prevention and treatment. At present, accurate devices rely on multiple sensors distributed on the body and thus may be too obtrusive for everyday use. This paper presents a novel wearable sensor, which is capable of very accurate recognition of common postures and activities. The patterns of heel acceleration and plantar pressure uniquely characterize postures and typical activities while requiring minimal preprocessing and no feature extraction. The shoe sensor was tested in nine adults performing sitting and standing postures and while walking, running, stair ascent/descent and cycling. Support vector machines (SVMs) were used for classification. A fourfold validation of a six-class subject-independent group model showed 95.2% average accuracy of posture/activity classification on full sensor set and over 98% on optimized sensor set. Using a combination of acceleration/pressure also enabled a pronounced reduction of the sampling frequency (25 to 1 Hz) without significant loss of accuracy (98% versus 93%). Subjects had shoe sizes (US) M9.5-11 and W7-9 and body mass index from 18.1 to 39.4 kg/m2 and thus suggesting that the device can be used by individuals with varying anthropometric characteristics.

  14. 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...... inhabitants in a real home and they often perform activities together. In this paper, we investigate the problem of recognizing multi-user activities using wearable sensors in a home setting. We develop a multi-modal, wearable sensor platform to collect sensor data for multiple users, and study two temporal...... probabilistic models—Coupled Hidden Markov Model (CHMM) and Factorial Conditional Random Field (FCRF)—to model interacting processes in a sensor-based, multi-user scenario. We conduct a real-world trace collection done by two subjects over two weeks, and evaluate these two models through our experimental...

  15. Weakly Supervised Recognition of Daily Life Activities with Wearable Sensors.

    Science.gov (United States)

    Stikic, Maja; Larlus, Diane; Ebert, Sandra; Schiele, Bernt

    2011-12-01

    This paper considers scalable and unobtrusive activity recognition using on-body sensing for context awareness in wearable computing. Common methods for activity recognition rely on supervised learning requiring substantial amounts of labeled training data. Obtaining accurate and detailed annotations of activities is challenging, preventing the applicability of these approaches in real-world settings. This paper proposes new annotation strategies that substantially reduce the required amount of annotation. We explore two learning schemes for activity recognition that effectively leverage such sparsely labeled data together with more easily obtainable unlabeled data. Experimental results on two public data sets indicate that both approaches obtain results close to fully supervised techniques. The proposed methods are robust to the presence of erroneous labels occurring in real-world annotation data.

  16. Estimation of Center of Mass Trajectory using Wearable Sensors during Golf Swing

    Directory of Open Access Journals (Sweden)

    Bijan Najafi, Jacqueline Lee-Eng, James S. Wrobel, Ruben Goebel

    2015-06-01

    Full Text Available This study suggests a wearable sensor technology to estimate center of mass (CoM trajectory during a golf swing. Groups of 3, 4, and 18 participants were recruited, respectively, for the purpose of three validation studies. Study 1 examined the accuracy of the system to estimate a 3D body segment angle compared to a camera-based motion analyzer (Vicon®. Study 2 assessed the accuracy of three simplified CoM trajectory models. Finally, Study 3 assessed the accuracy of the proposed CoM model during multiple golf swings. A relatively high agreement was observed between wearable sensors and the reference (Vicon® for angle measurement (r > 0.99, random error 0.93 v. r = 0.52, respectively. On the same note, the proposed two-link model estimated CoM trajectory during golf swing with relatively good accuracy (r > 0.9, A-P random error <1cm (7.7% and <2cm (10.4% for M-L. The proposed system appears to accurately quantify the kinematics of CoM trajectory as a surrogate of dynamic postural control during an athlete’s movement and its portability, makes it feasible to fit the competitive environment without restricting surface type.

  17. Recent advances in wearable sensors for animal health management

    Directory of Open Access Journals (Sweden)

    Suresh Neethirajan

    2017-02-01

    Full Text Available Biosensors, as an application for animal health management, are an emerging market that is quickly gaining recognition in the global market. Globally, a number of sensors being produced for animal health management are at various stages of commercialization. Some technologies for producing an accurate health status and disease diagnosis are applicable only for humans, with few modifications or testing in animal models. Now, these innovative technologies are being considered for their future use in livestock development and welfare. Precision livestock farming techniques, which include a wide span of technologies, are being applied, along with advanced technologies like microfluidics, sound analyzers, image-detection techniques, sweat and salivary sensing, serodiagnosis, and others. However, there is a need to integrate all the available sensors and create an efficient online monitoring system so that animal health status can be monitored in real time, without delay. This review paper discusses the scope of different wearable technologies for animals, nano biosensors and advanced molecular biology diagnostic techniques for the detection of various infectious diseases of cattle, along with the efforts to enlist and compare these technologies with respect to their drawbacks and advantages in the domain of animal health management. The paper considers all recent developments in the field of biosensors and their applications for animal health to provide insight regarding the appropriate approach to be used in the future of enhanced animal welfare.

  18. The Routing Algorithm Based on Fuzzy Logic Applied to the Individual Physiological Monitoring Wearable Wireless Sensor Network

    OpenAIRE

    Jie Jiang; Yun Liu; Fuxing Song; Ronghao Du; Mengsen Huang

    2015-01-01

    In recent years, the research of individual wearable physiological monitoring wireless sensor network is in the primary stage. The monitor of physiology and geographical position used in wearable wireless sensor network requires performances such as real time, reliability, and energy balance. According to these requirements, this paper introduces a design of individual wearable wireless sensor network monitoring system; what is more important, based on this background, this paper improves the...

  19. Wearable wireless tactile display for virtual interactions with soft bodies

    Directory of Open Access Journals (Sweden)

    Gabriele eFrediani

    2014-09-01

    Full Text Available We describe here a wearable, wireless, compact and lightweight tactile display, able to mechanically stimulate the fingertip of users, so as to simulate contact with soft bodies in virtual environments. The device was based on dielectric elastomer actuators, as high-performance electromechanically active polymers. The actuator was arranged at the user’s fingertip, integrated within a plastic case, which also hosted a compact high-voltage circuitry. A custom-made wireless control unit was arranged on the forearm and connected to the display via low-voltage leads. We present the structure of the device and a characterization of it, in terms of electromechanical response and stress relaxation. Furthermore, we present results of a psychophysical test aimed at assessing the ability of the system to generate different levels of force that can be perceived by users.

  20. Quantitative wearable sensors for objective assessment of Parkinson's disease

    NARCIS (Netherlands)

    Maetzler, W.; Domingos, J.; Srulijes, K.; Ferreira, J.J.; Bloem, B.R.

    2013-01-01

    There is a rapidly growing interest in the quantitative assessment of Parkinson's disease (PD)-associated signs and disability using wearable technology. Both persons with PD and their clinicians see advantages in such developments. Specifically, quantitative assessments using wearable technology ma

  1. Congestive heart failure patient monitoring using wearable Bio-impedance sensor technology.

    Science.gov (United States)

    Seulki Lee; Squillace, Gabriel; Smeets, Christophe; Vandecasteele, Marianne; Grieten, Lars; de Francisco, Ruben; Van Hoof, Chris

    2015-08-01

    A new technique to monitor the fluid status of congestive heart failure (CHF) patients in the hospital is proposed and verified in a clinical trial with 8 patients. A wearable Bio-impedance (BioZ) sensor allows a continuous localized measurement which can be complement clinical tools in the hospital. Thanks to the multi-parametric approach and correlation analysis with clinical reference, BioZ is successfully shown as a promising parameter for continuous and wearable CHF patient monitoring application.

  2. Three-dimensional multi-recognition flexible wearable sensor via graphene aerogel printing.

    Science.gov (United States)

    An, Boxing; Ma, Ying; Li, Wenbo; Su, Meng; Li, Fengyu; Song, Yanlin

    2016-09-21

    Multi-response, multi-function and high integration are the critical pursuits of advanced electronic wearable sensors. Graphene aerogel endows a three-dimensional (3D) deformation morphology with excellent flexible wearable electronics of sheeted graphene. Here we report the fabrication of a neat graphene aerogel with micro extrusion printing to electronic sensor devices with a 3D nanostructure. The printed neat graphene patterns have excellent conductivity and the controllable 3D nanostructure of graphene aerogel contributes multi-dimensional deformation responses, which are appropriately suitable for the multi-recognition flexible wearable electric sensor. With complicated movement perception, the printed graphene aerogel sensors run the remarkable gesture language analysis for a deaf-mute communication auxiliary device or gesture manipulation apparatuses.

  3. iCalm: wearable sensor and network architecture for wirelessly communicating and logging autonomic activity.

    NARCIS (Netherlands)

    Fletcher, R.R.; Dobson, K.; Goodwin, M.S.; Eydgahi, H.; Wilder-Smith, O.H.G.; Fernholz, D.; Kuboyama, Y.; Hedman, E.B.; Poh, M.Z.; Picard, R.W.

    2010-01-01

    Widespread use of affective sensing in healthcare applications has been limited due to several practical factors, such as lack of comfortable wearable sensors, lack of wireless standards, and lack of low-power affordable hardware. In this paper, we present a new low-cost, low-power wireless sensor

  4. MIMU-Wear: ontology-based sensor selection for real-world wearable activity recognition

    NARCIS (Netherlands)

    Villalonga, Claudia; Pomares, Hector; Rojas, Ignacio; Banos Legran, Oresti

    2017-01-01

    An enormous effort has been made during the recent years towards the recognition of human activity based on wearable sensors. Despite the wide variety of proposed systems, most existing solutions have in common to solely operate on predefined settings and constrained sensor setups. Real-world

  5. iCalm: wearable sensor and network architecture for wirelessly communicating and logging autonomic activity.

    NARCIS (Netherlands)

    Fletcher, R.R.; Dobson, K.; Goodwin, M.S.; Eydgahi, H.; Wilder-Smith, O.H.G.; Fernholz, D.; Kuboyama, Y.; Hedman, E.B.; Poh, M.Z.; Picard, R.W.

    2010-01-01

    Widespread use of affective sensing in healthcare applications has been limited due to several practical factors, such as lack of comfortable wearable sensors, lack of wireless standards, and lack of low-power affordable hardware. In this paper, we present a new low-cost, low-power wireless sensor p

  6. Analysis of indoor rowing motion using wearable inertial sensors

    NARCIS (Netherlands)

    Bosch, Stephan; Shoaib, Muhammad; Geerlings, Stephen; Buit, Lennart; Meratnia, Nirvana; Havinga, Paul

    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 experienc

  7. Highly stretchable and wearable graphene strain sensors with controllable sensitivity for human motion monitoring.

    Science.gov (United States)

    Park, Jung Jin; Hyun, Woo Jin; Mun, Sung Cik; Park, Yong Tae; Park, O Ok

    2015-03-25

    Because of their outstanding electrical and mechanical properties, graphene strain sensors have attracted extensive attention for electronic applications in virtual reality, robotics, medical diagnostics, and healthcare. Although several strain sensors based on graphene have been reported, the stretchability and sensitivity of these sensors remain limited, and also there is a pressing need to develop a practical fabrication process. This paper reports the fabrication and characterization of new types of graphene strain sensors based on stretchable yarns. Highly stretchable, sensitive, and wearable sensors are realized by a layer-by-layer assembly method that is simple, low-cost, scalable, and solution-processable. Because of the yarn structures, these sensors exhibit high stretchability (up to 150%) and versatility, and can detect both large- and small-scale human motions. For this study, wearable electronics are fabricated with implanted sensors that can monitor diverse human motions, including joint movement, phonation, swallowing, and breathing.

  8. 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.

  9. Flexible and Stretchable Physical Sensor Integrated Platforms for Wearable Human-Activity Monitoringand Personal Healthcare.

    Science.gov (United States)

    Trung, Tran Quang; Lee, Nae-Eung

    2016-06-01

    Flexible and stretchable physical sensors that can measure and quantify electrical signals generated by human activities are attracting a great deal of attention as they have unique characteristics, such as ultrathinness, low modulus, light weight, high flexibility, and stretchability. These flexible and stretchable physical sensors conformally attached on the surface of organs or skin can provide a new opportunity for human-activity monitoring and personal healthcare. Consequently, in recent years there has been considerable research effort devoted to the development of flexible and stretchable physical sensors to fulfill the requirements of future technology, and much progress has been achieved. Here, the most recent developments of flexible and stretchable physical sensors are described, including temperature, pressure, and strain sensors, and flexible and stretchable sensor-integrated platforms. The latest successful examples of flexible and stretchable physical sensors for the detection of temperature, pressure, and strain, as well as their novel structures, technological innovations, and challenges, are reviewed first. In the next section, recent progress regarding sensor-integrated wearable platforms is overviewed in detail. Some of the latest achievements regarding self-powered sensor-integrated wearable platform technologies are also reviewed. Further research direction and challenges are also proposed to develop a fully sensor-integrated wearable platform for monitoring human activity and personal healthcare in the near future.

  10. 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.

  11. Development of a belt-type wearable sensor system with multi-function for home health care

    Science.gov (United States)

    Ban, Yunho; Choi, Samjin; Jiang, Zhongwei; Park, Chanwon

    2005-12-01

    Some reports show that the physiological information measured in hospital is not enough without the one measured in home. The physiological information monitored in home, therefore, is strongly required recently. The goal of this research is to develop a wearable and tractable sensor system for detecting biomedical signals such as cardiac rhythm, respiration, body movement, and percentage of body fat (%BF) and for home health care. A belt type sensor for this purpose is developed, which consists of sensing materials of PVDF film and conductive fabrics. Also several data processing techniques, such as the discrete wavelet transform, cross correlation and adaptive filtering method, were introduced to eliminate noises and base wandering and to extract the specified components. The ECG and respiration signals obtained by the proposed belt type sensor system gave good agreements with commercial medical system. Furthermore, the body fat (%BF) measurement based on the four-electrode BIA was also built in the belt sensor. The body fat was calculated by measuring the body impedance from the belt type sensor and compared with the predicted %BF measured by the commercial adipometer (TBF-607). The results validated also the efficiency of the belt type sensor system.

  12. An Integrated Wireless Wearable Sensor System for Posture Recognition and Indoor Localization.

    Science.gov (United States)

    Huang, Jian; Yu, Xiaoqiang; Wang, Yuan; Xiao, Xiling

    2016-10-31

    In order to provide better monitoring for the elderly or patients, we developed an integrated wireless wearable sensor system that can realize posture recognition and indoor localization in real time. Five designed sensor nodes which are respectively fixed on lower limbs and a standard Kalman filter are used to acquire basic attitude data. After the attitude angles of five body segments (two thighs, two shanks and the waist) are obtained, the pitch angles of the left thigh and waist are used to realize posture recognition. Based on all these attitude angles of body segments, we can also calculate the coordinates of six lower limb joints (two hip joints, two knee joints and two ankle joints). Then, a novel relative localization algorithm based on step length is proposed to realize the indoor localization of the user. Several sparsely distributed active Radio Frequency Identification (RFID) tags are used to correct the accumulative error in the relative localization algorithm and a set-membership filter is applied to realize the data fusion. The experimental results verify the effectiveness of the proposed algorithms.

  13. An Integrated Wireless Wearable Sensor System for Posture Recognition and Indoor Localization

    Directory of Open Access Journals (Sweden)

    Jian Huang

    2016-10-01

    Full Text Available In order to provide better monitoring for the elderly or patients, we developed an integrated wireless wearable sensor system that can realize posture recognition and indoor localization in real time. Five designed sensor nodes which are respectively fixed on lower limbs and a standard Kalman filter are used to acquire basic attitude data. After the attitude angles of five body segments (two thighs, two shanks and the waist are obtained, the pitch angles of the left thigh and waist are used to realize posture recognition. Based on all these attitude angles of body segments, we can also calculate the coordinates of six lower limb joints (two hip joints, two knee joints and two ankle joints. Then, a novel relative localization algorithm based on step length is proposed to realize the indoor localization of the user. Several sparsely distributed active Radio Frequency Identification (RFID tags are used to correct the accumulative error in the relative localization algorithm and a set-membership filter is applied to realize the data fusion. The experimental results verify the effectiveness of the proposed algorithms.

  14. Quantitative wearable sensors for objective assessment of Parkinson's disease.

    Science.gov (United States)

    Maetzler, Walter; Domingos, Josefa; Srulijes, Karin; Ferreira, Joaquim J; Bloem, Bastiaan R

    2013-10-01

    There is a rapidly growing interest in the quantitative assessment of Parkinson's disease (PD)-associated signs and disability using wearable technology. Both persons with PD and their clinicians see advantages in such developments. Specifically, quantitative assessments using wearable technology may allow for continuous, unobtrusive, objective, and ecologically valid data collection. Also, this approach may improve patient-doctor interaction, influence therapeutic decisions, and ultimately ameliorate patients' global health status. In addition, such measures have the potential to be used as outcome parameters in clinical trials, allowing for frequent assessments; eg, in the home setting. This review discusses promising wearable technology, addresses which parameters should be prioritized in such assessment strategies, and reports about studies that have already investigated daily life issues in PD using this new technology.

  15. Stability of Enzymatic Biosensors for Wearable Applications.

    Science.gov (United States)

    Sonawane, Apurva; Manickam, Pandiaraj; Bhansali, Shekhar

    2017-05-19

    Technological evolution in wearable sensors is accounting for major growth and transformation in multitude of industries ranging from healthcare to computing & informatics to communication and biomedical sciences. The major driver for this transformation is the new-found ability to continuously monitor and analyze the patients' physiology in patients' natural setting. Numerous wearable sensors are already on the market and are summarized. Most of the current technologies have focused on electro-physiological, electro-mechanical or acoustic measurements. Wearable bio-chemical sensing devices are in their infancy. Traditional challenges in biochemical sensing such as reliability, repeatability, stability, and drift are amplified in wearable sensing systems due to variabilities in operating environment, sample/sensor handling and motion artifacts. Enzymatic sensing technologies, due to reduced fluidic challenges continue to be forerunners for translation into wearable sensors. This paper reviews the recent developments in wearable enzymatic sensors. The wearable sensors have been classified in three major groups based on sensor embodiment and placement relative to the human body: (i) On-body, (ii) Clothing/textile-based biosensors and (iii) Biosensor accessories. The sensors, which come in the forms of stickers, tattoos are categorized as on-body biosensors. The fabric-based biosensor comes in different models such as smart-shirts, socks, gloves and smart undergarments with printed sensors for continuous monitoring.

  16. 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

    2017-01-01

    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. PMID:28216556

  17. 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.

  18. 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.

  19. 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

  20. Printing of CNT/silicone rubber for a wearable flexible stretch sensor

    Science.gov (United States)

    Kurian, Agee S.; Giffney, Tim; Lee, Jim; Travas-Sejdic, Jadranka; Aw, Kean C.

    2016-04-01

    In this paper, we present a simple printing method for a highly resilient stretch sensor. The stretch sensors, based on multi-walled carbon nanotubes (MWCNT)/silicon rubber (Ecoflex® 00-30) polymer nanocomposites, were printed on silicon rubber (SR) substrate. The sensors exhibit good hysteresis with high linearity and small drift. Due to the biocompatibility of SR and is very soft, strong and able to be stretched many times its original size without tearing and will rebound to its original form without distortion, the proposed stretch sensor is suitable for many biomedical and wearable sensors application.

  1. Physiological monitoring in firefighter ensembles: wearable plethysmographic sensor vest versus standard equipment.

    Science.gov (United States)

    Coca, Aitor; Roberge, Raymond J; Williams, W Jon; Landsittel, Douglas P; Powell, Jeffrey B; Palmiero, Andrew

    2010-02-01

    We evaluated the accuracy of a wearable sensor vest for real-time monitoring of physiological responses to treadmill exercise. Ten subjects in standard firefighter ensembles, treadmill exercising at 50% VO(2) max, had heart rate (HR), respiratory rate (RR), skin temperature (T(sk)), oxygen saturation (SaO(2)), tidal volume (V(T)), and minute ventilation (V(E)) recorded concurrently by a wearable plethysmographic sensor vest and standard laboratory physiological monitoring equipment for comparison. A high degree of correlation was noted for most of the measured variables [HR (r = 0.99), RR (r = 0.98), T(sk) (r = 0.98), V(E) (r = 0.88), and SaO(2) (r = 0.79)]. V(T) (r = 0.60) had a moderate correlation, although a paired differences analysis showed a mean paired difference of -0.03 L. This mean paired difference represents a 1.92% variation for V(T). Data from the wearable sensor vest is comparable to data captured from standard laboratory physiological monitoring equipment on subjects wearing standard firefighter ensembles while exercising at a moderate work rate. This study demonstrates the accuracy of the wearable sensor technology for these physiological parameters under these conditions and suggests that it could be useful for actual field studies of firefighters in traditional firefighting gear.

  2. 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.

  3. COMPOSE: Using temporal patterns for interpreting wearable sensor data with computer interpretable guidelines

    NARCIS (Netherlands)

    Urovi, Visara; Jimenez del Toro, Oscar; Dubosson, Fabian; Ruiz Torres, Alehandra; Schumacher, Michael Ignaz

    2017-01-01

    This paper describes a novel temporal logic-based framework for reasoning with continuous data collected from wearable sensors. The work is motivated by the Metabolic Syndrome, a cluster of conditions which are linked to obesity and unhealthy lifestyle. We assume that, by interpreting the physiologi

  4. UHF wearable battery free sensor module for activity and falling detection.

    Science.gov (United States)

    Nam Trung Dang; Thang Viet Tran; Wan-Young Chung

    2016-08-01

    Falling is one of the most serious medical and social problems in aging population. Therefore taking care of the elderly by detecting activity and falling for preventing and mitigating the injuries caused by falls needs to be concerned. This study proposes a wearable, wireless, battery free ultra-high frequency (UHF) smart sensor tag module for falling and activity detection. The proposed tag is powered by UHF RF wave from reader and read by a standard UHF Electronic Product Code (EPC) Class-1 Generation-2 reader. The battery free sensor module could improve the wearability of the wireless device. The combination of accelerometer signal and received signal strength indication (RSSI) from a reader in the passive smart sensor tag detect the activity and falling of the elderly very successfully. The fabricated smart sensor tag module has an operating range of up to 2.5m and conducting in real-time activity and falling detection.

  5. 基于FDTD方法的2.4/5.2/5.7 GHz穿戴式躯域传感器网络体表信道特征分析%On-body propagation characterization based on FDTD method for 2.4/5.2/5.7 GHz wearable body sensor networks

    Institute of Scientific and Technical Information of China (English)

    鲍淑娣; 沈连丰; 张元亭

    2007-01-01

    The on-body path loss and time delay of radio propagation in 2.4/5.2/5.7 GHz wearable body sensor networks (W-BSN) are studied using Remcom XFDTD, a simulation tool based on the finite-difference timedomain method. The simulation is performed in the environment of free space with a simplified threedimensional human body model. Results show that the path loss at a higher radio frequency is significantly smaller. Given that the transmitter and the receiver are located on the body trunk, the path loss relevant to the proposed minimum equivalent surface distance follows a log-fitting parametric model, and the path loss exponents are 4.7, 4.1 and 4.0 at frequencies of 2.4, 5.2, 5.7 GHz, respectively. On the other hand, the firstarrival delays are less than 2 ns at all receivers, and the maximum time delay spread is about 10 ns. As suggested by the maximum time delay spread, transmission rates of W-BSN must be less than 108 symbol/s to avoid intersymbol interference from multiple-path delay.%应用基于时域有限差分法(FDTD)的XFDTD仿真工具分析研究2.4/5.2/5.7 GHz穿戴式躯域传感器网络(W-BsN)的体表路径损耗和时延特性.仿真环境为置于自由空间中的简化三维人体模型.分析结果表明,当工作频率较高时,路径损耗相对较小;当发送点和接收点都置于主躯干时,路径损耗与最小等效体表距离遵循对数拟合模型,并且2.4,5.2,5.7 GHz下的衰减指数分别为4.7,4.1和4.0.另一方面,各接收点的首径延迟约小于2 ns,而最大时延扩展为10 ns.为避免多径延迟引起的码间干扰,建议W-BSN的传输速率应小于108符号/s.

  6. Effective low-power wearable wireless surface EMG sensor design based on analog-compressed sensing.

    Science.gov (United States)

    Balouchestani, Mohammadreza; Krishnan, Sridhar

    2014-12-17

    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.

  7. 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.

  8. 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.

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

    Science.gov (United States)

    Balouchestani, Mohammadreza; Krishnan, Sridhar

    2014-01-01

    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. PMID:25526357

  10. 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.

  11. Drift Removal for Improving the Accuracy of Gait Parameters Using Wearable Sensor Systems

    Directory of Open Access Journals (Sweden)

    Ryo Takeda

    2014-12-01

    Full Text Available Accumulated signal noise will cause the integrated values to drift from the true value when measuring orientation angles of wearable sensors. This work proposes a novel method to reduce the effect of this drift to accurately measure human gait using wearable sensors. Firstly, an infinite impulse response (IIR digital 4th order Butterworth filter was implemented to remove the noise from the raw gyro sensor data. Secondly, the mode value of the static state gyro sensor data was subtracted from the measured data to remove offset values. Thirdly, a robust double derivative and integration method was introduced to remove any remaining drift error from the data. Lastly, sensor attachment errors were minimized by establishing the gravitational acceleration vector from the acceleration data at standing upright and sitting posture. These improvements proposed allowed for removing the drift effect, and showed an average of 2.1°, 33.3°, 15.6° difference for the hip knee and ankle joint flexion/extension angle, when compared to without implementation. Kinematic and spatio-temporal gait parameters were also calculated from the heel-contact and toe-off timing of the foot. The data provided in this work showed potential of using wearable sensors in clinical evaluation of patients with gait-related diseases.

  12. Drift Removal for Improving the Accuracy of Gait Parameters Using Wearable Sensor Systems

    Science.gov (United States)

    Takeda, Ryo; Lisco, Giulia; Fujisawa, Tadashi; Gastaldi, Laura; Tohyama, Harukazu; Tadano, Shigeru

    2014-01-01

    Accumulated signal noise will cause the integrated values to drift from the true value when measuring orientation angles of wearable sensors. This work proposes a novel method to reduce the effect of this drift to accurately measure human gait using wearable sensors. Firstly, an infinite impulse response (IIR) digital 4th order Butterworth filter was implemented to remove the noise from the raw gyro sensor data. Secondly, the mode value of the static state gyro sensor data was subtracted from the measured data to remove offset values. Thirdly, a robust double derivative and integration method was introduced to remove any remaining drift error from the data. Lastly, sensor attachment errors were minimized by establishing the gravitational acceleration vector from the acceleration data at standing upright and sitting posture. These improvements proposed allowed for removing the drift effect, and showed an average of 2.1°, 33.3°, 15.6° difference for the hip knee and ankle joint flexion/extension angle, when compared to without implementation. Kinematic and spatio-temporal gait parameters were also calculated from the heel-contact and toe-off timing of the foot. The data provided in this work showed potential of using wearable sensors in clinical evaluation of patients with gait-related diseases. PMID:25490587

  13. Sheath-Core Graphite/Silk Fiber Made by Dry-Meyer-Rod-Coating for Wearable Strain Sensors.

    Science.gov (United States)

    Zhang, Mingchao; Wang, Chunya; Wang, Qi; Jian, Muqiang; Zhang, Yingying

    2016-08-17

    Recent years have witnessed the explosive development of flexible strain sensors. Nanomaterials have been widely utilized to fabricate flexible strain sensors, because of their high flexibility and electrical conductivity. However, the fabrication processes for nanomaterials and the subsequent strain sensors are generally complicated and are manufactured at high cost. In this work, we developed a facile dry-Meyer-rod-coating process to fabricate sheath-core-structured single-fiber strain sensors using ultrafine graphite flakes as the sheath and silk fibers as the core by virtue of their flexibility, high production, and low cost. The fabricated strain sensor exhibits a high sensitivity with a gauge factor of 14.5 within wide workable strain range up to 15%, and outstanding stability (up to 3000 cycles). The single-fiber-based strain sensors could be attached to a human body to detect joint motions or easily integrated into the multidirectional strain sensor for monitoring multiaxial strain, showing great potential applications as wearable strain sensors.

  14. PhysioDroid: Combining Wearable Health Sensors and Mobile Devices for a Ubiquitous, Continuous, and Personal Monitoring

    Directory of Open Access Journals (Sweden)

    Oresti Banos

    2014-01-01

    Full Text Available 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.

  15. 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.

  16. Robotic Art for Wearable

    DEFF Research Database (Denmark)

    Lund, Henrik Hautop; Pagliarini, Luigi

    2010-01-01

    We present the robot art and how it may inspire to create a new type of wearable termed modular robotic wearable. Differently from the related works, modular robotic wearable aims at making no use of mechatronic devices (as, for example, in Cyberpunk and related research branches) and mostly relies...... 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...... functions - in most of the cases strongly rigid, cabled and centralized - through the use of local sensing circuits. It is exemplified here with the early prototype art work called Fatherboard, and the concept is believed to be applicable to different application fields, such as sport, health...

  17. Highly wearable galvanic skin response sensor using flexible and conductive polymer foam.

    Science.gov (United States)

    Kim, Jeehoon; Kwon, Sungjun; Seo, Sangwon; Park, Kwangsuk

    2014-01-01

    Owing to advancements in daily physiological monitoring technology, diverse healthcare applications have emerged recently. The monitoring of skin conductance responses has extensive feasibility to support healthcare applications such as detecting emotion changes. In this study, we proposed a highly wearable and reliable galvanic skin response (GSR) sensor that measures the signals from the back of the user. To enhance its wearability and usability, we employed flexible conductive foam as the sensing material and designed it to be easily attachable to (and detachable from) a wide variety of clothes. We evaluated the sensing reliability of the proposed sensor by comparing its signal with a reference GSR. The average correlation between the two signals was 0.768; this is sufficiently high to validate the feasibility of the proposed sensor for reliable GSR sensing on the back.

  18. Improving Kinematic Accuracy of Soft Wearable Data Gloves by Optimizing Sensor Locations.

    Science.gov (United States)

    Kim, Dong Hyun; Lee, Sang Wook; Park, Hyung-Soon

    2016-05-26

    Bending sensors enable compact, wearable designs when used for measuring hand configurations in data gloves. While existing data gloves can accurately measure angular displacement of the finger and distal thumb joints, accurate measurement of thumb carpometacarpal (CMC) joint movements remains challenging due to crosstalk between the multi-sensor outputs required to measure the degrees of freedom (DOF). To properly measure CMC-joint configurations, sensor locations that minimize sensor crosstalk must be identified. This paper presents a novel approach to identifying optimal sensor locations. Three-dimensional hand surface data from ten subjects was collected in multiple thumb postures with varied CMC-joint flexion and abduction angles. For each posture, scanned CMC-joint contours were used to estimate CMC-joint flexion and abduction angles by varying the positions and orientations of two bending sensors. Optimal sensor locations were estimated by the least squares method, which minimized the difference between the true CMC-joint angles and the joint angle estimates. Finally, the resultant optimal sensor locations were experimentally validated. Placing sensors at the optimal locations, CMC-joint angle measurement accuracies improved (flexion, 2.8° ± 1.9°; abduction, 1.9° ± 1.2°). The proposed method for improving the accuracy of the sensing system can be extended to other types of soft wearable measurement devices.

  19. Improving Kinematic Accuracy of Soft Wearable Data Gloves by Optimizing Sensor Locations

    Directory of Open Access Journals (Sweden)

    Dong Hyun Kim

    2016-05-01

    Full Text Available Bending sensors enable compact, wearable designs when used for measuring hand configurations in data gloves. While existing data gloves can accurately measure angular displacement of the finger and distal thumb joints, accurate measurement of thumb carpometacarpal (CMC joint movements remains challenging due to crosstalk between the multi-sensor outputs required to measure the degrees of freedom (DOF. To properly measure CMC-joint configurations, sensor locations that minimize sensor crosstalk must be identified. This paper presents a novel approach to identifying optimal sensor locations. Three-dimensional hand surface data from ten subjects was collected in multiple thumb postures with varied CMC-joint flexion and abduction angles. For each posture, scanned CMC-joint contours were used to estimate CMC-joint flexion and abduction angles by varying the positions and orientations of two bending sensors. Optimal sensor locations were estimated by the least squares method, which minimized the difference between the true CMC-joint angles and the joint angle estimates. Finally, the resultant optimal sensor locations were experimentally validated. Placing sensors at the optimal locations, CMC-joint angle measurement accuracies improved (flexion, 2.8° ± 1.9°; abduction, 1.9° ± 1.2°. The proposed method for improving the accuracy of the sensing system can be extended to other types of soft wearable measurement devices.

  20. Flexible piezoelectric nanogenerator in wearable self-powered active sensor for respiration and healthcare monitoring

    Science.gov (United States)

    Liu, Z.; Zhang, S.; Jin, Y. M.; Ouyang, H.; Zou, Y.; Wang, X. X.; Xie, L. X.; Li, Z.

    2017-06-01

    A wearable self-powered active sensor for respiration and healthcare monitoring was fabricated based on a flexible piezoelectric nanogenerator. An electrospinning poly(vinylidene fluoride) thin film on silicone substrate was polarized to fabricate the flexible nanogenerator and its electrical property was measured. When periodically stretched by a linear motor, the flexible piezoelectric nanogenerator generated an output open-circuit voltage and short-circuit current of up to 1.5 V and 400 nA, respectively. Through integration with an elastic bandage, a wearable self-powered sensor was fabricated and used to monitor human respiration, subtle muscle movement, and voice recognition. As respiration proceeded, the electrical output signals of the sensor corresponded to the signals measured by a physiological signal recording system with good reliability and feasibility. This self-powered, wearable active sensor has significant potential for applications in pulmonary function evaluation, respiratory monitoring, and detection of gesture and vocal cord vibration for the personal healthcare monitoring of disabled or paralyzed patients.

  1. Flexible thermoelectric generator for ambient assisted living wearable biometric sensors

    Science.gov (United States)

    Francioso, L.; De Pascali, C.; Farella, I.; Martucci, C.; Cretì, P.; Siciliano, P.; Perrone, A.

    2011-03-01

    In this work we proposed design, fabrication and functional characterization of a very low cost energy autonomous, maintenance free, flexible and wearable micro thermoelectric generator (μTEG), finalized to power very low consumption electronics ambient assisted living (AAL) applications. The prototype, integrating an array of 100 thin films thermocouples of Sb2Te3 and Bi2Te3, generates, at 40 °C, an open circuit output voltage of 430 mV and an electrical output power up to 32 nW with matched load. In real operation conditions of prototype, which are believed to be very close to a thermal gradient of 15 °C, the device generates an open circuit output voltage of about 160 mV, with an electrical output power up to 4.18 nW. In the first part of work, deposition investigation Sb2Te3 and Bi2Te3 thin films alloys on Kapton HN polyimide foil by RF magnetron co-sputtering technique is discussed. Deposition parameters have been optimized to gain perfect stoichiometric ratio and high thermoelectric power factor; fabricated thermogenerator has been tested at low gradient conditioned to evaluate applications like human skin wearable power generator for ambient assisted living applications.

  2. In Vivo Evaluation of Wearable Head Impact Sensors.

    Science.gov (United States)

    Wu, Lyndia C; Nangia, Vaibhav; Bui, Kevin; Hammoor, Bradley; Kurt, Mehmet; Hernandez, Fidel; Kuo, Calvin; Camarillo, David B

    2016-04-01

    Inertial sensors are commonly used to measure human head motion. Some sensors have been tested with dummy or cadaver experiments with mixed results, and methods to evaluate sensors in vivo are lacking. Here we present an in vivo method using high speed video to test teeth-mounted (mouthguard), soft tissue-mounted (skin patch), and headgear-mounted (skull cap) sensors during 6-13 g sagittal soccer head impacts. Sensor coupling to the skull was quantified by displacement from an ear-canal reference. Mouthguard displacements were within video measurement error (sensor error, we found that in-plane skin patch linear acceleration in the anterior-posterior direction could be modeled by an underdamped viscoelastic system. In summary, the mouthguard showed tighter skull coupling than the other sensor mounting approaches. Furthermore, the in vivo methods presented are valuable for investigating skull acceleration sensor technologies.

  3. A Novel Method for Fabricating Wearable, Piezoresistive, and Pressure Sensors Based on Modified-Graphite/Polyurethane Composite Films

    Science.gov (United States)

    He, Yin; Li, Wei; Yang, Guilin; Liu, Hao; Lu, Junyu; Zheng, Tongtong; Li, Xiaojiu

    2017-01-01

    A wearable, low-cost, highly repeatable piezoresistive sensor was fabricated by the synthesis of modified-graphite and polyurethane (PU) composites and polydimethylsiloxane (PDMS). Graphite sheets functionalized by using a silane coupling agent (KH550) were distributed in PU/N,N-dimethylformamide (DMF) solution, which were then molded to modified-graphite/PU (MG/PU) composite films. Experimental results show that with increasing modified-graphite content, the tensile strength of the MG/PU films first increased and then decreased, and the elongation at break of the composite films showed a decreasing trend. The electrical conductivity of the composite films can be influenced by filler modification and concentration, and the percolation threshold of MG/PU was 28.03 wt %. Under liner uniaxial compression, the 30 wt % MG/PU composite films exhibited 0.274 kPa−1 piezoresistive sensitivity within the range of low pressure, and possessed better stability and hysteresis. The flexible MG/PU composite piezoresistive sensors have great potential for body motion, wearable devices for human healthcare, and garment pressure testing. PMID:28773047

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

    OpenAIRE

    Millings, A.; Morris, J.; Rowe, A.; Easton, S.; Martin, J. K.; Majoe, D.; Mohr, C.

    2015-01-01

    Background: Internet interventions for mental health concerns are known to be effective, but how can developing technology be utilised to improve engagement and augment the effectiveness of these programs? One option might be to incorporate feedback about the user's physiological state into the program, via wearable sensors. Objectives: This mixed-methods pilot study sought to examine whether the effectiveness of an online intervention for stress in students could be augmented by the use o...

  5. 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.

  6. A Wearable Mobile Sensor Platform to Assist Fruit Grading

    Directory of Open Access Journals (Sweden)

    Luiz M. G. Gonçalves

    2013-05-01

    Full Text Available Wearable computing is a form of ubiquitous computing that offers flexible and useful tools for users. Specifically, glove-based systems have been used in the last 30 years in a variety of applications, but mostly focusing on sensing people’s attributes, such as finger bending and heart rate. In contrast, we propose in this work a novel flexible and reconfigurable instrumentation platform in the form of a glove, which can be used to analyze and measure attributes of fruits by just pointing or touching them with the proposed glove. An architecture for such a platform is designed and its application for intuitive fruit grading is also presented, including experimental results for several fruits.

  7. A Wearable Mobile Sensor Platform to Assist Fruit Grading

    Science.gov (United States)

    Aroca, Rafael V.; Gomes, Rafael B.; Dantas, Rummennigue R.; Calbo, Adonai G.; Gonçalves, Luiz M. G.

    2013-01-01

    Wearable computing is a form of ubiquitous computing that offers flexible and useful tools for users. Specifically, glove-based systems have been used in the last 30 years in a variety of applications, but mostly focusing on sensing people's attributes, such as finger bending and heart rate. In contrast, we propose in this work a novel flexible and reconfigurable instrumentation platform in the form of a glove, which can be used to analyze and measure attributes of fruits by just pointing or touching them with the proposed glove. An architecture for such a platform is designed and its application for intuitive fruit grading is also presented, including experimental results for several fruits. PMID:23666134

  8. Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects Models.

    Science.gov (United States)

    Cresswell, Kellen Garrison; Shin, Yongyun; Chen, Shanshan

    2017-02-25

    The emerging technology of wearable inertial sensors has shown its advantages in collecting continuous longitudinal gait data outside laboratories. This freedom also presents challenges in collecting high-fidelity gait data. In the free-living environment, without constant supervision from researchers, sensor-based gait features are susceptible to variation from confounding factors such as gait speed and mounting uncertainty, which are challenging to control or estimate. This paper is one of the first attempts in the field to tackle such challenges using statistical modeling. By accepting the uncertainties and variation associated with wearable sensor-based gait data, we shift our efforts from detecting and correcting those variations to modeling them statistically. From gait data collected on one healthy, non-elderly subject during 48 full-factorial trials, we identified four major sources of variation, and quantified their impact on one gait outcome-range per cycle-using a random effects model and a fixed effects model. The methodology developed in this paper lays the groundwork for a statistical framework to account for sources of variation in wearable gait data, thus facilitating informative statistical inference for free-living gait analysis.

  9. Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects Models

    Directory of Open Access Journals (Sweden)

    Kellen Garrison Cresswell

    2017-02-01

    Full Text Available The emerging technology of wearable inertial sensors has shown its advantages in collecting continuous longitudinal gait data outside laboratories. This freedom also presents challenges in collecting high-fidelity gait data. In the free-living environment, without constant supervision from researchers, sensor-based gait features are susceptible to variation from confounding factors such as gait speed and mounting uncertainty, which are challenging to control or estimate. This paper is one of the first attempts in the field to tackle such challenges using statistical modeling. By accepting the uncertainties and variation associated with wearable sensor-based gait data, we shift our efforts from detecting and correcting those variations to modeling them statistically. From gait data collected on one healthy, non-elderly subject during 48 full-factorial trials, we identified four major sources of variation, and quantified their impact on one gait outcome—range per cycle—using a random effects model and a fixed effects model. The methodology developed in this paper lays the groundwork for a statistical framework to account for sources of variation in wearable gait data, thus facilitating informative statistical inference for free-living gait analysis.

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

    Science.gov (United States)

    Wang, Cheng; Wang, Xiangdong; Long, Zhou; Yuan, Jing; Qian, Yueliang; Li, Jintao

    2016-12-17

    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.

  13. 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

    2016-02-05

    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.

  14. 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.

  15. Accuracy of a Wearable Sensor for Measures of Head Kinematics and Calculation of Brain Tissue Strain.

    Science.gov (United States)

    Knowles, Brooklynn M; Yu, Henry; Dennison, Christopher R

    2017-02-01

    Wearable kinematic sensors can be used to study head injury biomechanics based on kinematics and, more recently, based on tissue strain metrics using kinematics-driven brain models. These sensors require in-situ calibration and there is currently no data conveying wearable ability to estimate tissue strain. We simulated head impact (n = 871) to a 50th percentile Hybrid III (H-III) head wearing a hockey helmet instrumented with wearable GForceTracker (GFT) sensors measuring linear acceleration and angular velocity. A GFT was also fixed within the H-III head to establish a lower boundary on systematic errors. We quantified GFT errors relative to H-III measures based on peak kinematics and cumulative strain damage measure (CSDM). The smallest mean errors were 12% (peak resultant linear acceleration) and 15% (peak resultant angular velocity) for the GFT within the H-III. Errors for GFTs on the helmet were on average 54% (peak resultant linear acceleration) and 21% (peak resultant angular velocity). On average, the GFT inside the helmet overestimated CSDM by 0.15.

  16. Screen printable flexible conductive nanocomposite polymer with applications to wearable sensors

    Science.gov (United States)

    Chung, D.; Khosla, A.; Gray, B. L.

    2014-04-01

    We have developed a conductive nanocomposite polymer that possesses both good conductivity and flexibility, and screen printed it onto fabric to realize wearable flexible electrodes and electronic routing. The conductive polymer consists of dispersed silver nanoparticles (90~210nm) in a screen printable plastisol polymer. The conductive polymer is conductive for weight-percentages above approximately 61 wt-% of Ag nanoparticles, and has a resistivity of 2.12×10-6 ohm·m at 70 wt-% of Ag nanoparticles. To test the screen printed conductive polymer's flexibility and its effect on conductivity, we measured the resistivity of the Ag-doped composite polymer at different bending angles (-90˚ ~ 90˚) with a 10° step angle at different wt-% of silver particles, and compared the results. We also tested washability of the screen printed conductive polymer as applied to fabric for long-term use in wearable sensors systems. We also used the screen printed Ag composite polymer to realize an example wearable system. Flexible wearable dry electrocardiogram (ECG) electrodes were developed and ECG signal was measured via the electrodes. The sensing ECG electrodes (3mm diameter circle) were chloridized to form Ag/AgCl electrodes. We measured an ECG signal using a simple right-leg driven ECG circuit and observed normal ECG signals even without application of electrolyte gel.

  17. A machine learning approach for gait speed estimation using skin-mounted wearable sensors: From healthy controls to individuals with multiple sclerosis

    National Research Council Canada - National Science Library

    Ryan S McGinnis; Nikhil Mahadevan; Yaejin Moon; Kirsten Seagers; Nirav Sheth; John A Wright Jr; Steven DiCristofaro; Ikaro Silva; Elise Jortberg; Melissa Ceruolo; Jesus A Pindado; Jacob Sosnoff; Roozbeh Ghaffari; Shyamal Patel

    2017-01-01

    .... Recent advances in wearable sensor technologies have fostered the development of new methods for monitoring parameters that characterize mobility impairment, such as gait speed, outside the clinic...

  18. Wireless body sensor networks for health-monitoring applications.

    Science.gov (United States)

    Hao, Yang; Foster, Robert

    2008-11-01

    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.

  19. 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.

  20. Laboratory Validation of Two Wearable Sensor Systems for Measuring Head Impact Severity in Football Players.

    Science.gov (United States)

    Siegmund, Gunter P; Guskiewicz, Kevin M; Marshall, Stephen W; DeMarco, Alyssa L; Bonin, Stephanie J

    2016-04-01

    Wearable sensors can measure head impact frequency and magnitude in football players. Our goal was to quantify the impact detection rate and validity of the direction and peak kinematics of two wearable sensors: a helmet system (HITS) and a mouthguard system (X2). Using a linear impactor, modified Hybrid-III headform and one helmet model, we conducted 16 impacts for each system at 12 helmet sites and 5 speeds (3.6-11.2 m/s) (N = 896 tests). Peak linear and angular accelerations (PLA, PAA), head injury criteria (HIC) and impact directions from each device were compared to reference sensors in the headform. Both sensors detected ~96% of impacts. Median angular errors for impact directions were 34° for HITS and 16° for X2. PLA, PAA and HIC were simultaneously valid at 2 sites for HITS (side, oblique) and one site for X2 (side). At least one kinematic parameter was valid at 2 and 7 other sites for HITS and X2 respectively. Median relative errors for PLA were 7% for HITS and -7% for X2. Although sensor validity may differ for other helmets and headforms, our analyses show that data generated by these two sensors need careful interpretation.

  1. The Routing Algorithm Based on Fuzzy Logic Applied to the Individual Physiological Monitoring Wearable Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Jie Jiang

    2015-01-01

    Full Text Available In recent years, the research of individual wearable physiological monitoring wireless sensor network is in the primary stage. The monitor of physiology and geographical position used in wearable wireless sensor network requires performances such as real time, reliability, and energy balance. According to these requirements, this paper introduces a design of individual wearable wireless sensor network monitoring system; what is more important, based on this background, this paper improves the classical Collection Tree Protocol and puts forward the improved routing protocol F-CTP based on the fuzzy logic routing algorithm. Simulation results illustrate that, with the F-CTP protocol, the sensor node can transmit data to the sink node in real time with higher reliability and the energy of the nodes consumes balance. The sensor node can make full use of network resources reasonably and prolong the network life.

  2. 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.

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

    Science.gov (United States)

    Banos, Oresti; Toth, Mate Attila; Damas, Miguel; Pomares, Hector; Rojas, Ignacio

    2014-01-01

    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. PMID:24915181

  4. Wearable autonomous microsystem with electrochemical gas sensor array for real-time health and safety monitoring.

    Science.gov (United States)

    Li, Haitao; Mu, Xiaoyi; Wang, Zhe; Liu, Xiaowen; Guo, Min; Jin, Rong; Zeng, Xiangqun; Mason, Andrew J

    2012-01-01

    Airborne pollution and explosive gases threaten human health and occupational safety, therefore generating high demand for a wearable autonomous multi-analyte gas sensor system for real-time environmental monitoring. This paper presents a system level solution through synergistic integration of sensors, electronics, and data analysis algorithms. Electrochemical sensors featuring ionic liquids were chosen to provide low-power room-temperature operation, rapid response, high sensitivity, good selectivity, and a long operating life with low maintenance. The system utilizes a multi-mode electrochemical instrumentation circuit that combines all signal condition functions within a single microelectronics chip to minimize system cost, size and power consumption. Embedded sensor array signal processing algorithms enable gas classification and concentration estimation within a real-world mixture of analytes. System design and integration methodologies are described, and preliminary results are shown for a first generation SO(2) sensor and a thumb-drive sized prototype system.

  5. 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.

  6. 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.

  7. A Compact Dual-Mode Wearable Antenna for Body-Centric Wireless Communications

    Directory of Open Access Journals (Sweden)

    Chia-Hsien Lin

    2014-07-01

    Full Text Available The miniaturization of electronic devices is leading to the creation of body-centric wireless communications (BCWCs, in which wireless devices are attached to the human body. In particular, personal healthcare is considered as the biggest potential application. In this paper, we propose a compact wearable dual-mode (on-body and off-body modes antenna for personal healthcare systems. For on-body mode at 10 MHz, received voltages are analyzed with a chest phantom, while for the off-body mode in the 2.4 GHz ISM band, reflection coefficient (S11 and radiation patterns are studied.

  8. Monitoring System for Farming Operations with Wearable Devices Utilized Sensor Networks

    Directory of Open Access Journals (Sweden)

    Tokihiro Fukatsu

    2009-08-01

    Full Text Available In order to automatically monitor farmers’ activities, we propose a farm operation monitoring system using “Field Servers” and a wearable device equipped with an RFID reader and motion sensors. Our proposed system helps in recognizing farming operations by analyzing the data from the sensors and detected RFID tags that are attached to various objects such as farming materials, facilities, and machinery. This method can be applied to various situations without changing the conventional system. Moreover, this system provides useful information in real-time and controls specific machines in a coordinated manner on the basis of recognized operation.

  9. Monitoring System for Farming Operations with Wearable Devices Utilized Sensor Networks

    Science.gov (United States)

    Fukatsu, Tokihiro; Nanseki, Teruaki

    2009-01-01

    In order to automatically monitor farmers’ activities, we propose a farm operation monitoring system using “Field Servers” and a wearable device equipped with an RFID reader and motion sensors. Our proposed system helps in recognizing farming operations by analyzing the data from the sensors and detected RFID tags that are attached to various objects such as farming materials, facilities, and machinery. This method can be applied to various situations without changing the conventional system. Moreover, this system provides useful information in real-time and controls specific machines in a coordinated manner on the basis of recognized operation. PMID:22454578

  10. A novel approach for chewing detection based on a wearable PPG sensor.

    Science.gov (United States)

    Papapanagiotou, Vasileios; Diou, Christos; Lingchuan Zhou; van den Boer, Janet; Mars, Monica; Delopoulos, Anastasios

    2016-08-01

    Monitoring of human eating behaviour has been attracting interest over the last few years, as a means to a healthy lifestyle, but also due to its association with serious health conditions, such as eating disorders and obesity. Use of self-reports and other non-automated means of monitoring have been found to be unreliable, compared to the use of wearable sensors. Various modalities have been reported, such as acoustic signal from ear-worn microphones, or signal from wearable strain sensors. In this work, we introduce a new sensor for the task of chewing detection, based on a novel photoplethysmography (PPG) sensor placed on the outer earlobe to perform the task. We also present a processing pipeline that includes two chewing detection algorithms from literature and one new algorithm, to process the captured PPG signal, and present their effectiveness. Experiments are performed on an annotated dataset recorded from 21 individuals, including more than 10 hours of eating and non-eating activities. Results show that the PPG sensor can be successfully used to support dietary monitoring.

  11. Ubiquitous human upper-limb motion estimation using wearable sensors.

    Science.gov (United States)

    Zhang, Zhi-Qiang; Wong, Wai-Choong; Wu, Jian-Kang

    2011-07-01

    Human motion capture technologies have been widely used in a wide spectrum of applications, including interactive game and learning, animation, film special effects, health care, navigation, and so on. The existing human motion capture techniques, which use structured multiple high-resolution cameras in a dedicated studio, are complicated and expensive. With the rapid development of microsensors-on-chip, human motion capture using wearable microsensors has become an active research topic. Because of the agility in movement, upper-limb motion estimation has been regarded as the most difficult problem in human motion capture. In this paper, we take the upper limb as our research subject and propose a novel ubiquitous upper-limb motion estimation algorithm, which concentrates on modeling the relationship between upper-arm movement and forearm movement. A link structure with 5 degrees of freedom (DOF) is proposed to model the human upper-limb skeleton structure. Parameters are defined according to Denavit-Hartenberg convention, forward kinematics equations are derived, and an unscented Kalman filter is deployed to estimate the defined parameters. The experimental results have shown that the proposed upper-limb motion capture and analysis algorithm outperforms other fusion methods and provides accurate results in comparison to the BTS optical motion tracker.

  12. 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.

  13. Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis

    Science.gov (United States)

    Gao, Wei; Emaminejad, Sam; Nyein, Hnin Yin Yin; Challa, Samyuktha; Chen, Kevin; Peck, Austin; Fahad, Hossain M.; Ota, Hiroki; Shiraki, Hiroshi; Kiriya, Daisuke; Lien, Der-Hsien; Brooks, George A.; Davis, Ronald W.; Javey, Ali

    2016-01-01

    Wearable sensor technologies are essential to the realization of personalized medicine through continuously monitoring an individual’s state of health. Sampling human sweat, which is rich in physiological information, could enable non-invasive monitoring. Previously reported sweat-based and other non-invasive biosensors either can only monitor a single analyte at a time or lack on-site signal processing circuitry and sensor calibration mechanisms for accurate analysis of the physiological state. Given the complexity of sweat secretion, simultaneous and multiplexed screening of target biomarkers is critical and requires full system integration to ensure the accuracy of measurements. Here we present a mechanically flexible and fully integrated (that is, no external analysis is needed) sensor array for multiplexed in situ perspiration analysis, which simultaneously and selectively measures sweat metabolites (such as glucose and lactate) and electrolytes (such as sodium and potassium ions), as well as the skin temperature (to calibrate the response of the sensors). Our work bridges the technological gap between signal transduction, conditioning (amplification and filtering), processing and wireless transmission in wearable biosensors by merging plastic-based sensors that interface with the skin with silicon integrated circuits consolidated on a flexible circuit board for complex signal processing. This application could not have been realized using either of these technologies alone owing to their respective inherent limitations. The wearable system is used to measure the detailed sweat profile of human subjects engaged in prolonged indoor and outdoor physical activities, and to make a real-time assessment of the physiological state of the subjects. This platform enables a wide range of personalized diagnostic and physiological monitoring applications.

  14. Measurement Using Conductive Polymeric Fibers in a Wearable Sensor Platform

    Directory of Open Access Journals (Sweden)

    Ram Manoj Sarda

    2010-12-01

    Full Text Available The purpose of this study is to design polymer fiber sensors which can be embedded into a t-shirt for electrocardiography. Recent inventors have envisioned this concept, but the feasibility of this idea needs to be investigated. The sensors used in this study are designed in way to make good contact with the skin without using gel or any other adhesives which can cause pain when removed and can annoy the user. The method of using polymer fiber sensors in a fabric for the purpose of electrocardiogram (ECG signal detection was determined to be more feasible than the conventional silver/silver-chloride adhesive electrodes. This method of using polymer fibers as sensors for Electrocardiography is a novel method for this application.

  15. Wireless Power Transfer for Autonomous Wearable Neurotransmitter Sensors

    National Research Council Canada - National Science Library

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

    2015-01-01

    ...). 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...

  16. Wearable wireless multi-parameter sensor module for physiological monitoring.

    Science.gov (United States)

    Liverud, Anders E; Vedum, Jon; Fleurey, Franck; Seeberg, Trine M

    2012-01-01

    Advances in low power technology have given new possibilities for continuous physiological monitoring in several domains such as health care with disease prevention and quality of care services and workers in harsh environment. A miniaturized, multifunctional sensor module that transmits sensor data wirelessly using Bluetooth Smart technology has been developed. The wireless communication link is influenced by factors like antenna orientation, reflections, interference and noise. Test results for signal strength measurements for the wireless transmission in various setups are given and discussed.

  17. Soft wearable contact lens sensor for continuous intraocular pressure monitoring.

    Science.gov (United States)

    Chen, Guo-Zhen; Chan, Ion-Seng; Leung, Leo K K; Lam, David C C

    2014-09-01

    Intraocular pressure (IOP) is a primary indicator of glaucoma, but measurements from a single visit to the clinic miss the peak IOP that may occur at night during sleep. A soft chipless contact lens sensor that allows the IOP to be monitored throughout the day and at night is developed in this study. A resonance circuit composed of a thin film capacitor coupled with a sensing coil that can sense corneal curvature deformation is designed, fabricated and embedded into a soft contact lens. The resonance frequency of the sensor is designed to vary with the lens curvature as it changes with the IOP. The frequency responses and the ability of the sensor to track IOP cycles were tested using a silicone rubber model eye. The results showed that the sensor has excellent linearity with a frequency response of ∼8 kHz/mmHg, and the sensor can accurately track fluctuating IOP. These results showed that the chipless contact lens sensor can potentially be used to monitor IOP to improve diagnosis accuracy and treatment of glaucoma.

  18. 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.

  19. Wearable sensors for skin heating and electric field strength in harsh environments

    Science.gov (United States)

    Lewis, Jay; Klem, Ethan; Cunningham, Garry; Dummer, Andrew

    2010-04-01

    Two novel sensor technologies have been developed for the measurement of skin surface temperature and RF field strength in an RF environment. Such a sensor system would be particularly useful in the test and evaluation of directed energy systems. The sensors operate without being affected by the presence of RF fields and with minimal perturbation of the fields, therefore having a minimal effect on a test. The sensors are designed to be wearable and interface with a portable, battery powered electronics pack by optical fibers. The temperature sensor is based on the variation in fluorescence intensity of a sensor layer with temperature. The RF field sensors operate using a passive circuit that converts the RF field into an optical signal that is measured remotely. Both sensors have been demonstrated in high power microwave lab tests. RF sensor operability has been demonstrated for fields in the range of 0.4 - 8.9 W/cm2, while the temperature sensor has been demonstrated over the 30 - 60°C temperature range.

  20. Fabrication of a wearable fabric tactile sensor produced by artificial hollow fiber

    Science.gov (United States)

    Hasegawa, Yoshihiro; Shikida, Mitsuhiro; Ogura, Daisuke; Suzuki, Yoshitaka; Sato, Kazuo

    2008-08-01

    An artificial-hollow-fiber structure as a new material for MEMS was developed and applied to a novel type of fabric tactile sensor. The artificial hollow fiber was fabricated by uniformly deposited metal and insulation layers on the surface of an elastic tube. A special rotating mechanism for uniformly depositing a metal layer on the tube surface during sputtering was developed. A rectangular-shaped fabric tactile sensor was produced by combining artificial hollow fibers and typical cotton yarns, like a cloth. The sensor can detect a contact force by measuring changes in capacitance at all intersection points of the artificial hollow fibers. Two different types of wearable-tactile-sensor glove, a patched type and a direct knit type, were also fabricated, and it was confirmed that both types can detect a normal load by measuring the capacitance change.

  1. 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.

  2. Performance Evaluation of Wearable Sensor Systems: A Case Study in Moderate-Scale Deployment in Hospital Environment.

    Science.gov (United States)

    Sun, Wen; Ge, Yu; Zhang, Zhiqiang; Wong, Wai-Choong

    2015-09-25

    A wearable sensor system enables continuous and remote health monitoring and is widely considered as the next generation of healthcare technology. The performance, the packet error rate (PER) in particular, of a wearable sensor system may deteriorate due to a number of factors, particularly the interference from the other wearable sensor systems in the vicinity. We systematically evaluate the performance of the wearable sensor system in terms of PER in the presence of such interference in this paper. The factors that affect the performance of the wearable sensor system, such as density, traffic load, and transmission power in a realistic moderate-scale deployment case in hospital are all considered. Simulation results show that with 20% duty cycle, only 68.5% of data transmission can achieve the targeted reliability requirement (PER is less than 0.05) even in the off-peak period in hospital. We then suggest some interference mitigation schemes based on the performance evaluation results in the case study.

  3. Performance Evaluation of Wearable Sensor Systems: A Case Study in Moderate-Scale Deployment in Hospital Environment

    Directory of Open Access Journals (Sweden)

    Wen Sun

    2015-09-01

    Full Text Available A wearable sensor system enables continuous and remote health monitoring and is widely considered as the next generation of healthcare technology. The performance, the packet error rate (PER in particular, of a wearable sensor system may deteriorate due to a number of factors, particularly the interference from the other wearable sensor systems in the vicinity. We systematically evaluate the performance of the wearable sensor system in terms of PER in the presence of such interference in this paper. The factors that affect the performance of the wearable sensor system, such as density, traffic load, and transmission power in a realistic moderate-scale deployment case in hospital are all considered. Simulation results show that with 20% duty cycle, only 68.5% of data transmission can achieve the targeted reliability requirement (PER is less than 0.05 even in the off-peak period in hospital. We then suggest some interference mitigation schemes based on the performance evaluation results in the case study.

  4. 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.

  5. Wearable Wireless Telemetry System for Implantable BioMEMS Sensors

    Science.gov (United States)

    Simons, Rainee N.; Miranda, Felix A.; Wilson, Jeffrey D.; Simons, Renita E.

    2008-01-01

    Telemetry systems of a type that have been proposed for the monitoring of physiological functions in humans would include the following subsystems: Surgically implanted or ingested units that would comprise combinations of microelectromechanical systems (MEMS)- based sensors [bioMEMS sensors] and passive radio-frequency (RF) readout circuits that would include miniature loop antennas. Compact radio transceiver units integrated into external garments for wirelessly powering and interrogating the implanted or ingested units. The basic principles of operation of these systems are the same as those of the bioMEMS-sensor-unit/external-RFpowering- and-interrogating-unit systems described in "Printed Multi-Turn Loop Antennas for Biotelemetry" (LEW-17879-1) NASA Tech Briefs, Vol. 31, No. 6 (June 2007), page 48, and in the immediately preceding article, "Hand-Held Units for Short-Range Wireless Biotelemetry" (LEW-17483-1). The differences between what is reported here and what was reported in the cited prior articles lie in proposed design features and a proposed mode of operation. In a specific system of the type now proposed, the sensor unit would comprise mainly a capacitive MEMS pressure sensor located in the annular region of a loop antenna (more specifically, a square spiral inductor/ antenna), all fabricated as an integral unit on a high-resistivity silicon chip. The capacitor electrodes, the spiral inductor/antenna, and the conductor lines interconnecting them would all be made of gold. The dimensions of the sensor unit have been estimated to be about 110.4 mm. The external garment-mounted powering/ interrogating unit would include a multi-turn loop antenna and signal-processing circuits. During operation, this external unit would be positioned in proximity to the implanted or ingested unit to provide for near-field, inductive coupling between the loop antennas, which we have as the primary and secondary windings of an electrical transformer.

  6. Gait and balance analysis for patients with Alzheimer's disease using an inertial-sensor-based wearable instrument.

    Science.gov (United States)

    Hsu, Yu-Liang; Chung, Pau-Choo Julia; Wang, Wei-Hsin; Pai, Ming-Chyi; Wang, Chun-Yao; Lin, Chien-Wen; Wu, Hao-Li; Wang, Jeen-Shing

    2014-11-01

    Despite patients with Alzheimer's disease (AD) were reported of revealing gait disorders and balance problems, there is still lack of objective quantitative measurement of gait patterns and balance capability of AD patients. Based on an inertial-sensor-based wearable device, this paper develops gait and balance analyzing algorithms to obtain quantitative measurements and explores the essential indicators from the measurements for AD diagnosis. The gait analyzing algorithm is composed of stride detection followed by gait cycle decomposition so that gait parameters are developed from the decomposed gait details. On the other hand, the balance is measured by the sway speed in anterior-posterior (AP) and medial-lateral (ML) directions of the projection path of body's center of mass (COM). These devised gait and balance parameters were explored on twenty-one AD patients and fifty healthy controls (HCs). Special evaluation procedure including single-task and dual-task walking experiments for observing the cognitive function and attention is also devised for the comparison of AD and HC groups. Experimental results show that the wearable instrument with the designed gait and balance analyzing system is a promising tool for automatically analyzing gait information and balance ability, serving as assistant indicators for early diagnosis of AD.

  7. Predictive monitoring of mobile patients by combining clinical observations with data from wearable sensors.

    Science.gov (United States)

    Clifton, Lei; Clifton, David A; Pimentel, Marco A F; Watkinson, Peter J; Tarassenko, Lionel

    2014-05-01

    The majority of patients in the hospital are ambulatory and would benefit significantly from predictive and personalized monitoring systems. Such patients are well suited to having their physiological condition monitored using low-power, minimally intrusive wearable sensors. Despite data-collection systems now being manufactured commercially, allowing physiological data to be acquired from mobile patients, little work has been undertaken on the use of the resultant data in a principled manner for robust patient care, including predictive monitoring. Most current devices generate so many false-positive alerts that devices cannot be used for routine clinical practice. This paper explores principled machine learning approaches to interpreting large quantities of continuously acquired, multivariate physiological data, using wearable patient monitors, where the goal is to provide early warning of serious physiological determination, such that a degree of predictive care may be provided. We adopt a one-class support vector machine formulation, proposing a formulation for determining the free parameters of the model using partial area under the ROC curve, a method arising from the unique requirements of performing online analysis with data from patient-worn sensors. There are few clinical evaluations of machine learning techniques in the literature, so we present results from a study at the Oxford University Hospitals NHS Trust devised to investigate the large-scale clinical use of patient-worn sensors for predictive monitoring in a ward with a high incidence of patient mortality. We show that our system can combine routine manual observations made by clinical staff with the continuous data acquired from wearable sensors. Practical considerations and recommendations based on our experiences of this clinical study are discussed, in the context of a framework for personalized monitoring.

  8. Elbow spasticity during passive stretch-reflex: clinical evaluation using a wearable sensor system

    Science.gov (United States)

    2013-01-01

    Background Spasticity is a prevalent chronic condition among persons with upper motor neuron syndrome that significantly impacts function and can be costly to treat. Clinical assessment is most often performed with passive stretch-reflex tests and graded on a scale, such as the Modified Ashworth Scale (MAS). However, these scales are limited in sensitivity and are highly subjective. This paper shows that a simple wearable sensor system (angle sensor and 2-channel EMG) worn during a stretch-reflex assessment can be used to more objectively quantify spasticity in a clinical setting. Methods A wearable sensor system consisting of a fibre-optic goniometer and 2-channel electromyography (EMG) was used to capture data during administration of the passive stretch-reflex test for elbow flexor and extensor spasticity. A kinematic model of unrestricted passive joint motion was used to extract metrics from the kinematic and EMG data to represent the intensity of the involuntary reflex. Relationships between the biometric results and clinical measures (MAS, isometric muscle strength and passive range of motion) were explored. Results Preliminary results based on nine patients with varying degrees of flexor and extensor spasticity showed that kinematic and EMG derived metrics were strongly correlated with one another, were correlated positively (and significantly) with clinical MAS, and negatively correlated (though mostly non-significant) with isometric muscle strength. Conclusions We conclude that a wearable sensor system used in conjunction with a simple kinematic model can capture clinically relevant features of elbow spasticity during stretch-reflex testing in a clinical environment. PMID:23782931

  9. Measuring Changes in Gait and Vehicle Transfer Ability During Inpatient Rehabilitation with Wearable Inertial Sensors

    Science.gov (United States)

    Borisov, Vladimir; Sprint, Gina; Cook, Diane J.; Weeks, Douglas L.

    2016-01-01

    Restoration of functional independence in gait and vehicle transfer ability is a common goal of inpatient rehabilitation. Currently, ambulation changes tend to be subjectively assessed. To investigate more precise objective assessment of progress in inpatient rehabilitation, we quantitatively assessed gait and transfer performances over the course of rehabilitation with wearable inertial sensors for 20 patients receiving inpatient rehabilitation services. Secondarily, we asked physical therapists to provide feedback about the clinical utility of metrics derived from the sensors. Participant performance was recorded on a sequence of ambulatory tasks that closely resemble everyday activities. We developed a custom software system to process sensor signals and compute metrics that characterize ambulation performance. We quantify changes in gait and transfer ability by performing a repeated measures comparison of the metrics one week apart. Metrics showing the greatest improvement are walking speed, stride regularity, acceleration root mean square, walking smoothness, shank peak angular velocity, and shank range of motion. Furthermore, feedback from physical therapists suggests that wearable sensor-derived metrics can potentially provide rehabilitation therapists with additional valuable information to aid in treatment decisions. PMID:28691124

  10. 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.

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

    Science.gov (United States)

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

    2015-09-21

    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).

  12. A web-based system for home monitoring of patients with Parkinson's disease using wearable sensors.

    Science.gov (United States)

    Chen, Bor-Rong; Patel, Shyamal; Buckley, Thomas; Rednic, Ramona; McClure, Douglas J; Shih, Ludy; Tarsy, Daniel; Welsh, Matt; Bonato, Paolo

    2011-03-01

    This letter introduces MercuryLive, a platform to enable home monitoring of patients with Parkinson's disease (PD) using wearable sensors. MercuryLive contains three tiers: a resource-aware data collection engine that relies upon wearable sensors, web services for live streaming and storage of sensor data, and a web-based graphical user interface client with video conferencing capability. Besides, the platform has the capability of analyzing sensor (i.e., accelerometer) data to reliably estimate clinical scores capturing the severity of tremor, bradykinesia, and dyskinesia. Testing results showed an average data latency of less than 400 ms and video latency of about 200 ms with video frame rate of about 13 frames/s when 800 kb/s of bandwidth were available and we used a 40% video compression, and data feature upload requiring 1 min of extra time following a 10 min interactive session. These results indicate that the proposed platform is suitable to monitor patients with PD to facilitate the titration of medications in the late stages of the disease.

  13. A remote quantitative Fugl-Meyer assessment framework for stroke patients based on wearable sensor networks.

    Science.gov (United States)

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

    2016-05-01

    To extend the use of wearable sensor networks for stroke patients training and assessment in non-clinical settings, this paper proposes a novel remote quantitative Fugl-Meyer assessment (FMA) framework, in which two accelerometer and seven flex sensors were used to monitoring the movement function of upper limb, wrist and fingers. The extreme learning machine based ensemble regression model was established to map the sensor data to clinical FMA scores while the RRelief algorithm was applied to find the optimal features subset. Considering the FMA scale is time-consuming and complicated, seven training exercises were designed to replace the upper limb related 33 items in FMA scale. 24 stroke inpatients participated in the experiments in clinical settings and 5 of them were involved in the experiments in home settings after they left the hospital. Both the experimental results in clinical and home settings showed that the proposed quantitative FMA model can precisely predict the FMA scores based on wearable sensor data, the coefficient of determination can reach as high as 0.917. It also indicated that the proposed framework can provide a potential approach to the remote quantitative rehabilitation training and evaluation.

  14. Sensor evaluation for wearable strain gauges in neurological rehabilitation.

    Science.gov (United States)

    Giorgino, Toni; Tormene, Paolo; Lorussi, Federico; De Rossi, Danilo; Quaglini, Silvana

    2009-08-01

    Conductive elastomers are a novel strain sensing technology which can be unobtrusively embedded into a garment's fabric, allowing a new type of sensorized cloths for motion analysis. A possible application for this technology is remote monitoring and control of motor rehabilitation exercises. The present work describes a sensorized shirt for upper limb posture recognition. Supervised learning techniques have been employed to compare classification models for the analysis of strains, simultaneously measured at multiple points of the shirt. The instantaneous position of the limb was classified into a finite set of predefined postures, and the movement was decomposed in an ordered sequence of discrete states. The amount of information given by the observation of each sensor during the execution of a specific exercise was quantitatively estimated by computing the information gain for each sensor, which in turn allows the data-driven optimization of the garment. Real-time feedback on exercise progress can also be provided by reconstructing the sequence of consecutive positions assumed by the limb.

  15. Wearable Flow: investigation of materiality in the production of contemporary body/artist

    Directory of Open Access Journals (Sweden)

    Carolina de Paula Diniz

    2017-05-01

    Full Text Available This study deals with the body in relation to the costume within the context of the contemporary performing arts. We aim to analyze how the body relates directly to the elements that are part of it. We examine the specificity of the body in the creative process, which components are organized in a procedural and concomitant way. We problematize the costume in its action as coauthor in the process. The practice called Wearable Flow is presented, focused on the exploration of the materiality based on the relationship between body, movement, and what is worn in the performing production and development of the artist.

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

    Science.gov (United States)

    2016-01-01

    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. PMID:27054878

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

    Science.gov (United States)

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

    2016-01-01

    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.

  18. Smart Body Sensor Object Networking

    Institute of Scientific and Technical Information of China (English)

    Bhumip Khasnabish

    2014-01-01

    This paper discusses smart body sensor objects (BSOs), including their networking and internetworking. Smartness can be incorpo-rated into BSOs by embedding virtualization, predictive analytics, and proactive computing and communications capabilities. A few use cases including the relevant privacy and protocol requirements are also presented. General usage and deployment eti-quette along with the relevant regulatory implications are then discussed.

  19. Detection-gap-independent optical sensor design using divergence-beam-controlled slit lasers for wearable devices

    Science.gov (United States)

    Yoon, Young Zoon; Kim, Hyochul; Park, Yeonsang; Kim, Jineun; Lee, Min Kyung; Kim, Un Jeong; Roh, Young-Geun; Hwang, Sung Woo

    2016-09-01

    Wearable devices often employ optical sensors, such as photoplethysmography sensors, for detecting heart rates or other biochemical factors. Pulse waveforms, rather than simply detecting heartbeats, can clarify arterial conditions. However, most optical sensor designs require close skin contact to reduce power consumption while obtaining good quality signals without distortion. We have designed a detection-gap-independent optical sensor array using divergence-beam-controlled slit lasers and distributed photodiodes in a pulse-detection device wearable over the wrist's radial artery. It achieves high biosignal quality and low power consumption. The top surface of a vertical-cavity surface-emitting laser of 850 nm wavelength was covered by Au film with an open slit of width between 500 nm and 1500 nm, which generated laser emissions across a large divergence angle along an axis orthogonal to the slit direction. The sensing coverage of the slit laser diode (LD) marks a 50% improvement over nonslit LD sensor coverage. The slit LD sensor consumes 100% more input power than the nonslit LD sensor to obtain similar optical output power. The slit laser sensor showed intermediate performance between LD and light-emitting diode sensors. Thus, designing sensors with multiple-slit LD arrays can provide useful and convenient ways for incorporating optical sensors in wrist-wearable devices.

  20. Wearable Wireless Sensor for Multi-Scale Physiological Monitoring

    Science.gov (United States)

    2015-10-01

    variability across sleep stages Proc. of the Ann. Int. Conf. IEEE Engineering Medical Biology Society pp 6153–6 De Meersman  R  E, Zion  A  S, Teitelbaum  S...TERMS Motion and noise artifact detection and reconstruction; multi-channel pulse oximeter sensor 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF...B.A., N. Reljin, Y. Kong, Y. Nam and K.H. Chon, Towards the development of a mobile phonopneumogram: automatic breath-phase classification using

  1. Wearable motion capturing with the flexing and turning based on a hetero-core fiber optic stretching sensor

    Science.gov (United States)

    Koyama, Y.; Nishiyama, M.; Watanabe, K.

    2011-05-01

    In recent years, motion capturing technologies have been applied to the service of the rehabilitation for the physically challenged people and practicing sports in human daily life. In these application fields, it is important that a measurement system does not prevent human from doing natural activity for unrestricted motion capture in daily-life. The hetero-core optic fiber sensor that we developed is suited for the unconstrained motion capturing because of optical intensity-based measurement with excellent stability and repeatability using single-mode transmission fibers and needless of any compensation. In this paper, we propose the development of wearable sensor enables unconstrained motion capture systems using the hetero-core fiber optic stretching sensor in real time, which satisfy user's requirements of comfort and ubiquitous. The experiments of motion capturing were demonstrated by setting the hetero-core fiber optic stretching sensor on the elbow, the back of the body and the waist. As a result, the hetero-core fiber optic stretching sensor was able to detect the displacement of expansion and contraction in the optical loss by flexion motion of the arm and the trunk motion. The optical loss performance of the hetero-core fiber optic stretching sensor reveals monotonic characteristics with the displacement. The optical loss changes at the full scale of motion were 1.45dB for the motion of anteflexion and 1.99 dB for the motion of turn. The real-time motion capturing was demonstrated by means of the proposed hetero-core fiber optic stretching sensor without restricting natural human behavior.

  2. Monitoring gait in multiple sclerosis with novel wearable motion sensors

    Science.gov (United States)

    McGinnis, Ryan S.; Seagers, Kirsten; Motl, Robert W.; Sheth, Nirav; Wright, John A.; Ghaffari, Roozbeh; Sosnoff, Jacob J.

    2017-01-01

    Background Mobility impairment is common in people with multiple sclerosis (PwMS) and there is a need to assess mobility in remote settings. Here, we apply a novel wireless, skin-mounted, and conformal inertial sensor (BioStampRC, MC10 Inc.) to examine gait characteristics of PwMS under controlled conditions. We determine the accuracy and precision of BioStampRC in measuring gait kinematics by comparing to contemporary research-grade measurement devices. Methods A total of 45 PwMS, who presented with diverse walking impairment (Mild MS = 15, Moderate MS = 15, Severe MS = 15), and 15 healthy control subjects participated in the study. Participants completed a series of clinical walking tests. During the tests participants were instrumented with BioStampRC and MTx (Xsens, Inc.) sensors on their shanks, as well as an activity monitor GT3X (Actigraph, Inc.) on their non-dominant hip. Shank angular velocity was simultaneously measured with the inertial sensors. Step number and temporal gait parameters were calculated from the data recorded by each sensor. Visual inspection and the MTx served as the reference standards for computing the step number and temporal parameters, respectively. Accuracy (error) and precision (variance of error) was assessed based on absolute and relative metrics. Temporal parameters were compared across groups using ANOVA. Results Mean accuracy±precision for the BioStampRC was 2±2 steps error for step number, 6±9ms error for stride time and 6±7ms error for step time (0.6–2.6% relative error). Swing time had the least accuracy±precision (25±19ms error, 5±4% relative error) among the parameters. GT3X had the least accuracy±precision (8±14% relative error) in step number estimate among the devices. Both MTx and BioStampRC detected significantly distinct gait characteristics between PwMS with different disability levels (p<0.01). Conclusion BioStampRC sensors accurately and precisely measure gait parameters in PwMS across diverse walking

  3. 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.

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

    Directory of Open Access Journals (Sweden)

    Liangtian Wan

    2016-03-01

    Full Text Available 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.

  5. 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.

  6. Development of a respiratory inductive plethysmography module supporting multiple sensors for wearable systems.

    Science.gov (United States)

    Zhang, Zhengbo; Zheng, Jiewen; Wu, Hao; Wang, Weidong; Wang, Buqing; Liu, Hongyun

    2012-09-27

    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.

  7. Ultrasensitive and highly selective graphene-based single yarn for use in wearable gas sensor.

    Science.gov (United States)

    Yun, Yong Ju; Hong, Won G; Choi, Nak-Jin; Kim, Byung Hoon; Jun, Yongseok; Lee, Hyung-Kun

    2015-06-04

    Electric components based on fibers or textiles have been investigated owing to their potential applications in wearable devices. High performance on response to gas, drape-ability and washing durability are of important for gas sensors based on fiber substrates. In this report, we demonstrate the bendable and washable electronic textile (e-textile) gas sensors composed of reduced graphene oxides (RGOs) using commercially available yarn and molecular glue through an electrostatic self-assembly. The e-textile gas sensor possesses chemical durability to several detergent washing treatments and mechanical stability under 1,000 bending tests at an extreme bending radius of 1 mm as well as a high response to NO2 gas at room temperature with selectivity to other gases such as acetone, ethanol, ethylene, and CO2.

  8. A wearable, highly stable, strain and bending sensor based on high aspect ratio graphite nanobelts

    Science.gov (United States)

    Alaferdov, A. V.; Savu, R.; Rackauskas, T. A.; Rackauskas, S.; Canesqui, M. A.; de Lara, D. S.; Setti, G. O.; Joanni, E.; de Trindade, G. M.; Lima, U. B.; de Souza, A. S.; Moshkalev, S. A.

    2016-09-01

    A simple and scalable method was developed for the fabrication of wearable strain and bending sensors, based on high aspect ratio (length/thickness ˜103) graphite nanobelt thin films deposited by a modified Langmuir-Blodgett technique onto flexible polymer substrates. The sensing mechanism is based on the changes in contact resistance between individual nanobelts upon substrate deformation. Very high sensor response stability for more than 5000 strain-release cycles and a device power consumption as low as 1 nW were achieved. The device maximum stretchability is limited by the metal electrodes and the polymer substrate; the maximum strain that could be applied to the polymer used in this work was 40%. Bending tests carried out for various radii of curvature demonstrated distinct sensor responses for positive and negative curvatures. The graphite nanobelt thin flexible films were successfully tested for acoustic vibration and heartbeat sensing.

  9. Position Estimation by Wearable Walking Navigation System for Visually Impaired with Sensor Fusion

    Science.gov (United States)

    Watanabe, Hiromi; Yamamoto, Yoshihiko; Tanzawa, Tsutomu; Kotani, Shinji

    A wearable walking navigation system without any special infrastructures has been developed to guide visually impaired. It is important to estimate a position correctly so that safe navigation can be realized. In our system, different sensor data are fused to estimate a pedestrian's position. An image processing system and a laser range finder were used to estimate the positions indoors. In this paper, we introduce the concept of “similarity” between map information and sensor data. This similarity is used to estimate the positions. Experimental results show that highly accurate position estimation can be achieved by sensor fusion. The positions in a linear passage were estimated using image processing data, and when the passage turns, the positions were estimated using LRF data.

  10. 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.

  11. Ultrasensitive and Highly Selective Graphene-Based Single Yarn for Use in Wearable Gas Sensor

    Science.gov (United States)

    Ju Yun, Yong; Hong, Won G.; Choi, Nak-Jin; Hoon Kim, Byung; Jun, Yongseok; Lee, Hyung-Kun

    2015-06-01

    Electric components based on fibers or textiles have been investigated owing to their potential applications in wearable devices. High performance on response to gas, drape-ability and washing durability are of important for gas sensors based on fiber substrates. In this report, we demonstrate the bendable and washable electronic textile (e-textile) gas sensors composed of reduced graphene oxides (RGOs) using commercially available yarn and molecular glue through an electrostatic self-assembly. The e-textile gas sensor possesses chemical durability to several detergent washing treatments and mechanical stability under 1,000 bending tests at an extreme bending radius of 1 mm as well as a high response to NO2 gas at room temperature with selectivity to other gases such as acetone, ethanol, ethylene, and CO2.

  12. 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.

  13. 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.

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

    Science.gov (United States)

    Standoli, Carlo Emilio; Guarneri, Maria Renata; Perego, Paolo; Mazzola, Marco; Mazzola, Alessandra; Andreoni, Giuseppe

    2016-08-10

    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.

  15. Smartwatch-based driver alertness monitoring with wearable motion and physiological sensor.

    Science.gov (United States)

    Lee, Boon-Giin; Lee, Boon-Leng; Chung, Wan-Young

    2015-01-01

    Studies have shown that a high precision driver alertness monitoring system is an essential and a monetary countermeasure to reduce the road accidents. This paper presents a novel approach to measure the driver alertness, evaluated by a smartwatch device based on fusion of direct and indirect method. The driver chronic physiological state is monitor by adopting a photoplethysmography sensor on the driver finger that is connected to a wrist-type wearable device. A Bluetooth Low Energy module connected to the wearable device transmits the PPG data to the smartwatch in real-time. Meanwhile, the indirect method, driver steering wheel movement can be derived by utilizing the motion sensors integrated in the smartwatch which include a tri-axis accelerometer and a gyroscope sensors. The respiration signals can be derived from the PPG time- and frequency-domains attributes. The data obtained from both methods aforementioned are subsequently decomposed into relevant features in time, spectral context and phase space domain, and thus computes the alertness index. Here, the correlations between the extracted features and the subjective Koralinska Sleepiness Scale are studied as well along with the recorded experimental videos. This study reveals that the alertness index prediction accuracy can be reached up to 96.3% based on the descriptive extracted features.

  16. Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges.

    Science.gov (United States)

    Banaee, Hadi; Ahmed, Mobyen Uddin; Loutfi, Amy

    2013-12-17

    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.

  17. 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.

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

    Science.gov (United States)

    Lee, Si-Huei; Yeh, Shih-Ching; Chan, Rai-Chi; Chen, Shuya; Yang, Geng; Zheng, Li-Rong

    2016-01-01

    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.

  19. Wearable sensor-based objective assessment of motor symptoms in Parkinson's disease.

    Science.gov (United States)

    Ossig, Christiana; Antonini, Angelo; Buhmann, Carsten; Classen, Joseph; Csoti, Ilona; Falkenburger, Björn; Schwarz, Michael; Winkler, Jürgen; Storch, Alexander

    2016-01-01

    Effective management and development of new treatment strategies of motor symptoms in Parkinson's disease (PD) largely depend on clinical rating instruments like the Unified PD rating scale (UPDRS) and the modified abnormal involuntary movement scale (mAIMS). Regarding inter-rater variability and continuous monitoring, clinical rating scales have various limitations. Patient-administered questionnaires such as the PD home diary to assess motor stages and fluctuations in late-stage PD are frequently used in clinical routine and as clinical trial endpoints, but diary/questionnaire are tiring, and recall bias impacts on data quality, particularly in patients with cognitive dysfunction or depression. Consequently, there is a strong need for continuous and objective monitoring of motor symptoms in PD for improving therapeutic regimen and for usage in clinical trials. Recent advances in battery technology, movement sensors such as gyroscopes, accelerometers and information technology boosted the field of objective measurement of movement in everyday life and medicine using wearable sensors allowing continuous (long-term) monitoring. This systematic review summarizes the current wearable sensor-based devices to objectively assess the various motor symptoms of PD.

  20. 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.

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

    Science.gov (United States)

    Banaee, Hadi; Ahmed, Mobyen Uddin; Loutfi, Amy

    2013-01-01

    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. PMID:24351646

  2. Fabric-Based Wearable Dry Electrodes for Body Surface Biopotential Recording.

    Science.gov (United States)

    Yokus, Murat A; Jur, Jesse S

    2016-02-01

    A flexible and conformable dry electrode design on nonwoven fabrics is examined as a sensing platform for biopotential measurements. Due to limitations of commercial wet electrodes (e.g., shelf life, skin irritation), dry electrodes are investigated as the potential candidates for long-term monitoring of ECG signals. Multilayered dry electrodes are fabricated by screen printing of Ag/AgCl conductive inks on flexible nonwoven fabrics. This study focuses on the investigation of skin-electrode interface, form factor design, electrode body placement of printed dry electrodes for a wearable sensing platform. ECG signals obtained with dry and wet electrodes are comparatively studied as a function of body posture and movement. Experimental results show that skin-electrode impedance is influenced by printed electrode area, skin-electrode interface material, and applied pressure. The printed electrode yields comparable ECG signals to wet electrodes, and the QRS peak amplitude of ECG signal is dependent on printed electrode area and electrode on body spacing. Overall, fabric-based printed dry electrodes present an inexpensive health monitoring platform solution for mobile wearable electronics applications by fulfilling user comfort and wearability.

  3. A Wearable Context-Aware ECG Monitoring System Integrated with Built-in Kinematic Sensors of the Smartphone

    Directory of Open Access Journals (Sweden)

    Fen Miao

    2015-05-01

    Full Text Available Continuously monitoring the ECG signals over hours combined with activity status is very important for preventing cardiovascular diseases. A traditional ECG holter is often inconvenient to carry because it has many electrodes attached to the chest and because it is heavy. This work proposes a wearable, low power context-aware ECG monitoring system integrated built-in kinetic sensors of the smartphone with a self-designed ECG sensor. The wearable ECG sensor is comprised of a fully integrated analog front-end (AFE, a commercial micro control unit (MCU, a secure digital (SD card, and a Bluetooth module. The whole sensor is very small with a size of only 58 × 50 × 10 mm for wearable monitoring application due to the AFE design, and the total power dissipation in a full round of ECG acquisition is only 12.5 mW. With the help of built-in kinetic sensors of the smartphone, the proposed system can compute and recognize user’s physical activity, and thus provide context-aware information for the continuous ECG monitoring. The experimental results demonstrated the performance of proposed system in improving diagnosis accuracy for arrhythmias and identifying the most common abnormal ECG patterns in different activities. In conclusion, we provide a wearable, accurate and energy-efficient system for long-term and context-aware ECG monitoring without any extra cost on kinetic sensor design but with the help of the widespread smartphone.

  4. Wearable ECG Based on Impulse-Radio-Type Human Body Communication.

    Science.gov (United States)

    Wang, Jianqing; Fujiwara, Takuya; Kato, Taku; Anzai, Daisuke

    2016-09-01

    Human body communication (HBC) provides a promising physical layer for wireless body area networks (BANs) in healthcare and medical applications, because of its low propagation loss and high security characteristics. In this study, we have developed a wearable electrocardiogram (ECG) which employs impulse radio (IR)-type HBC technology for transmitting vital signals on the human body in a wearable BAN scenario. The HBC-based wearable ECG has two excellent features. First, the wideband performance of the IR scheme contributed to very low radiation power so that the transceiver is easy to satisfy the extremely weak radio laws, which does not need a license. This feature can provide big convenience in the use and spread of the wearable ECG. Second, the realization of common use of sensing and transmitting electrodes based on time sharing and capacitive coupling largely simplified the HBC-based ECG structure and contributed to its miniaturization. To verify the validity of the HBC-based ECG, we evaluated its communication performance and ECG acquisition performance. The measured bit error rate, smaller than 10 (-3) at 1.25 Mb/s, showed a good physical layer communication performance, and the acquired ECG waveform and various heart-rate variability parameters in time and frequency domains exhibited good agreement with a commercially available radio-frequency ECG and a Holter ECG. These results sufficiently showed the validity and feasibility of the HBC-based ECG for healthcare applications. This should be the first time to have realized a real-time ECG transmission by using the HBC technology.

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

    Directory of Open Access Journals (Sweden)

    Milica Djurić-Jovičić

    2017-01-01

    Full Text Available 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.

  6. Mining networks of human contact with wearable sensors

    Science.gov (United States)

    Barrat, Alain

    2012-02-01

    Due to the development of sensors of various types and the use of digital media and computational devices, we increasingly leave digital traces of our daily activities. The scale at which such data can be gathered and analyzed makes possible a novel, data-driven approach to the investigation of various aspects of human behavior. In this talk, I will focus on the research done within the SocioPatterns project (www.sociopatterns.org), in which we have developed the SocioPatterns sensing platform to obtain longitudinal datasets on face-to-face contact events between individuals in a variety of contexts ranging from scientific conferences to museum, schools or hospitals. The gathered data sets consists in dynamic networks of human contacts, and their analysis reveal interesting similarities and differences of human interaction patterns across contexts. I will also consider the impact of the temporal resolution, which allows to take into account causality constraints, on dynamical processes occurring on networks, such as spreading processes.

  7. Highly stretchable strain sensor based on SWCNTs/CB synergistic conductive network for wearable human-activity monitoring and recognition

    Science.gov (United States)

    Guo, Xiaohui; Huang, Ying; Zhao, Yunong; Mao, Leidong; Gao, Le; Pan, Weidong; Zhang, Yugang; Liu, Ping

    2017-09-01

    Flexible, stretchable, and wearable strain sensors have attracted significant attention for their potential applications in human movement detection and recognition. Here, we report a highly stretchable and flexible strain sensor based on a single-walled carbon nanotube (SWCNTs)/carbon black (CB) synergistic conductive network. The fabrication, synergistic conductive mechanism, and characterization of the sandwich-structured strain sensor were investigated. The experimental results show that the device exhibits high stretchability (120%), excellent flexibility, fast response (∼60 ms), temperature independence, and superior stability and reproducibility during ∼1100 stretching/releasing cycles. Furthermore, human activities such as the bending of a finger or elbow and gestures were monitored and recognized based on the strain sensor, indicating that the stretchable strain sensor based on the SWCNTs/CB synergistic conductive network could have promising applications in flexible and wearable devices for human motion monitoring.

  8. Monitoring of cigarette smoking using wearable sensors and support vector machines.

    Science.gov (United States)

    Lopez-Meyer, Paulo; Tiffany, Stephen; Patil, Yogendra; Sazonov, Edward

    2013-07-01

    Cigarette smoking is a serious risk factor for cancer, cardiovascular, and pulmonary diseases. Current methods of monitoring of cigarette smoking habits rely on various forms of self-report that are prone to errors and under reporting. This paper presents a first step in the development of a methodology for accurate and objective assessment of smoking using noninvasive wearable sensors (Personal Automatic Cigarette Tracker-PACT) by demonstrating feasibility of automatic recognition of smoke inhalations from signals arising from continuous monitoring of breathing and hand-to-mouth gestures by support vector machine classifiers. The performance of subject-dependent (individually calibrated) models was compared to performance of subject-independent (group) classification models. The models were trained and validated on a dataset collected from 20 subjects performing 12 different activities representative of everyday living (total duration 19.5 h or 21,411 breath cycles). Precision and recall were used as the accuracy metrics. Group models obtained 87% and 80% of average precision and recall, respectively. Individual models resulted in 90% of average precision and recall, indicating a significant presence of individual traits in signal patterns. These results suggest the feasibility of monitoring cigarette smoking by means of a wearable and noninvasive sensor system in free living conditions.

  9. Two-Dimensional Atomic-Layered Alloy Junctions for High-Performance Wearable Chemical Sensor.

    Science.gov (United States)

    Cho, Byungjin; Kim, Ah Ra; Kim, Dong Jae; Chung, Hee-Suk; Choi, Sun Young; Kwon, Jung-Dae; Park, Sang Won; Kim, Yonghun; Lee, Byoung Hun; Lee, Kyu Hwan; Kim, Dong-Ho; Nam, Jaewook; Hahm, Myung Gwan

    2016-08-03

    We first report that two-dimensional (2D) metal (NbSe2)-semiconductor (WSe2)-based flexible, wearable, and launderable gas sensors can be prepared through simple one-step chemical vapor deposition of prepatterned WO3 and Nb2O5. Compared to a control device with a Au/WSe2 junction, gas-sensing performance of the 2D NbSe2/WSe2 device was significantly enhanced, which might have resulted from the formation of a NbxW1-xSe2 transition alloy junction lowering the Schottky barrier height. This would make it easier to collect charges of channels induced by molecule adsorption, improving gas response characteristics toward chemical species including NO2 and NH3. 2D NbSe2/WSe2 devices on a flexible substrate provide gas-sensing properties with excellent durability under harsh bending. Furthermore, the device stitched on a T-shirt still performed well even after conventional cleaning with a laundry machine, enabling wearable and launderable chemical sensors. These results could pave a road toward futuristic gas-sensing platforms based on only 2D materials.

  10. Using wearable sensors for semiology-independent seizure detection - towards ambulatory monitoring of epilepsy.

    Science.gov (United States)

    Heldberg, Beeke E; Kautz, Thomas; Leutheuser, Heike; Hopfengartner, Rudiger; Kasper, Burkhard S; Eskofier, Bjoern M

    2015-08-01

    Epilepsy is a disease of the central nervous system. Nearly 70% of people with epilepsy respond to a proper treatment, but for a successful therapy of epilepsy, physicians need to know if and when seizures occur. The gold standard diagnosis tool video-electroencephalography (vEEG) requires patients to stay at hospital for several days. A wearable sensor system, e.g. a wristband, serving as diagnostic tool or event monitor, would allow unobtrusive ambulatory long-term monitoring while reducing costs. Previous studies showed that seizures with motor symptoms such as generalized tonic-clonic seizures can be detected by measuring the electrodermal activity (EDA) and motion measuring acceleration (ACC). In this study, EDA and ACC from 8 patients were analyzed. In extension to previous studies, different types of seizures, including seizures without motor activity, were taken into account. A hierarchical classification approach was implemented in order to detect different types of epileptic seizures using data from wearable sensors. Using a k-nearest neighbor (kNN) classifier an overall sensitivity of 89.1% and an overall specificity of 93.1% were achieved, for seizures without motor activity the sensitivity was 97.1% and the specificity was 92.9%. The presented method is a first step towards a reliable ambulatory monitoring system for epileptic seizures with and without motor activity.

  11. 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.

  12. A Secure Privacy-Preserving Data Aggregation Model in Wearable Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Changlun Zhang

    2015-01-01

    Full Text Available With the rapid development and widespread use of wearable wireless sensors, data aggregation technique becomes one of the most important research areas. However, the sensitive data collected by sensor nodes may be leaked at the intermediate aggregator nodes. So, privacy preservation is becoming an increasingly important issue in security data aggregation. In this paper, we propose a security privacy-preserving data aggregation model, which adopts a mixed data aggregation structure. Data integrity is verified both at cluster head and at base station. Some nodes adopt slicing technology to avoid the leak of data at the cluster head in inner-cluster. Furthermore, a mechanism is given to locate the compromised nodes. The analysis shows that the model is robust to many attacks and has a lower communication overhead.

  13. 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.

  14. [Usefulness for detection of inappropriate blood pressure variability using 'wearable blood pressure sensor'].

    Science.gov (United States)

    Iijima, Katsuya

    2015-11-01

    In the clinical settings, it has frequently seen that the elderly have rapid blood pressure (BP) elevation and decline, leading to such as orthostatic disorders and post-urination syncope. Excessive blood pressure variability (BPV) according to aging leads to aggravation of hypertensive target organ damage due to both disturbed baroreflex function and arterial stiffening. We developed continuous BP monitoring sensor using newly developing device 'wearable BP sensor', as our advantageous approach of without a cuff-stress. The new mobile device could reflect continuous beat-to-beat systolic BP, heart rate(HR), these very close changes and double product(sBPX HR) as a major indicator of cardiac lead, in consistent with cuff-based BP value. Our new challenge using this device might approach to the potential to achieve the quality-up of treatment strategy with consideration for very short-term BPV.

  15. 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...

  16. 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

    National Research Council Canada - National Science Library

    Villa, Federica; Magnani, Alessandro; Maggioni, Martina A; Stahn, Alexander; Rampichini, Susanna; Merati, Giampiero; Castiglioni, Paolo

    2016-01-01

    .... However, neither portable commercial instruments nor more advanced wearable prototypes simultaneously satisfy the demanding needs of unobtrusively tracking body fluid shifts in different segments...

  17. 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.

  18. A real-time maximum-likelihood heart-rate estimator for wearable textile sensors.

    Science.gov (United States)

    Cheng, Mu-Huo; Chen, Li-Chung; Hung, Ying-Che; Yang, Chang Ming

    2008-01-01

    This paper presents a real-time maximum-likelihood heart-rate estimator for ECG data measured via wearable textile sensors. The ECG signals measured from wearable dry electrodes are notorious for its susceptibility to interference from the respiration or the motion of wearing person such that the signal quality may degrade dramatically. To overcome these obstacles, in the proposed heart-rate estimator we first employ the subspace approach to remove the wandering baseline, then use a simple nonlinear absolute operation to reduce the high-frequency noise contamination, and finally apply the maximum likelihood estimation technique for estimating the interval of R-R peaks. A parameter derived from the byproduct of maximum likelihood estimation is also proposed as an indicator for signal quality. To achieve the goal of real-time, we develop a simple adaptive algorithm from the numerical power method to realize the subspace filter and apply the fast-Fourier transform (FFT) technique for realization of the correlation technique such that the whole estimator can be implemented in an FPGA system. Experiments are performed to demonstrate the viability of the proposed system.

  19. A Real-Time Cardiac Arrhythmia Classification System with Wearable Sensor Networks

    Science.gov (United States)

    Hu, Sheng; Wei, Hongxing; Chen, Youdong; Tan, Jindong

    2012-01-01

    Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for prevention on the heart attack and other high risk diseases. This paper presents the design of a real-time wearable ECG monitoring system with associated cardiac arrhythmia classification algorithms. One of the striking advantages is that ECG analog front-end and on-node digital processing are designed to remove most of the noise and bias. In addition, the wearable sensor node is able to monitor the patient's ECG and motion signal in an unobstructive way. To realize the real-time medical analysis, the ECG is digitalized and transmitted to a smart phone via Bluetooth. On the smart phone, the ECG waveform is visualized and a novel layered hidden Markov model is seamlessly integrated to classify multiple cardiac arrhythmias in real time. Experimental results demonstrate that the clean and reliable ECG waveform can be captured in multiple stressed conditions and the real-time classification on cardiac arrhythmia is competent to other workbenches. PMID:23112746

  20. A Real-Time Cardiac Arrhythmia Classification System with Wearable Sensor Networks

    Directory of Open Access Journals (Sweden)

    Jindong Tan

    2012-09-01

    Full Text Available Long term continuous monitoring of electrocardiogram (ECG in a free living environment provides valuable information for prevention on the heart attack and other high risk diseases. This paper presents the design of a real-time wearable ECG monitoring system with associated cardiac arrhythmia classification algorithms. One of the striking advantages is that ECG analog front-end and on-node digital processing are designed to remove most of the noise and bias. In addition, the wearable sensor node is able to monitor the patient’s ECG and motion signal in an unobstructive way. To realize the real-time medical analysis, the ECG is digitalized and transmitted to a smart phone via Bluetooth. On the smart phone, the ECG waveform is visualized and a novel layered hidden Markov model is seamlessly integrated to classify multiple cardiac arrhythmias in real time. Experimental results demonstrate that the clean and reliable ECG waveform can be captured in multiple stressed conditions and the real-time classification on cardiac arrhythmia is competent to other workbenches.

  1. 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.

  2. 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.

  3. OLAM: A wearable, non-contact sensor for continuous heart-rate and activity monitoring.

    Science.gov (United States)

    Albright, Ryan K; Goska, Benjamin J; Hagen, Tory M; Chi, Mike Y; Cauwenberghs, G; Chiang, Patrick Y

    2011-01-01

    A wearable, multi-modal sensor is presented that can non-invasively monitor a patient's activity level and heart function concurrently for more than a week. The 4 in(2) sensor incorporates both a non-contact heartrate sensor and a 5-axis inertial measurement unit (IMU), allowing simultaneous heart, respiration, and movement monitoring without requiring physical contact with the skin [1]. Hence, this Oregon State University Life and Activity Monitor (OLAM) provides the unique opportunity to combine motion data with heart-rate information, enabling assessment of actual physical activity beyond conventional movement sensors. OLAM also provides a unique platform for non-contact sensing, enabling the filtering of movement artifacts generated by the non-contact capacitive interface, using the IMU data as a movement noise channel. Intended to be used in clinical trials for weeks at a time with no physician intervention, the OLAM allows continuous non-invasive monitoring of patients, providing the opportunity for long-term observation into a patient's physical activity and subtle longitudinal changes.

  4. Algorithm for heart rate extraction in a novel wearable acoustic sensor.

    Science.gov (United States)

    Chen, Guangwei; Imtiaz, Syed Anas; Aguilar-Pelaez, Eduardo; Rodriguez-Villegas, Esther

    2015-02-01

    Phonocardiography is a widely used method of listening to the heart sounds and indicating the presence of cardiac abnormalities. Each heart cycle consists of two major sounds - S1 and S2 - that can be used to determine the heart rate. The conventional method of acoustic signal acquisition involves placing the sound sensor at the chest where this sound is most audible. Presented is a novel algorithm for the detection of S1 and S2 heart sounds and the use of them to extract the heart rate from signals acquired by a small sensor placed at the neck. This algorithm achieves an accuracy of 90.73 and 90.69%, with respect to heart rate value provided by two commercial devices, evaluated on more than 38 h of data acquired from ten different subjects during sleep in a pilot clinical study. This is the largest dataset for acoustic heart sound classification and heart rate extraction in the literature to date. The algorithm in this study used signals from a sensor designed to monitor breathing. This shows that the same sensor and signal can be used to monitor both breathing and heart rate, making it highly useful for long-term wearable vital signs monitoring.

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

    DEFF Research Database (Denmark)

    Jalaliniya, Shahram

    2017-01-01

    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...... with the WPA, and the need for more touch-less input modalities. My further exploration on touch-less input modalities such as body gestures and gaze, showed the great potential of using eye movements as an implicit input to WPAs. Since the involuntary eye movements (e.g. optokinetic nystagmus) are unconscious...

  6. 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.

  7. 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.

  8. 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.

  9. Automatic diet monitoring: a review of computer vision and wearable sensor-based methods.

    Science.gov (United States)

    Hassannejad, Hamid; Matrella, Guido; Ciampolini, Paolo; De Munari, Ilaria; Mordonini, Monica; Cagnoni, Stefano

    2017-01-31

    Food intake and eating habits have a significant impact on people's health. Widespread diseases, such as diabetes and obesity, are directly related to eating habits. Therefore, monitoring diet can be a substantial base for developing methods and services to promote healthy lifestyle and improve personal and national health economy. Studies have demonstrated that manual reporting of food intake is inaccurate and often impractical. Thus, several methods have been proposed to automate the process. This article reviews the most relevant and recent researches on automatic diet monitoring, discussing their strengths and weaknesses. In particular, the article reviews two approaches to this problem, accounting for most of the work in the area. The first approach is based on image analysis and aims at extracting information about food content automatically from food images. The second one relies on wearable sensors and has the detection of eating behaviours as its main goal.

  10. Development of Sensor and Control Systems of a Wearable Robot to Walk on a Step

    Science.gov (United States)

    Oda, Yuki; Kagawa, Takahiro; Uno, Yoji

    The goal of our study is to develop sensing and control systems for walking on a step using a wearable robot. Our system consists of (1) sensing of a bump from a movement of a walker, (2) detecting a foot placement state related to the bump and (3) generating gait patterns of stepping up and down for the bump. In the generation of gait patterns for the bump, toe trajectories are generated according to the height of the bump to avoid the collision of the swing leg and the bump. A hip trajectory is generated by the optimization technique to minimize the sum total of joint angular jerk of the robot subject to the constrained condition of the hip position and velocity at toe-off. Each joint angle trajectory is calculated from the generated trajectories using inverse kinematics equations. We examined the feasibility of the proposed sensor and control systems for two kinds of bumps with different height.

  11. Fabrication of Wearable Sensors for Human Health Monitoring through Magnetically Directed Assembly Techniques

    Science.gov (United States)

    Alizadeh, Azar; Ashe, Jeffrey; Misner, Matthew; Yang, Yanzhe; Zhong, Sheng; Yin, Ming; Brewer, Joleyn; Karp, Jason

    2013-03-01

    Many previous efforts to modify patient monitors for remote or wearable use have suffered from high cost, poor performance, and low medical acceptance. A new technology approach is needed to enable these clinical benefits and to satisfy challenging economic, clinical, and user-acceptance criteria. Here, we present results on our initial efforts aimed at designing and building a prototype multi-wavelength arrayed photoplethysmograph (PPG) by using magnetically directed self-assembly (MDSA). We will discuss novel approaches in magnetic nanomaterial design, synthesis and deposition to enable MDSA based manufacturing. We will also demonstrate that multiple devices can be deposited through heterogeneous MDSA. The novel MDSA technology could make such PPG sensors a reality. This effort is sponsored by the Department of the Army under award W81XWH1110833

  12. 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

  13. Daily Quantity of Infant Leg Movement: Wearable Sensor Algorithm and Relationship to Walking Onset

    Directory of Open Access Journals (Sweden)

    Beth A. Smith

    2015-08-01

    Full Text Available Background: Normative values are lacking for daily quantity of infant leg movements. This is critical for understanding the relationship between the quantity of leg movements and onset of independent walking, and will begin to inform early therapy intervention for infants at risk for developmental delay. Methods: We used wearable inertial movement sensors to record full-day leg movement activity from 12 infants with typical development, ages 1–12 months. Each infant was tested three times across 5 months, and followed until the onset of independent walking. We developed and validated an algorithm to identify infant-produced leg movements. Results: Infants moved their legs tens of thousands of times per day. There was a significant effect of leg movement quantity on walking onset. Infants who moved their legs more walked later than infants who moved their legs less, even when adjusting for age, developmental level or percentile length. We will need a much larger sample to adequately capture and describe the effect of movement experience on developmental rate. Our algorithm defines a leg movement in a specific way (each pause or change in direction is counted as a new movement, and further assessment of movement characteristics are necessary before we can fully understand and interpret our finding that infants who moved their legs more walked later than infants who moved their legs less. Conclusions: We have shown that typically-developing infants produce thousands of leg movements in a typical day, and that this can be accurately captured in the home environment using wearable sensors. In our small sample we can identify there is an effect of leg movement quantity on walking onset, however we cannot fully explain it.

  14. 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

    2016-01-01

    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. PMID:27399696

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

    Directory of Open Access Journals (Sweden)

    Hiram Ponce

    2016-07-01

    Full Text Available 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.

  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. 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)

  18. Data Collection and Analysis Using Wearable Sensors for Monitoring Knee Range of Motion after Total Knee Arthroplasty

    Science.gov (United States)

    Chiang, Chih-Yen; Chen, Kun-Hui; Liu, Kai-Chun; Hsu, Steen Jun-Ping; Chan, Chia-Tai

    2017-01-01

    Total knee arthroplasty (TKA) is the most common treatment for degenerative osteoarthritis of that articulation. However, either in rehabilitation clinics or in hospital wards, the knee range of motion (ROM) can currently only be assessed using a goniometer. In order to provide continuous and objective measurements of knee ROM, we propose the use of wearable inertial sensors to record the knee ROM during the recovery progress. Digitalized and objective data can assist the surgeons to control the recovery status and flexibly adjust rehabilitation programs during the early acute inpatient stage. The more knee flexion ROM regained during the early inpatient period, the better the long-term knee recovery will be and the sooner early discharge can be achieved. The results of this work show that the proposed wearable sensor approach can provide an alternative for continuous monitoring and objective assessment of knee ROM recovery progress for TKA patients compared to the traditional goniometer measurements. PMID:28241434

  19. Developing a wireless implantable body sensor network in MICS band.

    Science.gov (United States)

    Fang, Qiang; Lee, Shuenn-Yuh; Permana, Hans; Ghorbani, Kamran; Cosic, Irena

    2011-07-01

    Through an integration of wireless communication and sensing technologies, the concept of a body sensor network (BSN) was initially proposed in the early decade with the aim to provide an essential technology for wearable, ambulatory, and pervasive health monitoring for elderly people and chronic patients. It has become a hot research area due to big opportunities as well as great challenges it presents. Though the idea of an implantable BSN was proposed in parallel with the on-body sensor network, the development in this area is relatively slow due to the complexity of human body, safety concerns, and some technological bottlenecks such as the design of ultralow-power implantable RF transceiver. This paper describes a new wireless implantable BSN that operates in medical implant communication service (MICS) frequency band. This system innovatively incorporates both sensing and actuation nodes to form a closed-control loop for physiological monitoring and drug delivery for critically ill patients. The sensing node, which is designed using system-on-chip technologies, takes advantage of the newly available ultralow-power Zarlink MICS transceiver for wireless data transmission. Finally, the specific absorption rate distribution of the proposed system was simulated to determine the in vivo electromagnetic field absorption and the power safety limits.

  20. Wearable system for acquisition, processing and storage of the signal from amperometric glucose sensors.

    Science.gov (United States)

    Fabietti, P G; Massi Benedetti, M; Bronzo, F; Reboldi, G P; Sarti, E; Brunetti, P

    1991-03-01

    A wearable device for the acquisition, processing and storage of the signal from needle-type glucose sensors has been designed and developed as part of a project aimed at developing a portable artificial pancreas. The device is essential to assess the operational characteristics of miniaturized sensors in vivo. It can be connected to sensors operating at a constant potential of 0.65 Volts, and generating currents in the order of 10(-9) Amp. It is screened and equipped with filters that permit data recording and processing even in the presence of electrical noise. It can operate with sensors with different characteristics (1-200 nA full scale). The device has been designed to be worn by patients, so its weight and size have been kept to a minimum (250 g; 8.5 x 14.5 x 3.5 cm). It is powered by rechargeable Ni/Cd batteries allowing continuous operation for 72 h. The electronics consists of an analog card with operational amplifiers, and a digital one with a microprocessor (Intel 80C196, MCS-96 class, with internal 16-bit CPU supporting programs written in either C or Assembler language), a 32 Kb EPROM, and an 8 Kb RAM where the data are stored. The microprocessor can run either at 5 or 10 Mhz and features on-chip peripherals: an analog/digital (A/D) converter, a serial port (used to transfer data to a Personal Computer at the end of the 72 h), input-output (I/O) units at high-speed, and two timers. The device is programmed and prepared to operate by means of a second hand-held unit equipped with an LCD display and a 16-key numeric pad.(ABSTRACT TRUNCATED AT 250 WORDS)

  1. 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.

  2. 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.

  3. Fire fighters and rescuers monitoring through wearable sensors: The ProeTEX project.

    Science.gov (United States)

    Magenes, Giovanni; Curone, Davide; Caldani, Laura; Secco, Emanuele Lindo

    2010-01-01

    The final generation of ProeTEX prototypes has been delivered in April 2010: it is based on two sets of sensorized garments devoted to monitor the health status of emergency operators working in harsh environments. This new release of garments shows several improvements with respect to the previous ones, and it is characterized by a major specialization to the requirements imposed by the different categories of end-users (Fire-Fighters, Civil Protection rescuers) addressed by the project. Each ProeTEX prototype is provided with a communication infrastructure allowing the real-time remote transmission of data recorded by the wearable sensors, and the presentation of such data to possible managers supervising the activities of the first line responders. After the delivery of the prototypes, an intense validation of the garments is being carried out both in laboratories, specialized in physiological measures, and in simulated fire-fighting scenarios. In such a context, this paper presents the main features characterizing the final ProeTEX prototypes and preliminary results of their laboratory assessment.

  4. 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.

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

    Directory of Open Access Journals (Sweden)

    Ekaterina Kutafina

    2016-08-01

    Full Text Available 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.

  6. Wearable wireless sensor platform for studying autonomic activity and social behavior in non-human primates.

    Science.gov (United States)

    Fletcher, Richard Ribón; Amemori, Ken-ichi; Goodwin, Matthew; Graybiel, Ann M

    2012-01-01

    A portable system has been designed to enable remote monitoring of autonomic nervous system output in non-human primates for the purpose of studying neural function related to social behavior over extended periods of time in an ambulatory setting. In contrast to prior systems which only measure heart activity, are restricted to a constrained laboratory setting, or require surgical attachment, our system is comprised of a multi-sensor self-contained wearable vest that can easily be transferred from one subject to another. The vest contains a small detachable low-power electronic sensor module for measuring electrodermal activity (EDA), electrocardiography (ECG), 3-axis acceleration, and temperature. The wireless transmission is implemented using a standard Bluetooth protocol and a mobile phone, which enables freedom of movement for the researcher as well as for the test subject. A custom Android software application was created on the mobile phone for viewing and recording live data as well as creating annotations. Data from up to seven monkeys can be recorded simultaneously using the mobile phone, with the option of real-time upload to a remote web server. Sample data are presented from two rhesus macaque monkeys showing stimulus-induced response in the laboratory as well as long-term ambulatory data collected in a large monkey cage. This system enables new possibilities for studying underlying mechanisms between autonomic brain function and social behavior with connection to human research in areas such as autism, substance abuse, and mood disorders.

  7. 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.

    2016-01-01

    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. PMID:27527167

  8. Exploring relationship between face-to-face interaction and team performance using wearable sensor badges.

    Science.gov (United States)

    Watanabe, Jun-ichiro; Ishibashi, Nozomu; Yano, Kazuo

    2014-01-01

    Quantitative analyses of human-generated data collected in various fields have uncovered many patterns of complex human behaviors. However, thus far the quantitative evaluation of the relationship between the physical behaviors of employees and their performance has been inadequate. Here, we present findings demonstrating the significant relationship between the physical behaviors of employees and their performance via experiments we conducted in inbound call centers while the employees wore sensor badges. There were two main findings. First, we found that face-to-face interaction among telecommunicators and the frequency of their bodily movements caused by the face-to-face interaction had a significant correlation with the entire call center performance, which we measured as "Calls per Hour." Second, our trial to activate face-to-face interaction on the basis of data collected by the wearable sensor badges the employees wore significantly increased their performance. These results demonstrate quantitatively that human-human interaction in the physical world plays an important role in team performance.

  9. Wearable Sensing of In-Ear Pressure for Heart Rate Monitoring with a Piezoelectric Sensor

    Directory of Open Access Journals (Sweden)

    Jang-Ho Park

    2015-09-01

    Full Text Available In this study, we developed a novel heart rate (HR monitoring approach in which we measure the pressure variance of the surface of the ear canal. A scissor-shaped apparatus equipped with a piezoelectric film sensor and a hardware circuit module was designed for high wearability and to obtain stable measurement. In the proposed device, the film sensor converts in-ear pulse waves (EPW into electrical current, and the circuit module enhances the EPW and suppresses noise. A real-time algorithm embedded in the circuit module performs morphological conversions to make the EPW more distinct and knowledge-based rules are used to detect EPW peaks. In a clinical experiment conducted using a reference electrocardiogram (ECG device, EPW and ECG were concurrently recorded from 58 healthy subjects. The EPW intervals between successive peaks and their corresponding ECG intervals were then compared to each other. Promising results were obtained from the samples, specifically, a sensitivity of 97.25%, positive predictive value of 97.17%, and mean absolute difference of 0.62. Thus, highly accurate HR was obtained from in-ear pressure variance. Consequently, we believe that our proposed approach could be used to monitor vital signs and also utilized in diverse applications in the near future.

  10. Exploring Relationship between Face-to-Face Interaction and Team Performance Using Wearable Sensor Badges

    Science.gov (United States)

    Watanabe, Jun-ichiro; Ishibashi, Nozomu; Yano, Kazuo

    2014-01-01

    Quantitative analyses of human-generated data collected in various fields have uncovered many patterns of complex human behaviors. However, thus far the quantitative evaluation of the relationship between the physical behaviors of employees and their performance has been inadequate. Here, we present findings demonstrating the significant relationship between the physical behaviors of employees and their performance via experiments we conducted in inbound call centers while the employees wore sensor badges. There were two main findings. First, we found that face-to-face interaction among telecommunicators and the frequency of their bodily movements caused by the face-to-face interaction had a significant correlation with the entire call center performance, which we measured as “Calls per Hour.” Second, our trial to activate face-to-face interaction on the basis of data collected by the wearable sensor badges the employees wore significantly increased their performance. These results demonstrate quantitatively that human-human interaction in the physical world plays an important role in team performance. PMID:25501748

  11. Quaternion-based pseudo kalman filter for wearable inertial/magnetic sensor applications

    Directory of Open Access Journals (Sweden)

    Lee Jung Keun

    2016-01-01

    Full Text Available This paper deals with orientation estimation using miniature inertial/magnetic sensor comprised of a tri-axial rate gyro, a tri-axial accelerometer, and a tri-axial magnetometer. Particularly, a novel quaternion-based pseudo Kalman filter (KF is proposed by modifying an indirect KF, in order to maximize the computational efficiency and implementation simplicity. In the proposed pseudo KF, time-update process for prediction is based on the quaternion itself, while measurement-update process for correction is performed through the quaternion error. Experimental tests were conducted to verify performance of the proposed algorithm in various dynamic conditions. By designing the pseudo KF structure, matrix operations required in a typical KF are simplified. For instance, the proposed KF does not require the evaluation of the a priori and a posteriori error covariance matrices. Thus, the proposed algorithm achieves higher computational efficiency even than a typical indirect KF, without sacrificing estimation accuracy. Due to its high efficiency, the proposed algorithm can be suitable for battery-powered and low cost processor-based wearable inertial/magnetic sensor applications.

  12. COMPOSE: Using temporal patterns for interpreting wearable sensor data with computer interpretable guidelines.

    Science.gov (United States)

    Urovi, V; Jimenez-Del-Toro, O; Dubosson, F; Ruiz Torres, A; Schumacher, M I

    2017-02-01

    This paper describes a novel temporal logic-based framework for reasoning with continuous data collected from wearable sensors. The work is motivated by the Metabolic Syndrome, a cluster of conditions which are linked to obesity and unhealthy lifestyle. We assume that, by interpreting the physiological parameters of continuous monitoring, we can identify which patients have a higher risk of Metabolic Syndrome. We define temporal patterns for reasoning with continuous data and specify the coordination mechanisms for combining different sets of clinical guidelines that relate to this condition. The proposed solution is tested with data provided by twenty subjects, which used sensors for four days of continuous monitoring. The results are compared to the gold standard. The novelty of the framework stands in extending a temporal logic formalism, namely the Event Calculus, with temporal patterns. These patterns are helpful to specify the rules for reasoning with continuous data and in combining new knowledge into one consistent outcome that is tailored to the patient's profile. The overall approach opens new possibilities for delivering patient-tailored interventions and educational material before the patients present the symptoms of the disease.

  13. Wearable Sensing of In-Ear Pressure for Heart Rate Monitoring with a Piezoelectric Sensor.

    Science.gov (United States)

    Park, Jang-Ho; Jang, Dae-Geun; Park, Jung Wook; Youm, Se-Kyoung

    2015-09-16

    In this study, we developed a novel heart rate (HR) monitoring approach in which we measure the pressure variance of the surface of the ear canal. A scissor-shaped apparatus equipped with a piezoelectric film sensor and a hardware circuit module was designed for high wearability and to obtain stable measurement. In the proposed device, the film sensor converts in-ear pulse waves (EPW) into electrical current, and the circuit module enhances the EPW and suppresses noise. A real-time algorithm embedded in the circuit module performs morphological conversions to make the EPW more distinct and knowledge-based rules are used to detect EPW peaks. In a clinical experiment conducted using a reference electrocardiogram (ECG) device, EPW and ECG were concurrently recorded from 58 healthy subjects. The EPW intervals between successive peaks and their corresponding ECG intervals were then compared to each other. Promising results were obtained from the samples, specifically, a sensitivity of 97.25%, positive predictive value of 97.17%, and mean absolute difference of 0.62. Thus, highly accurate HR was obtained from in-ear pressure variance. Consequently, we believe that our proposed approach could be used to monitor vital signs and also utilized in diverse applications in the near future.

  14. Exploring relationship between face-to-face interaction and team performance using wearable sensor badges.

    Directory of Open Access Journals (Sweden)

    Jun-ichiro Watanabe

    Full Text Available Quantitative analyses of human-generated data collected in various fields have uncovered many patterns of complex human behaviors. However, thus far the quantitative evaluation of the relationship between the physical behaviors of employees and their performance has been inadequate. Here, we present findings demonstrating the significant relationship between the physical behaviors of employees and their performance via experiments we conducted in inbound call centers while the employees wore sensor badges. There were two main findings. First, we found that face-to-face interaction among telecommunicators and the frequency of their bodily movements caused by the face-to-face interaction had a significant correlation with the entire call center performance, which we measured as "Calls per Hour." Second, our trial to activate face-to-face interaction on the basis of data collected by the wearable sensor badges the employees wore significantly increased their performance. These results demonstrate quantitatively that human-human interaction in the physical world plays an important role in team performance.

  15. An RF-based wearable sensor system for indoor tracking to facilitate efficient healthcare management.

    Science.gov (United States)

    Yuzhe Ouyang; Shan, Kai; Bui, Francis Minhthang

    2016-08-01

    To understand the utilization of clinical resources and improve the efficiency of healthcare, it is often necessary to accurately locate patients and doctors in a healthcare facility. However, existing tracking methods, such as GPS, Wi-Fi and RFID, have technological drawbacks or impose significant costs, thus limiting their applications in many clinical environments, especially those with indoor enclosures. This paper proposes a low-cost and flexible tracking system that is well suited for operating in an indoor environment. Based on readily available RF transceivers and microcontrollers, our wearable sensor system can facilitate locating users (e.g., patients or doctors) or objects (e.g., medical devices) in a building. The strategic construction of the sensor system, along with a suitably designed tracking algorithm, together provide for reliability and dispatch in localization performance. For demonstration purposes, several simplified experiments, with different configurations of the system, are implemented in two testing rooms to assess the baseline performance. From the obtained results, our system exhibits immense promise in acquiring a user location and corresponding time-stamp, with high accuracy and rapid response. This capability is conducive to both short- and long-term data analytics, which are crucial for improving healthcare management.

  16. 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.

  17. Gum Sensor: A Stretchable, Wearable, and Foldable Sensor Based on Carbon Nanotube/Chewing Gum Membrane.

    Science.gov (United States)

    Darabi, Mohammad Ali; Khosrozadeh, Ali; Wang, Quan; Xing, Malcolm

    2015-12-02

    Presented in this work is a novel and facile approach to fabricate an elastic, attachable, and cost-efficient carbon nanotube (CNT)-based strain gauge which can be efficiently used as bodily motion sensors. An innovative and unique method is introduced to align CNTs without external excitations or any complicated procedure. In this design, CNTs are aligned and distributed uniformly on the entire chewing gum by multiple stretching and folding technique. The current sensor is demonstrated to be a linear strain sensor for at least strains up to 200% and can detect strains as high as 530% with a high sensitivity ranging from 12 to 25 and high durability. The gum sensor has been used as bodily motion sensors, and outstanding results are achieved; the sensitivity is quite high, capable of tracing slow breathing. Since the gum sensor can be patterned into various forms, it has wide applications in miniaturized sensors and biochips. Interestingly, we revealed that our gum sensor has the ability to monitor humidity changes with high sensitivity and fast resistance response capable of monitoring human breathing.

  18. A novel system identification technique for improved wearable hemodynamics assessment.

    Science.gov (United States)

    Wiens, Andrew D; Inan, Omer T

    2015-05-01

    Recent advances have led to renewed interest in ballistocardiography (BCG), a noninvasive measure of the small movements of the body due to cardiovascular events. A broad range of platforms have been developed and verified for BCG measurement including beds, chairs, and weighing scales: while the body is coupled to such a platform, the cardiogenic movements are measured. Wearable BCG, measured with an accelerometer affixed to the body, may enable continuous, or more regular, monitoring during the day; however, the signals from such wearable BCGs represent local or distal accelerations of skin and tissue rather than the whole body. In this paper, we propose a novel method to reconstruct the BCG measured with a weighing scale (WS BCG) from a wearable sensor via a training step to remove these local effects. Preliminary validation of this method was performed with 15 subjects: the wearable sensor was placed at three locations on the surface of the body while WS BCG measurements were recorded simultaneously. A regularized system identification approach was used to reconstruct the WS BCG from the wearable BCG. Preliminary results suggest that the relationship between local and central disturbances is highly dependent on both the individual and the location where the accelerometer is placed on the body and that these differences can be resolved via calibration to accurately measure changes in cardiac output and contractility from a wearable sensor. Such measurements could be highly effective, for example, for improved monitoring of heart failure patients at home.

  19. Highly Stretchable, Ultrasensitive, and Wearable Strain Sensors Based on Facilely Prepared Reduced Graphene Oxide Woven Fabrics in an Ethanol Flame.

    Science.gov (United States)

    Yin, Biao; Wen, Yanwei; Hong, Tao; Xie, Zhongshuai; Yuan, Guoliang; Ji, Qingmin; Jia, Hongbing

    2017-09-11

    The recent booming development of wearable electronics urgently calls for high-performance flexible strain sensors. To date, it is still a challenge to manufacture flexible strain sensors with superb sensitivity and a large workable strain range simultaneously. Herein, a facile, quick, cost-effective, and scalable strategy is adopted to fabricate novel strain sensors based on reduced graphene oxide woven fabrics (GWF). By pyrolyzing commercial cotton bandages coated with graphene oxide (GO) sheets in an ethanol flame, the reduction of GO and the pyrolysis of the cotton bandage template can be synchronously completed in tens of seconds. Due to the unique hierarchical structure of the GWF, the strain sensor based on GWF exhibits large stretchability (57% strain) with high sensitivity, inconspicuous drift, and durability. The GWF strain sensor is successfully used to monitor full-range (both subtle and vigorous) human activities or physical vibrational signals of the local environment. The present work offers an effective strategy to rapidly prepare low-cost flexible strain sensors with potential applications in the fields of wearable electronics, artificial intelligence devices, and so forth.

  20. 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

  1. 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

  2. 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.

  3. 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.

  4. A Wearable Ground Reaction Force Sensor System and Its Application to the Measurement of Extrinsic Gait Variability

    Science.gov (United States)

    Liu, Tao; Inoue, Yoshio; Shibata, Kyoko

    2010-01-01

    Wearable sensors for gait analysis are attracting wide interest. In this paper, a wearable ground reaction force (GRF) sensor system and its application to measure extrinsic gait variability are presented. To validate the GRF and centre of pressure (CoP) measurements of the sensor system and examine the effectiveness of the proposed method for gait analysis, we conducted an experimental study on seven volunteer subjects. Based on the assessment of the influence of the sensor system on natural gait, we found that no significant differences were found for almost all measured gait parameters (p-values < 0.05). As for measurement accuracy, the root mean square (RMS) differences for the two transverse components and the vertical component of the GRF were 7.2% ± 0.8% and 9.0% ± 1% of the maximum of each transverse component and 1.5% ± 0.9% of the maximum vertical component of GRF, respectively. The RMS distance between both CoP measurements was 1.4% ± 0.2% of the length of the shoe. The area of CoP distribution on the foot-plate and the average coefficient of variation of the triaxial GRF, are the introduced parameters for analysing extrinsic gait variability. Based on a statistical analysis of the results of the tests with subjects wearing the sensor system, we found that the proposed parameters changed according to walking speed and turning (p-values < 0.05). PMID:22163468

  5. 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.

  6. 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; 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

    2015-01-01

    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 < 20° and 10° for most movements in major joints of UE and LE, respectively. Among inpatients undergoing rehabilitation, ROM measurements using the new 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. PMID:26609398

  7. A Wearable Ground Reaction Force Sensor System and Its Application to the Measurement of Extrinsic Gait Variability

    Directory of Open Access Journals (Sweden)

    Kyoko Shibata

    2010-11-01

    Full Text Available Wearable sensors for gait analysis are attracting wide interest. In this paper, a wearable ground reaction force (GRF sensor system and its application to measure extrinsic gait variability are presented. To validate the GRF and centre of pressure (CoP measurements of the sensor system and examine the effectiveness of the proposed method for gait analysis, we conducted an experimental study on seven volunteer subjects. Based on the assessment of the influence of the sensor system on natural gait, we found that no significant differences were found for almost all measured gait parameters (p-values < 0.05. As for measurement accuracy, the root mean square (RMS differences for the two transverse components and the vertical component of the GRF were 7.2% ± 0.8% and 9.0% ± 1% of the maximum of each transverse component and 1.5% ± 0.9% of the maximum vertical component of GRF, respectively. The RMS distance between both CoP measurements was 1.4% ± 0.2% of the length of the shoe. The area of CoP distribution on the foot-plate and the average coefficient of variation of the triaxial GRF, are the introduced parameters for analysing extrinsic gait variability. Based on a statistical analysis of the results of the tests with subjects wearing the sensor system, we found that the proposed parameters changed according to walking speed and turning (p-values < 0.05.

  8. Pupil and Glint Detection Using Wearable Camera Sensor and Near-Infrared LED Array.

    Science.gov (United States)

    Wang, Jianzhong; Zhang, Guangyue; Shi, Jiadong

    2015-12-02

    This paper proposes a novel pupil and glint detection method for gaze tracking system using a wearable camera sensor and near-infrared LED array. A novel circular ring rays location (CRRL) method is proposed for pupil boundary points detection. Firstly, improved Otsu optimal threshold binarization, opening-and-closing operation and projection of 3D gray-level histogram are utilized to estimate rough pupil center and radius. Secondly, a circular ring area including pupil edge inside is determined according to rough pupil center and radius. Thirdly, a series of rays are shot from inner to outer ring to collect pupil boundary points. Interference points are eliminated by calculating gradient amplitude. At last, an improved total least squares is proposed to fit collected pupil boundary points. In addition, the improved total least squares developed is utilized for the solution of Gaussian function deformation to calculate glint center. The experimental results show that the proposed method is more robust and accurate than conventional detection methods. When interference factors such as glints and natural light reflection are located on pupil contour, pupil boundary points and center can be detected accurately. The proposed method contributes to enhance stability, accuracy and real-time quality of gaze tracking system.

  9. ThimbleSense: a fingertip-wearable tactile sensor for grasp analysis.

    Science.gov (United States)

    Battaglia, Edoardo; Bianchi, Matteo; Altobelli, Alessandro; Grioli, Giorgio; Catalano, Manuel; Serio, Alessandro; Santello, Marco; Bicchi, Antonio

    2015-10-08

    Accurate measurement of contact forces between hand and grasped objects is crucial to study sensorimotor control during grasp and manipulation. In this work we introduce ThimbleSense, a prototype of individual-digit wearable force/torque sensor based on the principle of intrinsic tactile sensing. By exploiting the integration of this approach with an active marker-based motion capture system, the proposed device simultaneously measures absolute position and orientation of the fingertip, which in turn yields measurements of contacts and force components expressed in a global reference frame. The main advantage of this approach with respect to more conventional solutions is its versatility. Specifically, ThimbleSense can be used to study grasping and manipulation of a wide variety of objects, while still retaining complete force/torque measurements. Nevertheless, validation of the proposed device is a necessary step before it can be used for experimental purposes. In this work we present the results of a series of experiments designed to validate the accuracy of ThimbleSense measurements and evaluate the effects of distortion of tactile afferent inputs caused by the device's rigid shells on grasp forces.

  10. Ensemble of One-Class Classifiers for Personal Risk Detection Based on Wearable Sensor Data.

    Science.gov (United States)

    Rodríguez, Jorge; Barrera-Animas, Ari Y; Trejo, Luis A; Medina-Pérez, Miguel Angel; Monroy, Raúl

    2016-09-29

    This study introduces the One-Class K-means with Randomly-projected features Algorithm (OCKRA). OCKRA is an ensemble of one-class classifiers built over multiple projections of a dataset according to random feature subsets. Algorithms found in the literature spread over a wide range of applications where ensembles of one-class classifiers have been satisfactorily applied; however, none is oriented to the area under our study: personal risk detection. OCKRA has been designed with the aim of improving the detection performance in the problem posed by the Personal RIsk DEtection(PRIDE) dataset. PRIDE was built based on 23 test subjects, where the data for each user were captured using a set of sensors embedded in a wearable band. The performance of OCKRA was compared against support vector machine and three versions of the Parzen window classifier. On average, experimental results show that OCKRA outperformed the other classifiers for at least 0.53% of the area under the curve (AUC). In addition, OCKRA achieved an AUC above 90% for more than 57% of the users.

  11. Ensemble of One-Class Classifiers for Personal Risk Detection Based on Wearable Sensor Data

    Directory of Open Access Journals (Sweden)

    Jorge Rodríguez

    2016-09-01

    Full Text Available This study introduces the One-Class K-means with Randomly-projected features Algorithm (OCKRA. OCKRA is an ensemble of one-class classifiers built over multiple projections of a dataset according to random feature subsets. Algorithms found in the literature spread over a wide range of applications where ensembles of one-class classifiers have been satisfactorily applied; however, none is oriented to the area under our study: personal risk detection. OCKRA has been designed with the aim of improving the detection performance in the problem posed by the Personal RIsk DEtection(PRIDE dataset. PRIDE was built based on 23 test subjects, where the data for each user were captured using a set of sensors embedded in a wearable band. The performance of OCKRA was compared against support vector machine and three versions of the Parzen window classifier. On average, experimental results show that OCKRA outperformed the other classifiers for at least 0.53% of the area under the curve (AUC. In addition, OCKRA achieved an AUC above 90% for more than 57% of the users.

  12. Pupil and Glint Detection Using Wearable Camera Sensor and Near-Infrared LED Array

    Directory of Open Access Journals (Sweden)

    Jianzhong Wang

    2015-12-01

    Full Text Available This paper proposes a novel pupil and glint detection method for gaze tracking system using a wearable camera sensor and near-infrared LED array. A novel circular ring rays location (CRRL method is proposed for pupil boundary points detection. Firstly, improved Otsu optimal threshold binarization, opening-and-closing operation and projection of 3D gray-level histogram are utilized to estimate rough pupil center and radius. Secondly, a circular ring area including pupil edge inside is determined according to rough pupil center and radius. Thirdly, a series of rays are shot from inner to outer ring to collect pupil boundary points. Interference points are eliminated by calculating gradient amplitude. At last, an improved total least squares is proposed to fit collected pupil boundary points. In addition, the improved total least squares developed is utilized for the solution of Gaussian function deformation to calculate glint center. The experimental results show that the proposed method is more robust and accurate than conventional detection methods. When interference factors such as glints and natural light reflection are located on pupil contour, pupil boundary points and center can be detected accurately. The proposed method contributes to enhance stability, accuracy and real-time quality of gaze tracking system.

  13. 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.

  14. Activity classification and dead reckoning for pedestrian navigation with wearable sensors

    Science.gov (United States)

    Sun, Zuolei; Mao, Xuchu; Tian, Weifeng; Zhang, Xiangfen

    2009-01-01

    This paper addresses an approach which integrates activity classification and dead reckoning techniques in step-based pedestrian navigation. In the proposed method, the pedestrian is equipped with a prototype wearable sensor module to record accelerations and determine the headings while walking. To improve the step detection accuracy, different types of activities are classified according to extracted features by means of a probabilistic neural network (PNN). The vertical acceleration data, which indicate the periodic vibration during gait cycle are filtered through a wavelet transform before being used to count the steps and assess the step length from which the distance traveled is estimated. By coupling the distance with the azimuth, navigation through pedestrian dead reckoning is implemented. This research provides a possible seamless pedestrian navigation solution which can be applied to a wide range of areas where the global navigation satellite system (GNSS) signal remains vulnerable. Results of two experiments in this paper reveal that the proposed approach is effective in reducing navigation errors and improving accuracy.

  15. 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

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

    Science.gov (United States)

    Sherrill, Delsey M; Moy, Marilyn L; Reilly, John J; Bonato, Paolo

    2005-01-01

    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 clustering methods are relevant

  17. 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.

  18. 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.

  19. 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

  20. Quaternion-Based Unscented Kalman Filter for Accurate Indoor Heading Estimation Using Wearable Multi-Sensor System

    Directory of Open Access Journals (Sweden)

    Xuebing Yuan

    2015-05-01

    Full Text Available Inertial navigation based on micro-electromechanical system (MEMS inertial measurement units (IMUs has attracted numerous researchers due to its high reliability and independence. The heading estimation, as one of the most important parts of inertial navigation, has been a research focus in this field. Heading estimation using magnetometers is perturbed by magnetic disturbances, such as indoor concrete structures and electronic equipment. The MEMS gyroscope is also used for heading estimation. However, the accuracy of gyroscope is unreliable with time. In this paper, a wearable multi-sensor system has been designed to obtain the high-accuracy indoor heading estimation, according to a quaternion-based unscented Kalman filter (UKF algorithm. The proposed multi-sensor system including one three-axis accelerometer, three single-axis gyroscopes, one three-axis magnetometer and one microprocessor minimizes the size and cost. The wearable multi-sensor system was fixed on waist of pedestrian and the quadrotor unmanned aerial vehicle (UAV for heading estimation experiments in our college building. The results show that the mean heading estimation errors are less 10° and 5° to multi-sensor system fixed on waist of pedestrian and the quadrotor UAV, respectively, compared to the reference path.

  1. A review of wearable technology in medicine.

    Science.gov (United States)

    Iqbal, Mohammed H; Aydin, Abdullatif; Brunckhorst, Oliver; Dasgupta, Prokar; Ahmed, Kamran

    2016-10-01

    With rapid advances in technology, wearable devices have evolved and been adopted for various uses, ranging from simple devices used in aiding fitness to more complex devices used in assisting surgery. Wearable technology is broadly divided into head-mounted displays and body sensors. A broad search of the current literature revealed a total of 13 different body sensors and 11 head-mounted display devices. The latter have been reported for use in surgery (n = 7), imaging (n = 3), simulation and education (n = 2) and as navigation tools (n = 1). Body sensors have been used as vital signs monitors (n = 9) and for posture-related devices for posture and fitness (n = 4). Body sensors were found to have excellent functionality in aiding patient posture and rehabilitation while head-mounted displays can provide information to surgeons to while maintaining sterility during operative procedures. There is a potential role for head-mounted wearable technology and body sensors in medicine and patient care. However, there is little scientific evidence available proving that the application of such technologies improves patient satisfaction or care. Further studies need to be conducted prior to a clear conclusion. © The Royal Society of Medicine.

  2. Performance evaluation of wearable wireless body area networks during walking motions in 444.5 MHz and 2450 MHz.

    Science.gov (United States)

    Takizawa, Kenichi; Watanabe, Katsuhiro; Kumazawa, Masaki; Hamada, Yusuke; Ikegami, Tetsushi; Hamaguchi, Kiyoshi

    2010-01-01

    This paper gives performance evaluation of wearable wireless body area networks (WBANs) during walking motion. In order to evaluate the performance, received signal strength (RSS), packet error rate (PER), and bit error rate (BER) are measured in an anechoic chamber and an office room. This measurement is conducted in the frequency band of 444.5 and 2450 MHz by using GFSK signal with symbol rate of 1 MHz. The results show that in the anechoic chamber the WBAN using the 444.5 MHz enables to provide error-free communication, on the other hand, the WBAN operated in the 2450 MHz faces packet errors. Measurement results in the office room give comparable performance between these frequencies. From these observations, the use of 2450 MHz for wearable WBANs needs reflection waves in order to compensate a shadowing effect caused by the human body using the WBAN.

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

    Science.gov (United States)

    Zhou, Qifan; Zhang, Hai; Lari, Zahra; Liu, Zhenbo; El-Sheimy, Naser

    2016-10-21

    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.

  4. 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.

  5. Using Hexoskin Wearable Technology to Obtain Body Metrics During Trail Hiking.

    Science.gov (United States)

    Montes, Jeff; Stone, Tori M; Manning, Jacob W; McCune, Damon; Tacad, Debra K; Young, John C; Debeliso, Mark; Navalta, James W

    Use of wearable technology to obtain various body metrics appears to be a trending phenomenon. However there is very little literature supporting the notion that these apparatuses can be used for research purposes in the field. The purpose of this study was to utilize Hexoskin wearable technology shirts (HxS) to obtain data in a pilot study using a trail hiking situation. Ten individuals (male, n = 4, female n = 6) volunteered to participate. On the first day, volunteers completed two approximately flat trail hikes at a self-preferred pace with a 15-minute rest between trials. On the second day, participants completed a strenuous uphill hike (17.6% grade) with a 15-minute rest at the summit and then completed the downhill portion. Body metrics provided by the HxS were average heart rate (HR), maximal HR (MHR), total energy expenditure (EE), average respiratory rate (RR), maximal respiratory rate (MRR), total steps (SC), and cadence (CA). Other measurements obtained were systolic and diastolic blood pressure (SBP, DBP), and ratings of perceived exertion (RPE). Data were analyzed using both one-way repeated measures analysis of variance (ANOVA) with significance accepted at p≤0.05 and intraclass correlation coefficients (ICC) for each variable. Both were determined using Statistical Package for the Social Sciences software (SPSS). No significant differences for trail type were noted for MHR (p=0.38), RR (p=0.45) or MRR (p=0.31). The uphill trail elicited significantly elevated HR (up=154±24 bpm, easy=118±11 bpm, down=129±19 bpm; p=0.04) and EE (up=251±78 kcal, easy=124±38 kcal, down=171±52 kcal; p=0.02). Significant ICC were observed for DBP (r = 0.80, p = 0.02), RR (r = 0.98, p = 0.01), SC (r = 0.97, p = 0.01) and RPE (r = 0.94, p = 0.01). Non-significant correlation were noted for uphill RR vs CA (r=0.51, p=0.16) or RPE vs SBP (r=0.03, p=0.94), HR (r=0.60, p=0.12), and MHR (r=0.70, p=0.051). We utilized HxS to provide physiological data in an applied

  6. Smart Woven Fabrics With Portable And Wearable Vibrating Electronics

    Directory of Open Access Journals (Sweden)

    Özdemir Hakan

    2015-06-01

    Full Text Available The portable and wearable instrumented fabrics capable of measuring biothermal variable is essential for drivers, especially long-distance drivers. Here we report on portable and wearable devices that are able to read the temperature of human body within the woven fabric. The sensory function of the fabric is achieved by temperature sensors, soldered on conductive threads coated with cotton. The presence of stainless steel wires gives these materials conductive properties, enabling the detection of human body temperature and transmitting the signal form sensors to the motors on the fabric. When body temperature decreases, hardware/software platforms send a signal to the vibration motors in order to stimulate the driver. The ‘smart woven fabric’-sensing architecture can be divided into two parts: a textile platform, where portable and wearable devices acquire thermal signals, and hardware/software platforms, to which a sensor sends the acquired data, which send the signals to the vibration motors.

  7. Use of the shape memory polymer polystyrene in the creation of thin film stretchable sensors for wearable applications

    Science.gov (United States)

    Van Volkinburg, Kyle R.; Nguyen, Thao; Pegan, Jonathan D.; Khine, Michelle; Washington, Gregory N.

    2016-04-01

    The shape memory polymer polystyrene (PS) has been used to create complex hierarchical wrinkling in the fabrication of stretchable thin film bimetallic sensors ideal for wearable based gesture monitoring applications. The film has been bonded to the elastomer polydimethylsiloxane (PDMS) and operates as a strain gauge under the general notion of geometric piezoresistivity. The film was subject to tensile, cyclic, and step loading conditions in order to characterize its dynamic behavior. To measure the joint angle of the metacarpophalangeal (MCP) joint on the right index finger, the sensor was adhered to a fitted golf glove above said joint and a motion study was conducted. At maximum joint angle the sensor experienced roughly 23.5% strain. From the study it was found that two simple curves, one while the finger was in flexion and the other while the finger was in extension, were able to predict the joint angle from measured voltage with an average error of 2.99 degrees.

  8. On the Design of a Wearable Multi-sensor System for Recognizing Motion Modes and Sit-to-stand Transition

    Directory of Open Access Journals (Sweden)

    Enhao Zheng

    2014-02-01

    Full Text Available Locomotion mode recognition is one of the key aspects of control of intelligent prostheses. This paper presents a wireless wearable multi-sensor system for locomotion mode recognition. The sensor suit of the system includes three inertial measurement units (IMUs and eight force sensors. The system was built to measure both kinematic (tilt angles and dynamic (ground contact forces signals of human gaits. To evaluate the recognition performance of the system, seven motion modes and sit-to-stand transition were monitored. With a linear discriminant analysis (LDA classifier, the proposed system can accurately classify the current states. The overall motion mode recognition accuracy was 99.9% during the stance phase and 98.5% during the swing phase. For sit-to-stand transition recognition, the average accuracy was 99.9%. These promising results show the potential of the designed system for the control of intelligent prostheses.

  9. Daily Life Event Segmentation for Lifestyle Evaluation Based on Multi-Sensor Data Recorded by a Wearable Device*

    Science.gov (United States)

    Li, Zhen; Wei, Zhiqiang; Jia, Wenyan; Sun, Mingui

    2013-01-01

    In order to evaluate people’s lifestyle for health maintenance, this paper presents a segmentation method based on multi-sensor data recorded by a wearable computer called eButton. This device is capable of recording more than ten hours of data continuously each day in multimedia forms. Automatic processing of the recorded data is a significant task. We have developed a two-step summarization method to segment large datasets automatically. At the first step, motion sensor signals are utilized to obtain candidate boundaries between different daily activities in the data. Then, visual features are extracted from images to determine final activity boundaries. It was found that some simple signal measures such as the combination of a standard deviation measure of the gyroscope sensor data at the first step and an image HSV histogram feature at the second step produces satisfactory results in automatic daily life event segmentation. This finding was verified by our experimental results. PMID:24110323

  10. On the Design of a Wearable Multi-sensor System for Recognizing Motion Modes and Sit-to-stand Transition

    Directory of Open Access Journals (Sweden)

    Enhao Zheng

    2014-02-01

    Full Text Available Locomotion mode recognition is one of the key aspects of control of intelligent prostheses. This paper presents a wireless wearable multi-sensor system for locomotion mode recognition. The sensor suit of the system includes three inertial measurement units (IMUs and eight force sensors. The system was built to measure both kinematic (tilt angles and dynamic (ground contact forces signals of human gaits. To evaluate the recognition performance of the system, seven motion modes and sit-to-stand transition were monitored. With a linear discriminant analysis (LDA classifier, the proposed system can accurately classify the current states. The overall motion mode recognition accuracy was 99.9% during the stance phase and 98.5% during the swing phase. For sit-to-stand transition recognition, the average accuracy was 99.9%. These promising results show the potential of the designed system for the control of intelligent prostheses.

  11. Collecting and distributing wearable sensor data: an embedded personal area network to local area network gateway server.

    Science.gov (United States)

    Neuhaeuser, Jakob; D'Angelo, Lorenzo T

    2013-01-01

    The goal of the concept and of the device presented in this contribution is to be able to collect sensor data from wearable sensors directly, automatically and wirelessly and to make them available over a wired local area network. Several concepts in e-health and telemedicine make use of portable and wearable sensors to collect movement or activity data. Usually these data are either collected via a wireless personal area network or using a connection to the user's smartphone. However, users might not carry smartphones on them while inside a residential building such as a nursing home or a hospital, but also within their home. Also, in such areas the use of other wireless communication technologies might be limited. The presented system is an embedded server which can be deployed in several rooms in order to ensure live data collection in bigger buildings. Also, the collection of data batches recorded out of range, as soon as a connection is established, is also possible. Both, the system concept and the realization are presented.

  12. 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.

    2015-01-01

    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 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 gmC 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. PMID:26694414

  13. 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.

  14. 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.

  15. Capacitive wearable tactile sensor based on smart textile substrate with carbon black /silicone rubber composite dielectric

    Science.gov (United States)

    Guo, Xiaohui; Huang, Ying; Cai, Xia; Liu, Caixia; Liu, Ping

    2016-04-01

    To achieve the wearable comfort of electronic skin (e-skin), a capacitive sensor printed on a flexible textile substrate with a carbon black (CB)/silicone rubber (SR) composite dielectric was demonstrated in this paper. Organo-silicone conductive silver adhesive serves as a flexible electrodes/shielding layer. The structure design, sensing mechanism and the influence of the conductive filler content and temperature variations on the sensor performance were investigated. The proposed device can effectively enhance the flexibility and comfort of wearing the device asthe sensing element has achieved a sensitivity of 0.02536%/KPa, a hysteresis error of 5.6%, and a dynamic response time of ~89 ms at the range of 0-700 KPa. The drift induced by temperature variations has been calibrated by presenting the temperature compensation model. The research on the time-space distribution of plantar pressure information and the experiment of the manipulator soft-grasping were implemented with the introduced device, and the experimental results indicate that the capacitive flexible textile tactile sensor has good stability and tactile perception capacity. This study provides a good candidate for wearable artificial skin.

  16. 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....

  17. Communications for Wearable Devices

    OpenAIRE

    Tabibu, Shivram

    2017-01-01

    Wearable devices are transforming computing and the human-computer interaction and they are a primary means for motion recognition of reflexive systems. We review basic wearable deployments and their open wireless communications. An algorithm that uses accelerometer data to provide a control and communication signal is described. Challenges in the further deployment of wearable device in the field of body area network and biometric verification are discussed.

  18. 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.

  19. Intrusion Detection and Prevention of Node Replication Attacks in Wireless Body Area Sensor Network

    Directory of Open Access Journals (Sweden)

    Anandkumar K.M

    2012-08-01

    Full Text Available Healthcare monitoring architecture coupled with wearable sensor systems for monitoring elderly or chronic patients in their residence has emerged as a promising technique. The wearable sensor system, built into a fabric belt, consists of various medical sensors that collect a timely set of physiological health indicators transmitted via low energy wireless communication (Zigbee to mobile computing devices. In this context, Security of the Wireless Body Area Sensor Network (WBASN in Ubiquitous healthcare applications is a crucial problem because sensitive and personal medical information must be protectedagainst flaws and misdeed and also in order to increase user’s acceptance to these new technologies. Moving towards this direction, we analyze the data access security due to replication attacks and the problems caused by it. We propose a secure multicast strategy that employs trust in order to evaluate the behavior of each node, so that only trustworthy nodes are allowed to participate in communications, while the replicated nodes are revocated from the network.

  20. 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.

  1. 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.

  2. 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

    2016-05-01

    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 cm2 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.

  3. Development of a body joint angle measurement system using IMU sensors.

    Science.gov (United States)

    Bakhshi, Saba; Mahoor, Mohammad H; Davidson, Bradley S

    2011-01-01

    This paper presents an approach for measuring and monitoring human body joint angles using inertial measurement unit (IMU) sensors. This type of monitoring is beneficial for therapists and physicians because it facilitates remote assessment of patient activities. In our approach, two IMUs are mounted on the upper leg and the lower leg to measure the Euler angles of each segment. The Euler angles are sent via Bluetooth protocols to a pc for calculating the knee joint angle. In our experiments, we utilized a motion capture system to accurately measure the knee joint angle and used this as the ground truth to assess the accuracy of the IMU system. The range of average error of the system across a variety of motion trials was 0.08 to 3.06 degrees. In summary, the accuracy of the IMU measurement system currently outperforms existing wearable systems such as conductive fiber optic sensors and flex-sensors.

  4. 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.

  5. Implementing and Evaluating a Wireless Body Sensor System for Automated Physiological Data Acquisition at Home

    CERN Document Server

    Chen, Chao; 10.5121/ijcsit.2010.2303

    2010-01-01

    Advances in embedded devices and wireless sensor networks have resulted in new and inexpensive health care solutions. This paper describes the implementation and the evaluation of a wireless body sensor system that monitors human physiological data at home. Specifically, a waist-mounted triaxial accelerometer unit is used to record human movements. Sampled data are transmitted using an IEEE 802.15.4 wireless transceiver to a data logger unit. The wearable sensor unit is light, small, and consumes low energy, which allows for inexpensive and unobtrusive monitoring during normal daily activities at home. The acceleration measurement tests show that it is possible to classify different human motion through the acceleration reading. The 802.15.4 wireless signal quality is also tested in typical home scenarios. Measurement results show that even with interference from nearby IEEE 802.11 signals and microwave ovens, the data delivery performance is satisfactory and can be improved by selecting an appropriate channe...

  6. Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys

    CERN Document Server

    Mastrandrea, Rossana; Barrat, Alain

    2015-01-01

    Given their importance in shaping social networks and determining how information or diseases propagate in a population, human interactions are the subject of many data collection efforts. To this aim, different methods are commonly used, from diaries and surveys to wearable sensors. These methods show advantages and limitations but are rarely compared in a given setting. As surveys targeting friendship relations might suffer less from memory biases than contact diaries, it is also interesting to explore how daily contact patterns compare with friendship relations and with online social links. Here we make progresses in these directions by leveraging data from a French high school: face-to-face contacts measured by two concurrent methods, sensors and diaries; self-reported friendship surveys; Facebook links. We compare the data sets and find that most short contacts are not reported in diaries while long contacts have larger reporting probability, with a general tendency to overestimate durations. Measured co...

  7. 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.

  8. Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams.

    Science.gov (United States)

    Adams, Roy J; Saleheen, Nazir; Thomaz, Edison; Parate, Abhinav; Kumar, Santosh; Marlin, Benjamin M

    2016-06-01

    The field of mobile health (mHealth) has the potential to yield new insights into health and behavior through the analysis of continuously recorded data from wearable health and activity sensors. In this paper, we present a hierarchical span-based conditional random field model for the key problem of jointly detecting discrete events in such sensor data streams and segmenting these events into high-level activity sessions. Our model includes higher-order cardinality factors and inter-event duration factors to capture domain-specific structure in the label space. We show that our model supports exact MAP inference in quadratic time via dynamic programming, which we leverage to perform learning in the structured support vector machine framework. We apply the model to the problems of smoking and eating detection using four real data sets. Our results show statistically significant improvements in segmentation performance relative to a hierarchical pairwise CRF.

  9. Quaternion-Based Gesture Recognition Using Wireless Wearable Motion Capture Sensors.

    Science.gov (United States)

    Alavi, Shamir; Arsenault, Dennis; Whitehead, Anthony

    2016-04-28

    This work presents the development and implementation of a unified multi-sensor human motion capture and gesture recognition system that can distinguish between and classify six different gestures. Data was collected from eleven participants using a subset of five wireless motion sensors (inertial measurement units) attached to their arms and upper body from a complete motion capture system. We compare Support Vector Machines and Artificial Neural Networks on the same dataset under two different scenarios and evaluate the results. Our study indicates that near perfect classification accuracies are achievable for small gestures and that the speed of classification is sufficient to allow interactivity. However, such accuracies are more difficult to obtain when a participant does not participate in training, indicating that more work needs to be done in this area to create a system that can be used by the general population.

  10. 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

  11. 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

  12. 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).

  13. Wearable Sensor-Based Biofeedback Training for Balance and Gait in Parkinson Disease: A Pilot Randomized Controlled Trial.

    Science.gov (United States)

    Carpinella, Ilaria; Cattaneo, Davide; Bonora, Gianluca; Bowman, Thomas; Martina, Laura; Montesano, Angelo; Ferrarin, Maurizio

    2017-04-01

    To analyze the feasibility and efficacy of a novel system (Gamepad [GAMing Experience in PArkinson's Disease]) for biofeedback rehabilitation of balance and gait in Parkinson disease (PD). Randomized controlled trial. Clinical rehabilitation gym. Subjects with PD (N=42) were randomized into experimental and physiotherapy without biofeedback groups. Both groups underwent 20 sessions of training for balance and gait. The experimental group performed tailored functional tasks using Gamepad. The system, based on wearable inertial sensors, provided users with real-time visual and acoustic feedback about their movement during the exercises. The physiotherapy group underwent individually structured physiotherapy without feedback. Assessments were performed by a blinded examiner preintervention, postintervention, and at 1-month follow-up. Primary outcomes were the Berg Balance Scale (BBS) and 10-m walk test (10MWT). Secondary outcomes included instrumental stabilometric indexes and the Tele-healthcare Satisfaction Questionnaire. Gamepad was well accepted by participants. Statistically significant between-group differences in BBS scores suggested better balance performances of the experimental group compared with the physiotherapy without biofeedback group both posttraining (experimental group-physiotherapy without biofeedback group: mean, 2.3±3.4 points; P=.047) and at follow-up (experimental group-physiotherapy without biofeedback group: mean, 2.7±3.3 points; P=.018). Posttraining stabilometric indexes showed that mediolateral body sway during upright stance was significantly reduced in the experimental group compared with the physiotherapy without biofeedback group (experimental group-physiotherapy without biofeedback group: -1.6±1.5mm; P=.003). No significant between-group differences were found in the other outcomes. Gamepad-based training was feasible and superior to physiotherapy without feedback in improving BBS performance and retaining it for 1 month. After

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

    DEFF Research Database (Denmark)

    Jalaliniya, Shahram

    2017-01-01

    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.......g. Google Glass) are far away from such a tight integration with their users. Apart from the purely technological challenges, progress is also hampered by the common attempt by system designers to deploy existing interaction paradigms from desktop and mobile computing (e.g. visual output, touch-based input...

  15. Highly Sensitive Wearable Textile-Based Humidity Sensor Made of High-Strength, Single-Walled Carbon Nanotube/Poly(vinyl alcohol) Filaments.

    Science.gov (United States)

    Zhou, Gengheng; Byun, Joon-Hyung; Oh, Youngseok; Jung, Byung-Mun; Cha, Hwa-Jin; Seong, Dong-Gi; Um, Moon-Kwang; Hyun, Sangil; Chou, Tsu-Wei

    2017-02-08

    Textile-based humidity sensors can be an important component of smart wearable electronic-textiles and have potential applications in the management of wounds, bed-wetting, and skin pathologies or for microclimate control in clothing. Here, we report a wearable textile-based humidity sensor for the first time using high strength (∼750 MPa) and ultratough (energy-to-break, 4300 J g(-1)) SWCNT/PVA filaments via a wet-spinning process. The conductive SWCNT networks in the filaments can be modulated by adjusting the intertube distance by swelling the PVA molecular chains via the absorption of water molecules. The diameter of a SWCNT/PVA filament under wet conditions can be as much as 2 times that under dry conditions. The electrical resistance of a fiber sensor stitched onto a hydrophobic textile increases significantly (by more than 220 times) after water sprayed. Textile-based humidity sensors using a 1:5 weight ratio of SWCNT/PVA filaments showed high sensitivity in high relative humidity. The electrical resistance increases by more than 24 times in a short response time of 40 s. We also demonstrated that our sensor can be used to monitor water leakage on a high hydrophobic textile (contact angle of 115.5°). These smart textiles will pave a new way for the design of novel wearable sensors for monitoring blood leakage, sweat, and underwear wetting.

  16. Large-Scale Wearable Sensor Deployment in Parkinson’s Patients: The Parkinson@Home Study Protocol

    Science.gov (United States)

    Hahn, Tim; de Vries, Nienke M; Cohen, Eli; Bataille, Lauren; Little, Max A; Baldus, Heribert; Bloem, Bastiaan R; Faber, Marjan J

    2016-01-01

    Background Long-term management of Parkinson’s disease does not reach its full potential because we lack knowledge about individual variations in clinical presentation and disease progression. Continuous and longitudinal assessments in real-life (ie, within the patients’ own home environment) might fill this knowledge gap. Objective The primary aim of the Parkinson@Home study is to evaluate the feasibility and compliance of using multiple wearable sensors to collect clinically relevant data. Our second aim is to address the usability of these data for answering clinical research questions. Finally, we aim to build a database for future validation of novel algorithms applied to sensor-derived data from Parkinson’s patients during daily functioning. Methods The Parkinson@Home study is a two-phase observational study involving 1000 Parkinson’s patients and 250 physiotherapists. Disease status is assessed using a short version of the Parkinson's Progression Markers Initiative protocol, performed by certified physiotherapists. Additionally, participants will wear a set of sensors (smartwatch, smartphone, and fall detector), and use these together with a customized smartphone app (Fox Insight), 24/7 for 3 months. The sensors embedded within the smartwatch and fall detector may be used to estimate physical activity, tremor, sleep quality, and falls. Medication intake and fall incidents will be measured via patients’ self-reports in the smartphone app. Phase one will address the feasibility of the study protocol. In phase two, mathematicians will distill relevant summary statistics from the raw sensor signals, which will be compared against the clinical outcomes. Results Recruitment of 300 participants for phase one was concluded in March, 2016, and the follow-up period will end in June, 2016. Phase two will include the remaining participants, and will commence in September, 2016. Conclusions The Parkinson@Home study is expected to generate new insights into the

  17. Intrusion Detection in Wireless Body Sensor Networks

    Directory of Open Access Journals (Sweden)

    Nadya El MOUSSAID

    2017-01-01

    Full Text Available The recent advances in electronic and robotics industry have enabled the manufacturing of sensors capable of measuring a set of application-oriented parameters and transmit them back to the base station for analysis purposes. These sensors are widely used in many applications including the healthcare systems forming though a Wireless Body Sensor Networks. The medical data must be highly secured and possible intrusion has to be fully detected to proceed with the prevention phase. In this paper, we propose a new intrusion superframe schema for 802.15.6 standard to detect the cloning attack. The results proved the efficiency of our technique in detecting this type of attack based on 802.15.6 parameters performances coupled with frequency switching at the radio model.

  18. 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…

  19. Body sensor networks for ubiquitous healthcare

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    Body sensor networks provide a platform for ubiquitous healthcare, driving the diagnosis in hospital static environment to the daily life dynamic context. We realized the importance of sensing of activities, which is not only a dimension of human health but also important context information for diagnosis based on the physiologic data. This paper presents our ubiquitous healthcare system, uCare. It consists of uCare devices and a server system. Currently, the uCare system is designed for cardiovascular dise...

  20. 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

  1. 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.

  2. 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.

  3. Development of a Wearable Sensor System for Dynamically Mapping the Behavior of an Energy Storing and Returning Prosthetic Foot

    Science.gov (United States)

    Hawkins, James; Noroozi, Siamak; Dupac, Mihai; Sewell, Philip

    2016-06-01

    It has been recognized that that the design and prescription of Energy Storing and Returning prosthetic running feet are not well understood and that further information on their performance would be beneficial to increase this understanding. Dynamic analysis of an amputee wearing a prosthetic foot is typically performed using reflective markers and motion-capture systems. High-speed cameras and force plates are used to collect data of a few strides. This requires specialized and expensive equipment in an unrepresentative environment within a large area. Inertial Measurement Units are also capable of being used as wearable sensors but suffer from drift issues. This paper presents the development of a wearable sensing system that records the action of an Energy Storing and Returning prosthetic running foot (sagittal plane displacement and ground contact position) which could have research and/or clinical applications. This is achieved using five standalone pieces of apparatus including foot-mounted pressure sensors and a rotary vario-resistive displacement transducer. It is demonstrated, through the collection of profiles for both foot deflection and ground contact point over the duration of a stride, that the system can be attached to an amputee's prosthesis and used in a non-laboratory environment. It was found from the system that the prosthetic ground contact point, for the amputee tested, progresses along the effective metatarsal portion of the prosthetic foot towards the distal end of the prosthesis over the duration of the stride. Further investigation of the effective stiffness changes of the foot due to the progression of the contact point is warranted.

  4. Development of a Wearable Sensor System for Dynamically Mapping the Behavior of an Energy Storing and Returning Prosthetic Foot

    Directory of Open Access Journals (Sweden)

    Hawkins James

    2016-06-01

    Full Text Available It has been recognized that that the design and prescription of Energy Storing and Returning prosthetic running feet are not well understood and that further information on their performance would be beneficial to increase this understanding. Dynamic analysis of an amputee wearing a prosthetic foot is typically performed using reflective markers and motion-capture systems. High-speed cameras and force plates are used to collect data of a few strides. This requires specialized and expensive equipment in an unrepresentative environment within a large area. Inertial Measurement Units are also capable of being used as wearable sensors but suffer from drift issues. This paper presents the development of a wearable sensing system that records the action of an Energy Storing and Returning prosthetic running foot (sagittal plane displacement and ground contact position which could have research and/or clinical applications. This is achieved using five standalone pieces of apparatus including foot-mounted pressure sensors and a rotary vario-resistive displacement transducer. It is demonstrated, through the collection of profiles for both foot deflection and ground contact point over the duration of a stride, that the system can be attached to an amputee’s prosthesis and used in a non-laboratory environment. It was found from the system that the prosthetic ground contact point, for the amputee tested, progresses along the effective metatarsal portion of the prosthetic foot towards the distal end of the prosthesis over the duration of the stride. Further investigation of the effective stiffness changes of the foot due to the progression of the contact point is warranted.

  5. A pervasive body sensor network for measuring postoperative recovery at home.

    Science.gov (United States)

    Aziz, O; Atallah, L; Lo, B; Elhelw, M; Wang, L; Yang, G Z; Darzi, A

    2007-06-01

    Patients going home following major surgery are susceptible to complications such as wound infection, abscess formation, malnutrition, poor analgesia, and depression, all of which can develop after the fifth postoperative day and slow recovery. Although current hospital recovery monitoring systems are effective during perioperative and early postoperative periods, they cannot be used when the patient is at home. Measuring and quantifying home recovery is currently a subjective and labor-intensive process. This case report highlights the development and piloting of a wireless body sensor network to monitor postoperative recovery at home in patients undergoing abdominal surgery. The device consists of wearable sensors (vital signs, motion) combined with miniaturized computers wirelessly linked to each other, thus allowing continuous monitoring of patients in a pervasive (unobtrusive) manner in any environment. Initial pilot work with results in both the simulated (with volunteers) and the real home environment (with patients) is presented.

  6. Effectiveness of a Batteryless and Wireless Wearable Sensor System for Identifying Bed and Chair Exits in Healthy Older People.

    Science.gov (United States)

    Torres, Roberto Luis Shinmoto; Visvanathan, Renuka; Hoskins, Stephen; van den Hengel, Anton; Ranasinghe, Damith C

    2016-04-15

    Aging populations are increasing worldwide and strategies to minimize the impact of falls on older people need to be examined. Falls in hospitals are common and current hospital technological implementations use localized sensors on beds and chairs to alert caregivers of unsupervised patient ambulations; however, such systems have high false alarm rates. We investigate the recognition of bed and chair exits in real-time using a wireless wearable sensor worn by healthy older volunteers. Fourteen healthy older participants joined in supervised trials. They wore a batteryless, lightweight and wireless sensor over their attire and performed a set of broadly scripted activities. We developed a movement monitoring approach for the recognition of bed and chair exits based on a machine learning activity predictor. We investigated the effectiveness of our approach in generating bed and chair exit alerts in two possible clinical deployments (Room 1 and Room 2). The system obtained recall results above 93% (Room 2) and 94% (Room 1) for bed and chair exits, respectively. Precision was >78% and 67%, respectively, while F-score was >84% and 77% for bed and chair exits, respectively. This system has potential for real-time monitoring but further research in the final target population of older people is necessary.

  7. Effectiveness of a Batteryless and Wireless Wearable Sensor System for Identifying Bed and Chair Exits in Healthy Older People

    Directory of Open Access Journals (Sweden)

    Roberto Luis Shinmoto Torres

    2016-04-01

    Full Text Available Aging populations are increasing worldwide and strategies to minimize the impact of falls on older people need to be examined. Falls in hospitals are common and current hospital technological implementations use localized sensors on beds and chairs to alert caregivers of unsupervised patient ambulations; however, such systems have high false alarm rates. We investigate the recognition of bed and chair exits in real-time using a wireless wearable sensor worn by healthy older volunteers. Fourteen healthy older participants joined in supervised trials. They wore a batteryless, lightweight and wireless sensor over their attire and performed a set of broadly scripted activities. We developed a movement monitoring approach for the recognition of bed and chair exits based on a machine learning activity predictor. We investigated the effectiveness of our approach in generating bed and chair exit alerts in two possible clinical deployments (Room 1 and Room 2. The system obtained recall results above 93% (Room 2 and 94% (Room 1 for bed and chair exits, respectively. Precision was >78% and 67%, respectively, while F-score was >84% and 77% for bed and chair exits, respectively. This system has potential for real-time monitoring but further research in the final target population of older people is necessary.

  8. 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.

  9. 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.

  10. Observing the state of balance with a single upper-body sensor

    Directory of Open Access Journals (Sweden)

    Charlotte ePaiman

    2016-04-01

    Full Text Available 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, only very limited sensor instrumentation is possible, often restricted to inertial measurement units at waist level. We propose to augment this limited sensor information by combining it with additional knowledge on human gait, in the form of an observer concept. The observer contains a combination of validated concepts to model human gait: A spring-loaded inverted pendulum model with articulated upper body, where foot placement and stance leg are controlled via the extrapolated center of mass (XCoM and the virtual pivot point (VPP, respectively. State estimation is performed via an Additive Unscented Kalman Filter (Additive UKF. We investigated sensitivity of the proposed concept to model uncertainties, and we evaluated observer performance with real data from human subjects walking on a treadmill. Data was collected from an Inertial Measurement Unit (IMU placed near the subject's center of mass (CoM, and observer estimates were compared to the ground truth as obtained via infrared motion capture. We found that the root mean squared deviation did not exceed 13cm on position, 22cm/s on velocity (0.56m/s-1.35m/s, 1.2degrees on orientation and 17degrees/s on angular velocity.

  11. Conductive fiber-based ultrasensitive textile pressure sensor for wearable electronics.

    Science.gov (United States)

    Lee, Jaehong; Kwon, Hyukho; Seo, Jungmok; Shin, Sera; Koo, Ja Hoon; Pang, Changhyun; Son, Seungbae; Kim, Jae Hyung; Jang, Yong Hoon; Kim, Dae Eun; Lee, Taeyoon

    2015-04-17

    A flexible and sensitive textile-based pressure sensor is developed using highly conductive fibers coated with dielectric rubber materials. The pressure sensor exhibits superior sensitivity, very fast response time, and high stability, compared with previous textile-based pressure sensors. By using a weaving method, the pressure sensor can be applied to make smart gloves and clothes that can control machines wirelessly as human-machine interfaces.

  12. 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

  13. Energy Efficient Design for Body Sensor Nodes

    Directory of Open Access Journals (Sweden)

    Yanqing Zhang

    2011-04-01

    Full Text Available This paper describes the hardware requirements and design constraints that derive from unique features of body sensor networks (BSNs. Based on the BSN requirements, we examine the tradeoff between custom hardware and commercial off the shelf (COTS designs for BSNs. The broad range of BSN applications includes situations where either custom chips or COTS design is optimal. For both types of nodes, we survey key techniques to improve energy efficiency in BSNs and identify general approaches to energy efficiency in this space.

  14. Highly Sensitive, Flexible, and Wearable Pressure Sensor Based on a Giant Piezocapacitive Effect of Three-Dimensional Microporous Elastomeric Dielectric Layer.

    Science.gov (United States)

    Kwon, Donguk; Lee, Tae-Ik; Shim, Jongmin; Ryu, Seunghwa; Kim, Min Seong; Kim, Seunghwan; Kim, Taek-Soo; Park, Inkyu

    2016-07-06

    We report a flexible and wearable pressure sensor based on the giant piezocapacitive effect of a three-dimensional (3-D) microporous dielectric elastomer, which is capable of highly sensitive and stable pressure sensing over a large tactile pressure range. Due to the presence of micropores within the elastomeric dielectric layer, our piezocapacitive pressure sensor is highly deformable by even very small amounts of pressure, leading to a dramatic increase in its sensitivity. Moreover, the gradual closure of micropores under compression increases the effective dielectric constant, thereby further enhancing the sensitivity of the sensor. The 3-D microporous dielectric layer with serially stacked springs of elastomer bridges can cover a much wider pressure range than those of previously reported micro-/nanostructured sensing materials. We also investigate the applicability of our sensor to wearable pressure-sensing devices as an electronic pressure-sensing skin in robotic fingers as well as a bandage-type pressure-sensing device for pulse monitoring at the human wrist. Finally, we demonstrate a pressure sensor array pad for the recognition of spatially distributed pressure information on a plane. Our sensor, with its excellent pressure-sensing performance, marks the realization of a true tactile pressure sensor presenting highly sensitive responses to the entire tactile pressure range, from ultralow-force detection to high weights generated by human activity.

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

    Science.gov (United States)

    Li, Yanran; Zhang, Xu; Gong, Yanan; Cheng, Ying; Gao, Xiaoping; Chen, Xiang

    2017-03-13

    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.

  16. 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.

  17. A low power level-crossing ADC for wearable wireless ECG sensors.

    Science.gov (United States)

    Zhenzhen Tian; Rendong Ying; Peilin Liu; Guoxing Wang; Yong Lian

    2016-08-01

    Ultra-low power consumption is desired in most wearable biomedical devices. The event-driven based Analog-to-Digital converter (ADC) could be an excellent candidate for low power system because of the reduction in sampling points for biosignals. In the existing event-driven based ADC architectures, two or more high precision comparators are utilized to sample the input signal. In this paper, we propose a new scheme utilizing only one high precision comparator to genertate samples with the assistance of a low precision one. From the Matlab simulations on real ECG signal, it is shown that around 25% reduction on the number of samples can be be achieved compared with the Nyquist sampling scheme.

  18. 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.

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

    Science.gov (United States)

    Mehmood, Raja Majid; Lee, Hyo Jong

    2017-02-08

    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.

  20. Objective biomarkers of balance and gait for Parkinson's disease using body-worn sensors.

    Science.gov (United States)

    Horak, Fay B; Mancini, Martina

    2013-09-15

    Balance and gait impairments characterize the progression of Parkinson's disease (PD), predict the risk of falling, and are important contributors to reduced quality of life. Advances in technology of small, body-worn, inertial sensors have made it possible to develop quick, objective measures of balance and gait impairments in the clinic for research trials and clinical practice. Objective balance and gait metrics may eventually provide useful biomarkers for PD. In fact, objective balance and gait measures are already being used as surrogate endpoints for demonstrating clinical efficacy of new treatments, in place of counting falls from diaries, using stop-watch measures of gait speed, or clinical balance rating scales. This review summarizes the types of objective measures available from body-worn sensors. The metrics are organized based on the neural control system for mobility affected by PD: postural stability in stance, postural responses, gait initiation, gait (temporal-spatial lower and upper body coordination and dynamic equilibrium), postural transitions, and freezing of gait. However, the explosion of metrics derived by wearable sensors during prescribed balance and gait tasks, which are abnormal in individuals with PD, do not yet qualify as behavioral biomarkers, because many balance and gait impairments observed in PD are not specific to the disease, nor have they been related to specific pathophysiologic biomarkers. In the future, the most useful balance and gait biomarkers for PD will be those that are sensitive and specific for early PD and are related to the underlying disease process.

  1. 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.

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

    OpenAIRE

    2012-01-01

    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 electr...

  3. 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.

  4. 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

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

    Directory of Open Access Journals (Sweden)

    Kamuran Turksoy

    2017-03-01

    Full Text Available 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.

  6. 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

    2017-03-07

    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.

  7. Detection of anticipatory postural adjustments prior to gait initiation using inertial wearable sensors

    Directory of Open Access Journals (Sweden)

    Sekine Masaki

    2011-04-01

    Full Text Available Abstract Background The present study was performed to evaluate and characterize the potential of accelerometers and angular velocity sensors to detect and assess anticipatory postural adjustments (APAs generated by the first step at the beginning of the gait. This paper proposes an algorithm to automatically detect certain parameters of APAs using only inertial sensors. Methods Ten young healthy subjects participated in this study. The subjects wore an inertial unit containing a triaxial accelerometer and a triaxial angular velocity sensor attached to the lower back and one footswitch on the dominant leg to detect the beginning of the step. The subjects were standing upright on a stabilometer to detect the center of pressure displacement (CoP generated by the anticipatory adjustments. The subjects were asked to take a step forward at their own speed and stride length. The duration and amplitude of the APAs detected by the accelerometer and angular velocity sensors were measured and compared with the results obtained from the stabilometer. The different phases of gait initiation were identified and compared using inertial sensors. Results The APAs were detected by all of the sensors. Angular velocity sensors proved to be adequate to detect the beginning of the step in a manner similar to the footswitch by using a simple algorithm, which is easy to implement in low computational power devices. The amplitude and duration of APAs detected using only inertial sensors were similar to those detected by the stabilometer. An automatic algorithm to detect APA duration using triaxial inertial sensors was proposed. Conclusions These results suggest that the feasibility of accelerometers is improved through the use of angular velocity sensors, which can be used to automatically detect and evaluate APAs. The results presented can be used to develop portable sensors that may potentially be useful for monitoring patients in the home environment, thus

  8. 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.

  9. DEVELOPMENT OF A WEARABLE GLUCOSE SENSOR - STUDIES IN HEALTHY-VOLUNTEERS AND IN DIABETIC-PATIENTS

    NARCIS (Netherlands)

    AALDERS, AL; SCHMIDT, FJ; SCHOONEN, AJM; BROEK, IR; MAESSEN, AGFM; DOORENBOS, H

    1991-01-01

    A glucose sensor with a subcutaneous dialysis system was tested in six healthy volunteers during an oral glucose tolerance test and in ten diabetic patients with hyperglycemia during rapid decline of blood glucose levels. There was a good correlation between sensor and blood glucose values. During o

  10. A low-power multi-modal body sensor network with application to epileptic seizure monitoring.

    Science.gov (United States)

    Altini, Marco; Del Din, Silvia; Patel, Shyamal; Schachter, Steven; Penders, Julien; Bonato, Paolo

    2011-01-01

    Monitoring patients' physiological signals during their daily activities in the home environment is one of the challenge of the health care. New ultra-low-power wireless technologies could help to achieve this goal. In this paper we present a low-power, multi-modal, wearable sensor platform for the simultaneous recording of activity and physiological data. First we provide a description of the wearable sensor platform, and its characteristics with respect to power consumption. Second we present the preliminary results of the comparison between our sensors and a reference system, on healthy subjects, to test the reliability of the detected physiological (electrocardiogram and respiration) and electromyography signals.

  11. Pointing Devices for Wearable Computers

    OpenAIRE

    Andrés A. Calvo; Saverio Perugini

    2014-01-01

    We present a survey of pointing devices for wearable computers, which are body-mounted devices that users can access at any time. Since traditional pointing devices (i.e., mouse, touchpad, and trackpoint) were designed to be used on a steady and flat surface they are inappropriate for wearable computers. Just as the advent of laptops resulted in the development of the touchpad and trackpoint, the emergence of wearable computers is leading to the development of pointing devices designed for th...

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

    Energy Technology Data Exchange (ETDEWEB)

    Zakharov, P; Talary, M S; Caduff, A [Solianis Monitoring AG, Leutschenbachstrasse 46, CH-8050 Zuerich (Switzerland)], E-mail: andreas.caduff@solianis.com

    2009-09-07

    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.

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

    Science.gov (United States)

    Li, Jianfeng; Xu, Bingang

    2015-12-01

    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.

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

    Science.gov (United States)

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

    2009-09-01

    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.

  15. A wearable bluetooth LE sensor for patient monitoring during MRI scans.

    Science.gov (United States)

    Vogt, Christian; Reber, Jonas; Waltisberg, Daniel; Buthe, Lars; Marjanovic, Josip; Munzenrieder, Niko; Pruessmann, Klaas P; Troster, Gerhard

    2016-08-01

    This paper presents a working prototype of a wearable patient monitoring device capable of recording the heart rate, blood oxygen saturation, surface temperature and humidity during an magnetic resonance imaging (MRI) experiment. The measured values are transmitted via Bluetooth low energy (LE) and displayed in real time on a smartphone on the outside of the MRI room. During 7 MRI image acquisitions of at least 1 min and a total duration of 25 min no Bluetooth data packets were lost. The raw measurements of the light intensity for the photoplethysmogram based heart rate measurement shows an increased noise floor by 50LSB (least significant bit) during the MRI operation, whereas the temperature and humidity readings are unaffected. The device itself creates a magnetic resonance (MR) signal loss with a radius of 14 mm around the device surface and shows no significant increase in image noise of an acquired MRI image due to its radio frequency activity. This enables continuous and unobtrusive patient monitoring during MRI scans.

  16. Innovation through Wearable Sensors to Collect Real-Life Data among Pediatric Patients with Cardiometabolic Risk Factors

    Directory of Open Access Journals (Sweden)

    Kestens Yan

    2014-01-01

    Full Text Available Background. While increasing evidence links environments to health behavior, clinicians lack information about patients’ physical activity levels and lifestyle environments. We present mobile health tools to collect and use spatio-behavioural lifestyle data for personalized physical activity plans in clinical settings. Methods. The Dyn@mo lifestyle intervention was developed at the Sainte-Justine University Hospital Center to promote physical activity and reduce sedentary time among children with cardiometabolic risk factors. Mobility, physical activity, and heart rate were measured in free-living environments during seven days. Algorithms processed data to generate spatio-behavioural indicators that fed a web-based interactive mapping application for personalised counseling. Proof of concept and tools are presented using data collected among the first 37 participants recruited in 2011. Results. Valid accelerometer data was available for 5.6 (SD=1.62 days in average, heart rate data for 6.5 days, and GPS data was available for 6.1 (2.1 days. Spatio-behavioural indicators were shared between patients, parents, and practitioners to support counseling. Conclusion. Use of wearable sensors along with data treatment algorithms and visualisation tools allow to better measure and describe real-life environments, mobility, physical activity, and physiological responses. Increased specificity in lifestyle interventions opens new avenues for remote patient monitoring and intervention.

  17. Innovation through Wearable Sensors to Collect Real-Life Data among Pediatric Patients with Cardiometabolic Risk Factors.

    Science.gov (United States)

    Yan, Kestens; Tracie, Barnett; Marie-Ève, Mathieu; Mélanie, Henderson; Jean-Luc, Bigras; Benoit, Thierry; St-Onge, Maxime; Marie, Lambert

    2014-01-01

    Background. While increasing evidence links environments to health behavior, clinicians lack information about patients' physical activity levels and lifestyle environments. We present mobile health tools to collect and use spatio-behavioural lifestyle data for personalized physical activity plans in clinical settings. Methods. The Dyn@mo lifestyle intervention was developed at the Sainte-Justine University Hospital Center to promote physical activity and reduce sedentary time among children with cardiometabolic risk factors. Mobility, physical activity, and heart rate were measured in free-living environments during seven days. Algorithms processed data to generate spatio-behavioural indicators that fed a web-based interactive mapping application for personalised counseling. Proof of concept and tools are presented using data collected among the first 37 participants recruited in 2011. Results. Valid accelerometer data was available for 5.6 (SD = 1.62) days in average, heart rate data for 6.5 days, and GPS data was available for 6.1 (2.1) days. Spatio-behavioural indicators were shared between patients, parents, and practitioners to support counseling. Conclusion. Use of wearable sensors along with data treatment algorithms and visualisation tools allow to better measure and describe real-life environments, mobility, physical activity, and physiological responses. Increased specificity in lifestyle interventions opens new avenues for remote patient monitoring and intervention.

  18. A Digital Ecosystem of Diabetes Data and Technology: Services, Systems, and Tools Enabled by Wearables, Sensors, and Apps.

    Science.gov (United States)

    Heintzman, Nathaniel D

    2015-12-20

    The management of type 1 diabetes (T1D) ideally involves regimented measurement of various health signals; constant interpretation of diverse kinds of data; and consistent cohesion between patients, caregivers, and health care professionals (HCPs). In the context of myriad factors that influence blood glucose dynamics for each individual patient (eg, medication, activity, diet, stress, sleep quality, hormones, environment), such coordination of self-management and clinical care is a great challenge, amplified by the routine unavailability of many types of data thought to be useful in diabetes decision-making. While much remains to be understood about the physiology of diabetes and blood glucose dynamics at the level of the individual, recent and emerging medical and consumer technologies are helping the diabetes community to take great strides toward truly personalized, real-time, data-driven management of this chronic disease. This review describes "connected" technologies--such as smartphone apps, and wearable devices and sensors--which comprise part of a new digital ecosystem of data-driven tools that can link patients and their care teams for precision management of diabetes. These connected technologies are rich sources of physiologic, behavioral, and contextual data that can be integrated and analyzed in "the cloud" for research into personal models of glycemic dynamics, and employed in a multitude of applications for mobile health (mHealth) and telemedicine in diabetes care. © 2015 Diabetes Technology Society.

  19. Lightweight, Wearable Metal Rubber-Textile Sensor for In-Situ Lunar Autonomous Health Monitoring Project

    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...

  20. IMPLEMENTING AND EVALUATING A WIRELESS BODY SENSOR SYSTEM FOR AUTOMATED PHYSIOLOGICAL DATA ACQUISITION AT HOME

    Directory of Open Access Journals (Sweden)

    Carlos Pomalaza-Raez

    2010-06-01

    Full Text Available Advances in embedded devices and wireless sensor networks have resulted in new and inexpensive healthcare solutions. This paper describes the implementation and the evaluation of a wireless body sensorsystem that monitors human physiological data at home. Specifically, a waist-mounted triaxialaccelerometer unit is used to record human movements. Sampled data are transmitted using an IEEE802.15.4 wireless transceiver to a data logger unit. The wearable sensor unit is light, small, andconsumes low energy, which allows for inexpensive and unobtrusive monitoring during normal dailyactivities at home. The acceleration measurement tests show that it is possible to classify different humanmotion through the acceleration reading. The 802.15.4 wireless signal quality is also tested in typicalhome scenarios. Measurement results show that even with interference from nearby IEEE 802.11 signalsand microwave ovens, the data delivery performance is satisfactory and can be improved by selecting anappropriate channel. Moreover, we found that the wireless signal can be attenuated by housing materials,home appliances, and even plants. Therefore, the deployment of wireless body sensor systems at homeneeds to take all these factors into consideration.

  1. Channel-Based Key Generation for Encrypted Body-Worn Wireless Sensor Networks

    Science.gov (United States)

    Van Torre, Patrick

    2016-01-01

    Body-worn sensor networks are important for rescue-workers, medical and many other applications. Sensitive data are often transmitted over such a network, motivating the need for encryption. Body-worn sensor networks are deployed in conditions where the wireless communication channel varies dramatically due to fading and shadowing, which is considered a disadvantage for communication. Interestingly, these channel variations can be employed to extract a common encryption key at both sides of the link. Legitimate users share a unique physical channel and the variations thereof provide data series on both sides of the link, with highly correlated values. An eavesdropper, however, does not share this physical channel and cannot extract the same information when intercepting the signals. This paper documents a practical wearable communication system implementing channel-based key generation, including an implementation and a measurement campaign comprising indoor as well as outdoor measurements. The results provide insight into the performance of channel-based key generation in realistic practical conditions. Employing a process known as key reconciliation, error free keys are generated in all tested scenarios. The key-generation system is computationally simple and therefore compatible with the low-power micro controllers and low-data rate transmissions commonly used in wireless sensor networks. PMID:27618051

  2. Channel-Based Key Generation for Encrypted Body-Worn Wireless Sensor Networks.

    Science.gov (United States)

    Van Torre, Patrick

    2016-09-08

    Body-worn sensor networks are important for rescue-workers, medical and many other applications. Sensitive data are often transmitted over such a network, motivating the need for encryption. Body-worn sensor networks are deployed in conditions where the wireless communication channel varies dramatically due to fading and shadowing, which is considered a disadvantage for communication. Interestingly, these channel variations can be employed to extract a common encryption key at both sides of the link. Legitimate users share a unique physical channel and the variations thereof provide data series on both sides of the link, with highly correlated values. An eavesdropper, however, does not share this physical channel and cannot extract the same information when intercepting the signals. This paper documents a practical wearable communication system implementing channel-based key generation, including an implementation and a measurement campaign comprising indoor as well as outdoor measurements. The results provide insight into the performance of channel-based key generation in realistic practical conditions. Employing a process known as key reconciliation, error free keys are generated in all tested scenarios. The key-generation system is computationally simple and therefore compatible with the low-power micro controllers and low-data rate transmissions commonly used in wireless sensor networks.

  3. Wearable Barometric Pressure Sensor to Improve Postural Transition Recognition of Mobility-Impaired Stroke Patients.

    Science.gov (United States)

    Masse, Fabien; Gonzenbach, Roman; Paraschiv-Ionescu, Anisoara; Luft, Andreas R; Aminian, Kamiar

    2016-11-01

    Sit-to-stand and Stand-to-sit transfers (STS) provide relevant information regarding the functional limitation of mobility-impaired patients. The characterization of STS pattern using a single trunk fixed inertial sensor has been proposed as an objective tool to assess changes in functional ability and balance due to disease. Despite significant research efforts, STS quantification remains challenging due to the high inter- and between- subject variability of this motion pattern. The present study aims to improve the performance of STS detection and classification by fusing the information from barometric pressure (BP) and inertial sensors while keeping a single sensor located at the trunk. A total number of 345 STSs were recorded from 12 post-stroke patients monitored in a semi-structured conditioned protocol. Model-based features of BP signal were combined with kinematic parameters from accelerometer and/or gyroscope and used in a logistic regression-based classifier to detect STS and then identify their types. The correct classification rate was 90.6% with full sensor (BP and inertial) configuration and 75.4% with single inertial sensor. Receiver-Operating-Characteristics analysis was carried out to characterize the robustness of the models. The results demonstrate the potential of BP sensor to improve the detection and classification of STSs when monitoring is performed unobtrusively in every-day life.

  4. Wearable Sensor System Powered by a Biofuel Cell for Detection of Lactate Levels in Sweat.

    Science.gov (United States)

    Garcia, S O; Ulyanova, Y V; Figueroa-Teran, R; Bhatt, K H; Singhal, S; Atanassov, P

    An NAD(+)-dependent enzymatic sensor with biofuel cell power source system for non-invasive monitoring of lactate in sweat was designed, developed, and tested. The sensor component, based on lactate dehydrogenase, showed linear current response with increasing lactate concentrations with limits of detection from 5 to 100 mM lactate and sensitivity of 0.2 µA.mM(-1) in the presence of target analyte. In addition to the sensor patch a power source was also designed, developed and tested. The power source was a biofuel cell designed to oxidize glucose via glucose oxidase. The biofuel cell showed excellent performance, achieving over 80 mA at 0.4 V (16 mW) in a footprint of 3.5 × 3.5 × 0.7 cm. Furthermore, in order to couple the sensor to the power source, system electronic components were designed and fabricated. These consisted of an energy harvester (EH) and a micropotentiostat (MP). The EH was employed for harvesting power provided by the biofuel cell as well as up-converting the voltage to 3.0 V needed for the operation of the MP. The sensor was attached to MP for chronoamperometric detection of lactate. The Sensor Patch System was demonstrated under laboratory conditions.

  5. 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

  6. 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)

  7. Wearable Sensor Technology Efficacy in Peripheral Vascular Disease (wSTEP): A Randomized Controlled Trial.

    Science.gov (United States)

    Normahani, Pasha; Kwasnicki, Richard; Bicknell, Colin; Allen, Louise; Jenkins, Mike P; Gibbs, Richard; Cheshire, Nicholas; Darzi, Ara; Riga, Celia

    2017-05-11

    To evaluate the effect of using wearable activity monitors (WAMs) in patients with intermittent claudication (IC) within a single-center randomized controlled trial. WAMs allow users to set daily activity targets and monitor their progress. They may offer an alternative treatment to supervised exercise programs (SEPs) for patients with IC. Thirty-seven patients with IC were recruited and randomized into intervention or control group. The intervention consisted of a feedback-enabled, wrist-worn activity monitor (WAM) in addition to access to SEP. The control group was given access to SEP only. The outcome measures were maximum walking distance (MWD), claudication distance (CD), and quality of life as measured by the VascuQol questionnaire. Participants were assessed upon recruitment, and at 3, 6, and 12 months. Patients in the WAM group showed significant improvement in MWD at 3 and 6 months (80-112 m, to 178 m; P saw a temporary increase in VascuQol score at 6 months (4.5 vs 4.7; P = 0.028), but no other improvements in MWD or CD were observed. Significantly higher improvements in MWD were seen in the WAM group compared with that in the control group at 6 months (82 vs -5 m; P = 0.009, r = 0.47) and 12 months (69 vs 7.5 m; P = 0.011, r = 0.52). The study demonstrates the significant, sustained benefit of WAM-led technologies for patients with IC. This potentially resource-sparing intervention is likely to provide a valuable adjunct or alternative to SEP.

  8. Automatic Recognition of Activities of Daily Living utilizing Insole Based and Wrist Worn Wearable Sensors.

    Science.gov (United States)

    Hegde, Nagaraj; Bries, Matthew; Swibas, Tracy; Melanson, Edward; Sazonov, Edward

    2017-08-01

    Automatic recognition of activities of daily living (ADL) is an important component in understanding of energy balance, quality of life and other areas of health and well-being. In our previous work, we had proposed an insole based activity monitor - SmartStep, designed to be socially acceptable and comfortable. The goals of the current study were: first, validation of SmartStep in recognition of a broad set of ADL; second, comparison of the SmartStep to a wrist sensor and testing these in combination; third, evaluation of SmartStep accuracy in measuring wear non-compliance and a novel activity class (driving); fourth, performing the validation in free living against a well-studied criterion measure (ActivPAL, PAL Technologies); and fifth, quantitative evaluation of the perceived comfort of SmartStep. The activity classification models were developed from a laboratory study consisting of 13 different activities under controlled conditions. Leave-one-out cross validation showed 89% accuracy for the combined SmartStep and wrist sensor, 81% for the SmartStep alone, and 69% for the wrist sensor alone. When household activities were grouped together as one class, SmartStep performed equally well compared to the combination of SmartStep and wrist-worn sensor (90% vs 94%) whereas the accuracy of the wrist sensor increased marginally (73% from 69%). SmartStep achieved 92% accuracy in recognition of non-wear and 82% in recognition of driving. Participants then were studied for a day in free-living conditions. The overall agreement with ActivPAL was 82.5% (compared to 97% for the laboratory study). The SmartStep scored the best on the perceived comfort reported at the end of the study. These results suggest that insole-based activity sensors may present a compelling alternative or companion to commonly used wrist devices.

  9. A multivariate time-warping based classifier for gesture recognition with wearable strain sensors.

    Science.gov (United States)

    Giorgino, Toni; Tormene, Paolo; Quaglini, Silvana

    2007-01-01

    Conductive elastomer elements can be industrially embedded into garments to form unobtrusive strain sensing stripes. The present article outlines the structure of a strain-sensor based gesture detection algorithm. Current sensing prototypes include several dozens of sensors; their redundancy with respect to the limb's degrees of freedom, and other artifacts implied by this measurement technique, call for the development of novel robust multivariate pattern-matching techniques. The algorithm's construction is explained, and its performances are evaluated in the context of motor rehabilitation exercises for both two-class and multi-class tasks.

  10. Wearable slot antenna at 2.45 GHz for off-body radiation: Analysis of efficiency, frequency shift, and body absorption.

    Science.gov (United States)

    Fernandez, Marta; Espinosa, Hugo G; Thiel, David V; Arrinda, Amaia

    2017-09-12

    The interaction of body-worn antennas with the human body causes a significant decrease in antenna efficiency and a shift in resonant frequency. A resonant slot in a small conductive box placed on the body has been shown to reduce these effects. The specific absorption rate is less than international health standards for most wearable antennas due to small transmitter power. This paper reports the linear relationship between power absorbed by biological tissues at different locations on the body and radiation efficiency based on numerical modeling (r = 0.99). While the -10 dB bandwidth of the antenna remained constant and equal to 12.5%, the maximum frequency shift occurred when the antenna was close to the elbow (6.61%) and on the thigh (5.86%). The smallest change was found on the torso (4.21%). Participants with body-mass index (BMI) between 17 and 29 kg/m(2) took part in experimental measurements, where the maximum frequency shift was 2.51%. Measurements showed better agreement with simulations on the upper arm. These experimental results demonstrate that the BMI for each individual had little effect on the performance of the antenna. Bioelectromagnetics. 2017;9999:XX-XX. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  11. 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.

  12. Preface for the book: Antennas And Propagation for Body-Centric Wireless Communications

    DEFF Research Database (Denmark)

    Frederiksen, Flemming Bjerge; Prasad, Ramjee

    2006-01-01

    The book address the following subjects: Body Centric Wireless Communications possibilities, Electromagnetic properties of the body, On-body Communication Channels at high and low frequency bands, Body Centric UWB Communications, Wearable Antennas for cellular and WLAN communications, Body-Sensor......-Sensor Networks, Antennas and Propagation for telemedicine and for wireless implants....

  13. 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.

  14. 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.

  15. Integration of wearable devices in a wireless sensor network for an e-health application

    OpenAIRE

    Castillejo Parrilla, Pedro; Martínez Ortega, José Fernán; Rodríguez Molina, Jesús; Cuerva García, Alexandra

    2013-01-01

    Applications based on Wireless Sensor Networks for Internet of Things scenarios are on the rise. The multiple possibilities they offer have spread towards previously hard to imagine fields, like e-health or human physiological monitoring. An application has been developed for its usage in scenarios where data collection is applied to smart spaces, aiming at its usage in fire fighting and sports. This application has been tested in a gymnasium with real, non-simulated nodes and devices. A Grap...

  16. Flexible and printable paper-based strain sensors for wearable and large-area green electronics

    Science.gov (United States)

    Liao, Xinqin; Zhang, Zheng; Liao, Qingliang; Liang, Qijie; Ou, Yang; Xu, Minxuan; Li, Minghua; Zhang, Guangjie; Zhang, Yue

    2016-06-01

    Paper-based (PB) green electronics is an emerging and potentially game-changing technology due to ease of recycling/disposal, the economics of manufacture and the applicability to flexible electronics. Herein, new-type printable PB strain sensors (PPBSSs) from graphite glue (graphite powder and methylcellulose) have been fabricated. The graphite glue is exposed to thermal annealing to produce surface micro/nano cracks, which are very sensitive to compressive or tensile strain. The devices exhibit a gauge factor of 804.9, response time of 19.6 ms and strain resolution of 0.038%, all performance indicators attaining and even surpassing most of the recently reported strain sensors. Due to the distinctive sensing properties, flexibility and robustness, the PPBSSs are suitable for monitoring of diverse conditions such as structural strain, vibrational motion, human muscular movements and visual control.Paper-based (PB) green electronics is an emerging and potentially game-changing technology due to ease of recycling/disposal, the economics of manufacture and the applicability to flexible electronics. Herein, new-type printable PB strain sensors (PPBSSs) from graphite glue (graphite powder and methylcellulose) have been fabricated. The graphite glue is exposed to thermal annealing to produce surface micro/nano cracks, which are very sensitive to compressive or tensile strain. The devices exhibit a gauge factor of 804.9, response time of 19.6 ms and strain resolution of 0.038%, all performance indicators attaining and even surpassing most of the recently reported strain sensors. Due to the distinctive sensing properties, flexibility and robustness, the PPBSSs are suitable for monitoring of diverse conditions such as structural strain, vibrational motion, human muscular movements and visual control. Electronic supplementary information (ESI) available. See DOI: 10.1039/c6nr02172g

  17. The social comfort of wearable technology and gestural interaction.

    Science.gov (United States)

    Dunne, Lucy E; Profita, Halley; Zeagler, Clint; Clawson, James; Gilliland, Scott; Do, Ellen Yi-Luen; Budd, Jim

    2014-01-01

    The "wearability" of wearable technology addresses the factors that affect the degree of comfort the wearer experiences while wearing a device, including physical, psychological, and social aspects. While the physical and psychological aspects of wearing technology have been investigated since early in the development of the field of wearable computing, the social aspects of wearability have been less fully-explored. As wearable technology becomes increasingly common on the commercial market, social wearability is becoming an ever-more-important variable contributing to the success or failure of new products. Here we present an analysis of social aspects of wearability within the context of the greater understanding of wearability in wearable technology, and focus on selected theoretical frameworks for understanding how wearable products are perceived and evaluated in a social context. Qualitative results from a study of social acceptability of on-body interactions are presented as a case study of social wearability.

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

    Science.gov (United States)

    Liu, Z. H.; Pan, C. T.; Yen, C. K.; Lin, L. W.; Huang, J. C.; Ke, C. A.

    2015-08-01

    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, ultimate tensile strength, and strain at break of the HCNFES composite NFFs are obviously enhanced to 1.39 GPa, 39.6 MPa, 28 MPa, 48.17 MPa, 3.3%, and 32.5%, respectively. Finally, a flexible wearable sensor made of three-dimensional piezoelectric NFFs was actually experimented. Outstanding mechanical properties with highly deformable of PVDF

  19. Ambulatory measurement of three-dimensional foot displacement during treadmill walking using wearable wireless ultrasonic sensor network.

    Science.gov (United States)

    Qi, Yongbin; Soh, Cheong Boon; Gunawan, Erry; Low, Kay-Soon

    2015-03-01

    Techniques that could be used to monitor human motion precisely are helpful in various applications such as rehabilitation, gait analysis, and athletic performance analysis. This paper focuses on the 3-D foot trajectory measurements based on a wearable wireless ultrasonic sensor network. The system consists of an ultrasonic transmitter (mobile) and several receivers (anchors) with fixed known positions. In order not to restrict the movement of subjects, a radio frequency (RF) module is used for wireless data transmission. The RF module also provides the synchronization clock between mobile and anchors. The proposed system measures the time-of-arrival (TOA) of the ultrasonic signal from mobile to anchors. Together with the knowledge of the anchor's position, the absolute distance that the signal travels can be computed. Then, the range information defines a circle centered at this anchor with radius equal to the measured distance, and the mobile resides within the intersections of several such circles. Based on the TOA-based tracking technique, the 3-D foot trajectories are validated against a camera-based motion capture system for ten healthy subjects walking on a treadmill at slow, normal, and fast speeds. The experimental results have shown that the ultrasonic system has sufficient accuracy of net root-mean-square error ( 4.2 cm) for 3-D displacement, especially for foot clearance with accuracy and standard deviation ( 0.62 ±7.48 mm) compared to the camera-based motion capture system. The small form factor and lightweight feature of the proposed system make it easy to use. Such a system is also much lower in cost compared to the camera-based tracking system.

  20. Can a wearable strain sensor based on a carbon nanotube network be an alternative to an isokinetic dynamometer for the measurement of knee-extensor muscle strength?

    Science.gov (United States)

    Benlikaya, Ruhan; Ege, Yavuz; Pündük, Zekine; Slobodian, Petr; Meriç, Gökhan

    2017-04-01

    This study aimed to find out whether a wearable strain sensor including thermoplastic polyurethane composite with a multi-walled carbon nanotube network could be a viable alternative to an isokinetic dynamometer for the measurement of knee-extensor muscle strength. For the first time, the voltage-torque and angle–time relations of the sensor were determined to allow a comparison between the angle-dependent torque changes of the dynamometer and the sensor. This comparison suggested that the torque–angle relations of the dynamometer and the sensor did not have the same characteristics. In this regard, the sensor may be used in the torque measurements due to the moderate correlation between the torque values determined via the isokinetic dynamometer and the sensor and due to the significant difference between low and high torque values of the sensor. By the same token, the torque-angle graph of the sensor may be more informative than that of the dynamometer in evaluation of knee problems.

  1. 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.

  2. Sensing human physiological response using wearable carbon nanotube-based fabrics

    Science.gov (United States)

    Wang, Long; Loh, Kenneth J.; Koo, Helen S.

    2016-04-01

    Flexible and wearable sensors for human monitoring have received increased attention. Besides detecting motion and physical activity, measuring human vital signals (e.g., respiration rate and body temperature) provide rich data for assessing subjects' physiological or psychological condition. Instead of using conventional, bulky, sensing transducers, the objective of this study was to design and test a wearable, fabric-like sensing system. In particular, multi-walled carbon nanotube (MWCNT)-latex thin films of different MWCNT concentrations were first fabricated using spray coating. Freestanding MWCNT-latex films were then sandwiched between two layers of flexible fabric using iron-on adhesive to form the wearable sensor. Second, to characterize its strain sensing properties, the fabric sensors were subjected to uniaxial and cyclic tensile load tests, and they exhibited relatively stable electromechanical responses. Finally, the wearable sensors were placed on a human subject for monitoring simple motions and for validating their practical strain sensing performance. Overall, the wearable fabric sensor design exhibited advances such as flexibility, ease of fabrication, light weight, low cost, noninvasiveness, and user comfort.

  3. 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

    2017-03-02

    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, xx(x), xx-xx.].

  4. Measurement and Geometric Modelling of Human Spine Posture for Medical Rehabilitation Purposes Using a Wearable Monitoring System Based on Inertial Sensors

    Science.gov (United States)

    Voinea, Gheorghe-Daniel; Butnariu, Silviu; Mogan, Gheorghe

    2016-01-01

    This paper presents a mathematical model that can be used to virtually reconstruct the posture of the human spine. By using orientation angles from a wearable monitoring system based on inertial sensors, the model calculates and represents the curvature of the spine. Several hypotheses are taken into consideration to increase the model precision. An estimation of the postures that can be calculated is also presented. A non-invasive solution to identify the human back shape can help reducing the time needed for medical rehabilitation sessions. Moreover, it prevents future problems caused by poor posture. PMID:28025480

  5. Measurement and Geometric Modelling of Human Spine Posture for Medical Rehabilitation Purposes Using a Wearable Monitoring System Based on Inertial Sensors

    Directory of Open Access Journals (Sweden)

    Gheorghe-Daniel Voinea

    2016-12-01

    Full Text Available This paper presents a mathematical model that can be used to virtually reconstruct the posture of the human spine. By using orientation angles from a wearable monitoring system based on inertial sensors, the model calculates and represents the curvature of the spine. Several hypotheses are taken into consideration to increase the model precision. An estimation of the postures that can be calculated is also presented. A non-invasive solution to identify the human back shape can help reducing the time needed for medical rehabilitation sessions. Moreover, it prevents future problems caused by poor posture.

  6. Impact of Wireless Channel Model on 802.15.6 Standard Performance for Wireless Body Sensor Networks

    Directory of Open Access Journals (Sweden)

    Maryam El azhari

    2016-05-01

    Full Text Available Wireless Body Sensor Network (WBAN is a set of wearable and implantable devices capable of measuring physiological parameters and monitoring patient with chronic disease where early diagnosis is highly demanded. Several models introduced the general characterization of WBAN devices path loss considering possible shadowing due to obstruction of the signal (by the human body or any other obstacles as well as the different postures of the human body. This paper aims at reporting an overview of WBSNs technologies, particular applications, system architecture and channel modeling. Emphasis is given to the IEEE 802.15.6 standard which enables the development of WBAN for medical and non-medical applications. The standard's performance within a time based variation and log-distance path loss is presented based on various simulations.

  7. 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

    2013-01-14

    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 433MHz ISM band, called Integrated Posture and Activity NEtwork by Medit Aachen (IPANEMA) BSN. The 433MHz 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%).

  8. 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.

  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.

    2012-01-01

    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. PMID:22875236

  10. 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.

  11. 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.

  12. A wearable wireless ultrasonic sensor network for human arm motion tracking.

    Science.gov (United States)

    Qi, Yongbin; Soh, Cheong Boon; Gunawan, Erry; Low, Kay-Soon

    2014-01-01

    This paper introduces a novel method for arm flexion/extension angles measurement using wireless ultrasonic sensor network. The approach uses unscented Kalman filter and D-H kinematical chain model to retrieve the joint angles. This method was experimentally validated by calculating the 2-dimensional wrist displacements from one mobile, placed on the point of subject's wrist, and four anchors. The performance of the proposed ultrasonic motion analysis system was bench-marked by commercial camera motion capture system. The experimental results demonstrate that a favorable performance of the proposed system in the estimation of upper limb motion. The proposed system is wireless, easy to wear, to use and much cheaper than current camera system. Thus, it has the potential to become a new and useful tool for routine clinical assessment of human motion.

  13. Developing Accessibility Design Guidelines for Wearables: Accessibility Standards for Multimodal Wearable Devices

    NARCIS (Netherlands)

    Wentzel, Jobke; Velleman, Eric M.; Geest, van der 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 co

  14. Wearable feedback systems for rehabilitation

    OpenAIRE

    Marci Carl; Sung Michael; Pentland Alex

    2005-01-01

    Abstract In this paper we describe LiveNet, a flexible wearable platform intended for long-term ambulatory health monitoring with real-time data streaming and context classification. Based on the MIT Wearable Computing Group's distributed mobile system architecture, LiveNet is a stable, accessible system that combines inexpensive, commodity hardware; a flexible sensor/peripheral interconnection bus; and a powerful, light-weight distributed sensing, classification, and inter-process communicat...

  15. Wearable Health Monitoring Systems Project

    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 Health Monitoring Systems Project

    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,...

  17. 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.

  18. A Wearable Contactless Sensor Suitable for Continuous Simultaneous Monitoring of Respiration and Cardiac Activity

    Directory of Open Access Journals (Sweden)

    Gaetano D. Gargiulo

    2015-01-01

    Full Text Available A reliable system that can simultaneously and accurately monitor respiration and cardiac output would have great utility in healthcare applications. In this paper we present a novel approach to creating such a system. This noninvasive, low power, low cost, contactless sensor is suitable for continuous monitoring of respiration (tidal volume and cardiac stroke volume. Furthermore, it is capable of delivering this data in true volume (i.e., mL. The current embodiment, specifically designed for sleep monitoring applications, requires only 100 mW when powered by a 4.8 V battery pack and is based on the use of a single electroresistive band embedded in a T-shirt. Here, we describe the implementation of the device, explaining the rational and design choices for the electronic circuit and the physical garment together with the preliminary tests performed using one volunteer subject. Comparison of the device with a commercially available spirometer demonstrates that tidal volume can be monitored over extended periods with a precision of ±10%. We further demonstrate the utility of the device to measure cardiac output and respiration effort.

  19. Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors

    Directory of Open Access Journals (Sweden)

    Yueng Santiago Delahoz

    2014-10-01

    Full Text Available According to nihseniorhealth.gov (a website for older adults, falling represents a great threat as people get older, and providing mechanisms to detect and prevent falls is critical to improve people’s lives. Over 1.6 million U.S. adults are treated for fall-related injuries in emergency rooms every year suffering fractures, loss of independence, and even death. It is clear then, that this problem must be addressed in a prompt manner, and the use of pervasive computing plays a key role to achieve this. Fall detection (FD and fall prevention (FP are research areas that have been active for over a decade, and they both strive for improving people’s lives through the use of pervasive computing. This paper surveys the state of the art in FD and FP systems, including qualitative comparisons among various studies. It aims to serve as a point of reference for future research on the mentioned systems. A general description of FD and FP systems is provided, including the different types of sensors used in both approaches. Challenges and current solutions are presented and described in great detail. A 3-level taxonomy associated with the risk factors of a fall is proposed. Finally, cutting edge FD and FP systems are thoroughly reviewed and qualitatively compared, in terms of design issues and other parameters.

  20. Semantic Interoperability in Body Area Sensor Networks and Applications

    NARCIS (Netherlands)

    Bui, V.T.; Brandt, P.; Liu, H.; Basten, T.; Lukkien, J.

    2014-01-01

    Crucial to the success of Body Area Sensor Networks is the flexibility with which stakeholders can share, extend and adapt the system with respect to sensors, data and functionality. The first step is to develop an interoperable platform with explicit interfaces, which takes care of common managemen