Wearable Optical Sensors

KAUST Repository

Ballard, Zachary S.; Ozcan, Aydogan

2017-01-01

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

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

Science.gov (United States)

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

2014-10-09

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

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.

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.

Wearable sensors for human health monitoring

Science.gov (United States)

Asada, H. Harry; Reisner, Andrew

2006-03-01

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

Wearable bio and chemical sensors

OpenAIRE

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

2014-01-01

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

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

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.

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

Science.gov (United States)

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

2016-11-10

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

Assessment of Wearable Sensor Technologies for Biosurveillance

Science.gov (United States)

2014-11-01

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

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

Directory of Open Access Journals (Sweden)

Chien-Ming Chen

2018-07-01

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

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.

Smart jacket design for neonatal monitoring with wearable sensors

NARCIS (Netherlands)

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

2009-01-01

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

Wearable and Implantable Wireless Sensor Network Solutions for Healthcare Monitoring

OpenAIRE

Darwish, Ashraf; Hassanien, Aboul Ella

2011-01-01

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

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.  .

Human Activity Recognition from Body Sensor Data using Deep Learning.

Science.gov (United States)

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

2018-04-16

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

Flexible wearable sensor nodes with solar energy harvesting.

Science.gov (United States)

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

2017-07-01

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

Channel Model on Various Frequency Bands for Wearable Body Area Network

Science.gov (United States)

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

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

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

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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.

Wearable Fall Detector using Integrated Sensors and Energy Devices

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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.

Unintended Consequences of Wearable Sensor Use in Healthcare

Science.gov (United States)

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

2016-01-01

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

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

Science.gov (United States)

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

2018-01-01

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

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

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

2017-07-13

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

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

Science.gov (United States)

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

2012-03-01

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

Wearable Sensors; Applications, design and implementation

Science.gov (United States)

Mukhopadhyay, Subhas Chandra; Islam, Tarikul

2017-12-01

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

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.

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

International Nuclear Information System (INIS)

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

2010-01-01

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

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

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Apoorva Gaidhani

2017-12-01

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

A Method of Data Aggregation for Wearable Sensor Systems

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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.

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.

Physical Human Activity Recognition Using Wearable Sensors.

Science.gov (United States)

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

2015-12-11

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

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.

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

Directory of Open Access Journals (Sweden)

Chao Li

2017-12-01

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

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

International Nuclear Information System (INIS)

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

2017-01-01

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

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

Flexible Graphene-Based Wearable Gas and Chemical Sensors.

Science.gov (United States)

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

2017-10-11

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

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

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Amir Mehmood

2017-06-01

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

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

Science.gov (United States)

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

2018-05-01

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

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

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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.

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. .

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

OpenAIRE

Cook, Diane J.; Song, WenZhan

2009-01-01

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

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.

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

Science.gov (United States)

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

2017-11-01

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

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.

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.

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.

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

Science.gov (United States)

Seesaard, Thara; Lorwongtragool, Panida; Kerdcharoen, Teerakiat

2015-01-16

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

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.

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

Science.gov (United States)

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

2017-07-19

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

Emotion and Cognitive Reappraisal Based on GSR Wearable Sensor

Institute of Scientific and Technical Information of China (English)

LI Minjia; XIE Lun; WANG Zhiliang; REN Fuji

2017-01-01

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

Wearable PPG sensor based alertness scoring system.

Science.gov (United States)

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

2017-07-01

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

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.

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

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

Science.gov (United States)

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

2018-03-13

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

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.

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

Science.gov (United States)

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

2018-04-01

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

A Printed Organic Amplification System for Wearable Potentiometric Electrochemical Sensors.

Science.gov (United States)

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

2018-03-02

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

Ultra-low power and wearable CO2 sensors

Data.gov (United States)

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

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.

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

Science.gov (United States)

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

2017-11-30

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

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.

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

Directory of Open Access Journals (Sweden)

Changyong Liu

2018-06-01

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

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

Directory of Open Access Journals (Sweden)

Shao-Hui Zhang

2017-11-01

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

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

Science.gov (United States)

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

2017-08-01

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

Wearable and flexible electronics for continuous molecular monitoring.

Science.gov (United States)

Yang, Yiran; Gao, Wei

2018-04-03

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

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

Science.gov (United States)

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

2018-04-03

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

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

Science.gov (United States)

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

2017-03-24

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

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

Directory of Open Access Journals (Sweden)

Jing Chen

2018-02-01

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

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

KAUST Repository

Zhou, Jian

2018-01-22

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

Flexible heartbeat sensor for wearable device.

Science.gov (United States)

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

2017-08-15

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

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

KAUST Repository

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

2015-01-01

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

A Printed Organic Circuit System for Wearable Amperometric Electrochemical Sensors.

Science.gov (United States)

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

2018-04-23

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

Carbon Nanotube-Based Ion Selective Sensors for Wearable Applications.

Science.gov (United States)

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

2017-10-11

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

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

Science.gov (United States)

Lee, Youngbum; Lee, Byungwoo; Lee, Myoungho

2010-03-01

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

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

NARCIS (Netherlands)

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

2017-01-01

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

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.

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.

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

Science.gov (United States)

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

2016-08-01

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

A flexible wearable sensor for knee flexion assessment during gait.

Science.gov (United States)

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

2018-05-01

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

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

Science.gov (United States)

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

2018-04-01

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

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

Science.gov (United States)

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

2015-04-01

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

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

Science.gov (United States)

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

2018-05-01

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

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...

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

DEFF Research Database (Denmark)

Wang, Liang; Gu, Tao; Tao, Xianping

2010-01-01

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

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

Science.gov (United States)

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

2017-11-23

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

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.

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.

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.

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.

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.

Monitoring of Vital Signs with Flexible and Wearable Medical Devices.

Science.gov (United States)

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

2016-06-01

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

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

Directory of Open Access Journals (Sweden)

Carlos Gonçalves

2018-03-01

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

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

Science.gov (United States)

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

2017-09-01

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

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

Science.gov (United States)

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

2017-01-01

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

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

Wearable sensor system for human localization and motion capture

OpenAIRE

Zihajehzadeh, Shaghayegh

2017-01-01

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

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.

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

Science.gov (United States)

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

2018-01-01

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

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.

Wearable Gait Measurement System with an Instrumented Cane for Exoskeleton Control

Directory of Open Access Journals (Sweden)

Modar Hassan

2014-01-01

Full Text Available In this research we introduce a wearable sensory system for motion intention estimation and control of exoskeleton robot. The system comprises wearable inertial motion sensors and shoe-embedded force sensors. The system utilizes an instrumented cane as a part of the interface between the user and the robot. The cane reflects the motion of upper limbs, and is used in terms of human inter-limb synergies. The developed control system provides assisted motion in coherence with the motion of other unassisted limbs. The system utilizes the instrumented cane together with body worn sensors, and provides assistance for start, stop and continuous walking. We verified the function of the proposed method and the developed wearable system through gait trials on treadmill and on ground. The achievement contributes to finding an intuitive and feasible interface between human and robot through wearable gait sensors for practical use of assistive technology. It also contributes to the technology for cognitively assisted locomotion, which helps the locomotion of physically challenged people.

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.

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

NARCIS (Netherlands)

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

2017-01-01

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

Wearable technology: role in respiratory health and disease

OpenAIRE

Aliverti, Andrea

2017-01-01

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

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

Science.gov (United States)

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

2017-08-01

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

Wireless body sensor networks for health-monitoring applications

International Nuclear Information System (INIS)

Hao, Yang; Foster, Robert

2008-01-01

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

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.

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

Directory of Open Access Journals (Sweden)

Kee-Hoon Kim

2017-12-01

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

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

Science.gov (United States)

Kim, Kee-Hoon; Cho, Sung-Bae

2017-12-11

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

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

Science.gov (United States)

Kim, Kee-Hoon

2017-01-01

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

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.

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

Science.gov (United States)

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

2018-03-23

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

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

Science.gov (United States)

Dobkin, Bruce H

2013-12-01

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

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.

Wearable technology: role in respiratory health and disease.

Science.gov (United States)

Aliverti, Andrea

2017-06-01

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

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.

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

Science.gov (United States)

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

2004-01-01

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

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

Science.gov (United States)

Ostaszewski, Michal; Pauk, Jolanta

2018-05-16

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

Wearable Performance Devices in Sports Medicine.

Science.gov (United States)

Li, Ryan T; Kling, Scott R; Salata, Michael J; Cupp, Sean A; Sheehan, Joseph; Voos, James E

2016-01-01

Wearable performance devices and sensors are becoming more readily available to the general population and athletic teams. Advances in technology have allowed individual endurance athletes, sports teams, and physicians to monitor functional movements, workloads, and biometric markers to maximize performance and minimize injury. Movement sensors include pedometers, accelerometers/gyroscopes, and global positioning satellite (GPS) devices. Physiologic sensors include heart rate monitors, sleep monitors, temperature sensors, and integrated sensors. The purpose of this review is to familiarize health care professionals and team physicians with the various available types of wearable sensors, discuss their current utilization, and present future applications in sports medicine. Data were obtained from peer-reviewed literature through a search of the PubMed database. Included studies searched development, outcomes, and validation of wearable performance devices such as GPS, accelerometers, and physiologic monitors in sports. Clinical review. Level 4. Wearable sensors provide a method of monitoring real-time physiologic and movement parameters during training and competitive sports. These parameters can be used to detect position-specific patterns in movement, design more efficient sports-specific training programs for performance optimization, and screen for potential causes of injury. More recent advances in movement sensors have improved accuracy in detecting high-acceleration movements during competitive sports. Wearable devices are valuable instruments for the improvement of sports performance. Evidence for use of these devices in professional sports is still limited. Future developments are needed to establish training protocols using data from wearable devices. © 2015 The Author(s).

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

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

2018-04-01

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

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

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Ozgur Atalay

2018-05-01

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

Convergence Revolution Comes to Wearables: Multiple Advances are Taking Biosensor Networks to the Next Level in Health Care.

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Mertz, Leslie

2016-01-01

In the field of wearable biomedical sensors, the convergence revolution is more than a fanciful, utopian view of the way innovation should be done. Medical-grade wearable sensors rely on it. Their development requires technical know-how, computing expertise, clinical input, and collaboration-a true meeting of the minds to permit the conversion of wearables from neat gadgets into practical and proficient tools that will propel health care to new heights. Beyond the increasing miniaturization of hardware and the shift to wireless communication technology, flexible electronics and more powerful computing capabilities, including application specific integrated circuits (ASICs) and microelectromechanical systems (MEMS), have enabled new work on body sensor networks (BSNs) that monitor, analyze, and make sense of body signals for the prevention, diagnosis, and treatment of health disorders. Developments in processing, such as sensor-connected nodes combined with evolving algorithms and decreasing power requirements, have also contributed. In addition, new approaches to subjective measures (pain and emotion) have opened possibilities.

Wearable Computing in E-education

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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.

The application of wearable technology in surgery: ensuring the positive impact of the wearable revolution on surgical patients.

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Slade Shantz, Jesse Alan; Veillette, Christian J H

2014-01-01

Wearable technology has become an important trend in consumer electronics in the past year. The miniaturization and mass production of myriad sensors have made possible the integration of sensors and output devices in wearable platforms. Despite the consumer focus of the wearable revolution some surgical applications are being developed. These fall into augmentative, assistive, and assessment functions and primarily layer onto current surgical workflows. Some challenges to the adoption of wearable technologies are discussed and a conceptual framework for understanding the potential of wearable technology to revolutionize surgical practice are presented.

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

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Jennifer Howcroft

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

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

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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.

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

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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.

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

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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

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

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Michard, Frederic

2017-04-01

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

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

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

2015-01-01

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

Dealing with the Effects of Sensor Displacement in Wearable Activity Recognition

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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.

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

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

2018-01-01

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

Wearable carbon nanotube based dry-electrodes for electrophysiological sensors

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Kang, Byeong-Cheol; Ha, Tae-Jun

2018-05-01

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

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

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

2015-06-01

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

Loose Coupling of Wearable-Based INSs with Automatic Heading Evaluation

OpenAIRE

Bousdar Ahmed, Dina; Munoz Diaz, Estefania

2017-01-01

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

Recent Progress of Self-Powered Sensing Systems for Wearable Electronics.

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Lou, Zheng; Li, La; Wang, Lili; Shen, Guozhen

2017-12-01

Wearable/flexible electronic sensing systems are considered to be one of the key technologies in the next generation of smart personal electronics. To realize personal portable devices with mobile electronics application, i.e., wearable electronic sensors that can work sustainably and continuously without an external power supply are highly desired. The recent progress and advantages of wearable self-powered electronic sensing systems for mobile or personal attachable health monitoring applications are presented. An overview of various types of wearable electronic sensors, including flexible tactile sensors, wearable image sensor array, biological and chemical sensor, temperature sensors, and multifunctional integrated sensing systems is provided. Self-powered sensing systems with integrated energy units are then discussed, separated as energy harvesting self-powered sensing systems, energy storage integrated sensing systems, and all-in-on integrated sensing systems. Finally, the future perspectives of self-powered sensing systems for wearable electronics are discussed. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Wearable Sweat Rate Sensors for Human Thermal Comfort Monitoring.

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

2018-01-19

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

Wearable physiological systems and technologies for metabolic monitoring.

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Gao, Wei; Brooks, George A; Klonoff, David C

2018-03-01

Wearable sensors allow continuous monitoring of metabolites for diabetes, sports medicine, exercise science, and physiology research. These sensors can continuously detect target analytes in skin interstitial fluid (ISF), tears, saliva, and sweat. In this review, we will summarize developments on wearable devices and their potential applications in research, clinical practice, and recreational and sporting activities. Sampling skin ISF can require insertion of a needle into the skin, whereas sweat, tears, and saliva can be sampled by devices worn outside the body. The most widely sampled metabolite from a wearable device is glucose in skin ISF for monitoring diabetes patients. Continuous ISF glucose monitoring allows estimation of the glucose concentration in blood without the pain, inconvenience, and blood waste of fingerstick capillary blood glucose testing. This tool is currently used by diabetes patients to provide information for dosing insulin and determining a diet and exercise plan. Similar technologies for measuring concentrations of other analytes in skin ISF could be used to monitor athletes, emergency responders, warfighters, and others in states of extreme physiological stress. Sweat is a potentially useful substrate for sampling analytes for metabolic monitoring during exercise. Lactate, sodium, potassium, and hydrogen ions can be measured in sweat. Tools for converting the concentrations of these analytes sampled from sweat, tears, and saliva into blood concentrations are being developed. As an understanding of the relationships between the concentrations of analytes in blood and easily sampled body fluid increases, then the benefits of new wearable devices for metabolic monitoring will also increase.

Gait Kinematic Analysis in Water Using Wearable Inertial Magnetic Sensors.

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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

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

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

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

2018-03-25

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

Inkjet-/3D-/4D-printed autonomous wearable RF modules for biomonitoring, positioning and sensing applications

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Bito, Jo; Bahr, Ryan; Hester, Jimmy; Kimionis, John; Nauroze, Abdullah; Su, Wenjing; Tehrani, Bijan; Tentzeris, Manos M.

2017-05-01

In this paper, numerous inkjet-/3D-/4D-printed wearable flexible antennas, RF electronics, modules and sensors fabricated on paper and other polymer (e.g. LCP) substrates are introduced as a system-level solution for ultra-low-cost mass production of autonomous Biomonitoring, Positioning and Sensing applications. This paper briefly discusses the state-of-the-art area of fully-integrated wearable wireless sensor modules on paper or flexible LCP and show the first ever 4D sensor module integration on paper, as well as numerous 3D and 4D multilayer paper-based and LCP-based RF/microwave, flexible and wearable structures, that could potentially set the foundation for the truly convergent wireless sensor ad-hoc "on-body networks of the future with enhanced cognitive intelligence and "rugged" packaging. Also, some challenges concerning the power sources of "nearperpetual" wearable RF modules, including flexible miniaturized batteries as well as power-scavenging approaches involving electromagnetic and solar energy forms are discuessed. The final step of the paper will involve examples from mmW wearable (e.g. biomonitoring) antennas and RF modules, as well as the first examples of the integration of inkjet-printed nanotechnology-based (e.g.CNT) sensors on paper and organic substrates for Internet of Things (IoT) applications. It has to be noted that the paper will review and present challenges for inkjetprinted organic active and nonlinear devices as well as future directions in the area of environmentally-friendly "green") wearable RF electronics and "smart-skin conformal sensors.

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

Science.gov (United States)

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

2015-02-01

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

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...

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

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

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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.

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

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

2018-05-16

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

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

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Ramasamy, Mouli; Varadan, Vijay K.

2017-04-01

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

CCS_WHMS: A Congestion Control Scheme for Wearable Health Management System.

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Kafi, Mohamed Amine; Ben Othman, Jalel; Bagaa, Miloud; Badache, Nadjib

2015-12-01

Wearable computing is becoming a more and more attracting field in the last years thanks to the miniaturisation of electronic devices. Wearable healthcare monitoring systems (WHMS) as an important client of wearable computing technology has gained a lot. Indeed, the wearable sensors and their surrounding healthcare applications bring a lot of benefits to patients, elderly people and medical staff, so facilitating their daily life quality. But from a research point of view, there is still work to accomplish in order to overcome the gap between hardware and software parts. In this paper, we target the problem of congestion control when all these healthcare sensed data have to reach the destination in a reliable manner that avoids repetitive transmission which wastes precious energy or leads to loss of important information in emergency cases, too. We propose a congestion control scheme CCS_WHMS that ensures efficient and fair data delivery while used in the body wearable system part or in the multi-hop inter bodies wearable ones to get the destination. As the congestion detection paradigm is very important in the control process, we do experimental tests to compare between state of the art congestion detection methods, using MICAz motes, in order to choose the appropriate one for our scheme.

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

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

2018-02-01

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

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.

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

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Rovini, Erika; Maremmani, Carlo; Cavallo, Filippo

2017-01-01

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

Flexible Sensing Electronics for Wearable/Attachable Health Monitoring.

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Wang, Xuewen; Liu, Zheng; Zhang, Ting

2017-07-01

Wearable or attachable health monitoring smart systems are considered to be the next generation of personal portable devices for remote medicine practices. Smart flexible sensing electronics are components crucial in endowing health monitoring systems with the capability of real-time tracking of physiological signals. These signals are closely associated with body conditions, such as heart rate, wrist pulse, body temperature, blood/intraocular pressure and blood/sweat bio-information. Monitoring such physiological signals provides a convenient and non-invasive way for disease diagnoses and health assessments. This Review summarizes the recent progress of flexible sensing electronics for their use in wearable/attachable health monitoring systems. Meanwhile, we present an overview of different materials and configurations for flexible sensors, including piezo-resistive, piezo-electrical, capacitive, and field effect transistor based devices, and analyze the working principles in monitoring physiological signals. In addition, the future perspectives of wearable healthcare systems and the technical demands on their commercialization are briefly discussed. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

Directory of Open Access Journals (Sweden)

Taehwan Kim

2017-05-01

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

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

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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

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

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Ray, Partha Pratim

2017-08-01

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

Wearable Photoplethysmographic Sensors—Past and Present

Directory of Open Access Journals (Sweden)

Toshiyo Tamura

2014-04-01

Full Text Available Photoplethysmography (PPG technology has been used to develop small, wearable, pulse rate sensors. These devices, consisting of infrared light-emitting diodes (LEDs and photodetectors, offer a simple, reliable, low-cost means of monitoring the pulse rate noninvasively. Recent advances in optical technology have facilitated the use of high-intensity green LEDs for PPG, increasing the adoption of this measurement technique. In this review, we briefly present the history of PPG and recent developments in wearable pulse rate sensors with green LEDs. The application of wearable pulse rate monitors is discussed.

Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care

Directory of Open Access Journals (Sweden)

Jose-Luis Bayo-Monton

2018-06-01

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

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

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

2017-01-01

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

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

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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.

Wearable flex sensor system for multiple badminton player grip identification

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

2017-09-01

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

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

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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.

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

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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.

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

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Wentzel, M.J.; Velleman, Eric M.; van der Geest, Thea; Antona, Margherita; Stephanidis, Constantine

2016-01-01

Smart wearable devices are integrated our everyday lives. Such wearable technology is worn on or near the body, while leaving both hands free. This enables users to receive and send information in a non-obtrusive way. Because of the ability to continuously assist and support activities, wearables

Tissue viability monitoring: a multi-sensor wearable platform approach

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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.

Distinguishing the causes of falls in humans using an array of wearable tri-axial accelerometers.

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Aziz, Omar; Park, Edward J; Mori, Greg; Robinovitch, Stephen N

2014-01-01

Falls are the number one cause of injury in older adults. Lack of objective evidence on the cause and circumstances of falls is often a barrier to effective prevention strategies. Previous studies have established the ability of wearable miniature inertial sensors (accelerometers and gyroscopes) to automatically detect falls, for the purpose of delivering medical assistance. In the current study, we extend the applications of this technology, by developing and evaluating the accuracy of wearable sensor systems for determining the cause of falls. Twelve young adults participated in experimental trials involving falls due to seven causes: slips, trips, fainting, and incorrect shifting/transfer of body weight while sitting down, standing up from sitting, reaching and turning. Features (means and variances) of acceleration data acquired from four tri-axial accelerometers during the falling trials were input to a linear discriminant analysis technique. Data from an array of three sensors (left ankle+right ankle+sternum) provided at least 83% sensitivity and 89% specificity in classifying falls due to slips, trips, and incorrect shift of body weight during sitting, reaching and turning. Classification of falls due to fainting and incorrect shift during rising was less successful across all sensor combinations. Furthermore, similar classification accuracy was observed with data from wearable sensors and a video-based motion analysis system. These results establish a basis for the development of sensor-based fall monitoring systems that provide information on the cause and circumstances of falls, to direct fall prevention strategies at a patient or population level. Copyright © 2013 Elsevier B.V. All rights reserved.

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

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

2016-01-01

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

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

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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.

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

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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.

NASA Wearable Technology CLUSTER 2013-2014 Report

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Simon, Cory; Dunne, Lucy; Zeagler, Clint; Martin, Tom; Pailes-Friedman, Rebecca

2014-01-01

Wearable technology has the potential to revolutionize the way humans interact with one another, with information, and with the electronic systems that surround them. This change can already be seen in the dramatic increase in the availability and use of wearable health and activity monitors. These devices continuously monitor the wearer using on-­-body sensors and wireless communication. They provide feedback that can be used to improve physical health and performance. Smart watches and head mounted displays are also receiving a great deal of commercial attention, providing immediate access to information via graphical displays, as well as additional sensing features. For the purposes of the Wearable Technology CLUSTER, wearable technology is broadly defined as any electronic sensing, human interfaces, computing, or communication that is mounted on the body. Current commercially available wearable devices primarily house electronics in rigid packaging to provide protection from flexing, moisture, and other contaminants. NASA mentors are interested in this approach, but are also interested in direct integration of electronics into clothing to enable more comfortable systems. For human spaceflight, wearable technology holds a great deal of promise for significantly improving safety, efficiency, autonomy, and research capacity for the crew in space and support personnel on the ground. Specific capabilities of interest include: Continuous biomedical monitoring for research and detection of health problems. Environmental monitoring for individual exposure assessments and alarms. Activity monitoring for responsive robotics and environments. Multi-modal caution and warning using tactile, auditory, and visual alarms. Wireless, hands-free, on-demand voice communication. Mobile, on-demand access to space vehicle and robotic displays and controls. Many technical challenges must be overcome to realize these wearable technology applications. For example, to make a wearable

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

Directory of Open Access Journals (Sweden)

Erika Rovini

2017-10-01

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

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.

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

Science.gov (United States)

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

2017-11-09

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

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.

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

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

2017-12-01

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

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

Science.gov (United States)

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

2017-12-06

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

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.

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

Science.gov (United States)

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

2011-01-01

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

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

OpenAIRE

Suin Kim; Kyongkwan Ro; Joonbum Bae

2017-01-01

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

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

Science.gov (United States)

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

2017-12-01

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

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

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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.

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

Science.gov (United States)

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

2018-04-30

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

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

OpenAIRE

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

2016-01-01

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

Wearable ballistocardiogram and seismocardiogram systems for health and performance.

Science.gov (United States)

Etemadi, Mozziyar; Inan, Omer T

2018-02-01

Cardiovascular diseases (CVDs) are prevalent in the US, and many forms of CVD primarily affect the mechanical aspects of heart function. Wearable technologies for monitoring the mechanical health of the heart and vasculature could enable proactive management of CVDs through titration of care based on physiological status as well as preventative wellness monitoring to help promote lifestyle choices that reduce the overall risk of developing CVDs. Additionally, such wearable technologies could be used to optimize human performance in austere environments. This review describes our progress in developing wearable ballistocardiogram (BCG)- and seismocardiogram-based systems for monitoring relative changes in cardiac output, contractility, and blood pressure. Our systems use miniature, low-noise accelerometers to measure the movements of the body in response to the heartbeat and novel machine learning algorithms to provide robustness against motion artifacts and sensor misplacement. Moreover, we have mathematically related wearable BCG signals-representing local, cardiogenic movements of a point on the body-to better understood whole body BCG signals, and thereby improved estimation of key health parameters. We validated these systems with experiments in healthy subjects, studies in patients with heart failure, and measurements in austere environments such as water immersion. The systems can be used in future work as a tool for clinicians and physiologists to measure the mechanical aspects of cardiovascular function outside of clinical settings, and to thereby titrate care for patients with CVDs, provide preventative screening, and optimize performance in austere environments by providing real-time in-depth information regarding performance and risk.

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

Design a Wearable Device for Blood Oxygen Concentration and Temporal Heart Beat Rate

Science.gov (United States)

Myint, Cho Zin; Barsoum, Nader; Ing, Wong Kiing

2010-06-01

The wireless network technology is increasingly important in healthcare as a result of the aging population and the tendency to acquire chronic disease such as heart attack, high blood pressure amongst the elderly. A wireless sensor network system that has the capability to monitor physiological sign such as SpO2 (Saturation of Arterial Oxygen) and heart beat rate in real-time from the human's body is highlighted in this study. This research is to design a prototype sensor network hardware, which consists of microcontroller PIC18F series and transceiver unit. The sensor is corporate into a wearable body sensor network which is small in size and easy to use. The sensor allows a non invasive, real time method to provide information regarding the health of the body. This enables a more efficient and economical means for managing the health care of the population.

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

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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.

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

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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.

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

Directory of Open Access Journals (Sweden)

Ken-ichi Nomura

2018-01-01

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

Medication Adherence using Non-intrusive Wearable Sensors

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T. H. Lim

2017-12-01

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

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.

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.

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

Science.gov (United States)

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

2018-01-01

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

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.

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

Science.gov (United States)

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

2016-03-21

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

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

Science.gov (United States)

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

2017-03-07

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

Electromagnetics of body area networks antennas, propagation, and RF systems

CERN Document Server

Werner, Douglas H

2016-01-01

The book is a comprehensive treatment of the field, covering fundamental theoretical principles and new technological advancements, state-of-the-art device design, and reviewing examples encompassing a wide range of related sub-areas. In particular, the first area focuses on the recent development of novel wearable and implantable antenna concepts and designs including metamaterial-based wearable antennas, microwave circuit integrated wearable filtering antennas, and textile and/or fabric material enabled wearable antennas. The second set of topics covers advanced wireless propagation and the associated statistical models for on-body, in-body, and off-body modes. Other sub-areas such as efficient numerical human body modeling techniques, artificial phantom synthesis and fabrication, as well as low-power RF integrated circuits and related sensor technology are also discussed. These topics have been carefully selected for their transformational impact on the next generation of body-area network systems and beyo...

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.

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.

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....

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.

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

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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.

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.

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

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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.

Wearable Fall Detector using Integrated Sensors and Energy Devices

OpenAIRE

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

2015-01-01

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

Wireless Power Transfer for Autonomous Wearable Neurotransmitter Sensors.

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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.

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.

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

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

2016-07-01

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

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

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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.

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.

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

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

2017-04-01

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

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

Directory of Open Access Journals (Sweden)

Priyanka Kakria

2015-01-01

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

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

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

2015-01-01

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

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

Directory of Open Access Journals (Sweden)

Thijs Castel

2013-01-01

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

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

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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.

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

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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.

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

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

2016-03-15

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

Toward a Fault Tolerant Architecture for Vital Medical-Based Wearable Computing.

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Abdali-Mohammadi, Fardin; Bajalan, Vahid; Fathi, Abdolhossein

2015-12-01

Advancements in computers and electronic technologies have led to the emergence of a new generation of efficient small intelligent systems. The products of such technologies might include Smartphones and wearable devices, which have attracted the attention of medical applications. These products are used less in critical medical applications because of their resource constraint and failure sensitivity. This is due to the fact that without safety considerations, small-integrated hardware will endanger patients' lives. Therefore, proposing some principals is required to construct wearable systems in healthcare so that the existing concerns are dealt with. Accordingly, this paper proposes an architecture for constructing wearable systems in critical medical applications. The proposed architecture is a three-tier one, supporting data flow from body sensors to cloud. The tiers of this architecture include wearable computers, mobile computing, and mobile cloud computing. One of the features of this architecture is its high possible fault tolerance due to the nature of its components. Moreover, the required protocols are presented to coordinate the components of this architecture. Finally, the reliability of this architecture is assessed by simulating the architecture and its components, and other aspects of the proposed architecture are discussed.

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

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Shen, John; Naeim, Arash

2017-11-01

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

The Baetylus Theorem?the central disconnect driving consumer behavior and investment returns in Wearable Technologies

OpenAIRE

Levine, James A.

2016-01-01

The Wearable Technology market may increase fivefold by the end of the decade. There is almost no academic investigation as to what drives the investment hypothesis in wearable technologies. This paper seeks to examine this issue from an evidence-based perspective. There is a fundamental disconnect in how consumers view wearable sensors and how companies market them; this is called The Baetylus Theorem where people believe (falsely) that by buying a wearable sensor they will receive health be...

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

Directory of Open Access Journals (Sweden)

Dhafer Ben Arbia

2017-07-01

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

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

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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.

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.

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.

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

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

2017-12-01

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

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

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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.

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

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

2017-12-01

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

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

OpenAIRE

Casson, Alexander J.

2015-01-01

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

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

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

2017-07-01

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

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

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

2017-09-22

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

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.

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

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Woosuk Kim

2018-03-01

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

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

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Kim, Woosuk; Kim, Myunggyu

2018-03-19

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

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

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

2018-04-01

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

Interactive balance training integrating sensor-based visual feedback of movement performance: a pilot study in older adults

OpenAIRE

Schwenk, Michael; Grewal, Gurtej S; Honarvar, Bahareh; Schwenk, Stefanie; Mohler, Jane; Khalsa, Dharma S; Najafi, Bijan

2014-01-01

Background Wearable sensor technology can accurately measure body motion and provide incentive feedback during exercising. The aim of this pilot study was to evaluate the effectiveness and user experience of a balance training program in older adults integrating data from wearable sensors into a human-computer interface designed for interactive training. Methods Senior living community residents (mean age 84.6) with confirmed fall risk were randomized to an intervention (IG, n?=?17) or contro...

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

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

2017-08-02

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

Smart wearable systems: current status and future challenges.

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Chan, Marie; Estève, Daniel; Fourniols, Jean-Yves; Escriba, Christophe; Campo, Eric

2012-11-01

Extensive efforts have been made in both academia and industry in the research and development of smart wearable systems (SWS) for health monitoring (HM). Primarily influenced by skyrocketing healthcare costs and supported by recent technological advances in micro- and nanotechnologies, miniaturisation of sensors, and smart fabrics, the continuous advances in SWS will progressively change the landscape of healthcare by allowing individual management and continuous monitoring of a patient's health status. Consisting of various components and devices, ranging from sensors and actuators to multimedia devices, these systems support complex healthcare applications and enable low-cost wearable, non-invasive alternatives for continuous 24-h monitoring of health, activity, mobility, and mental status, both indoors and outdoors. Our objective has been to examine the current research in wearable to serve as references for researchers and provide perspectives for future research. Herein, we review the current research and development of and the challenges facing SWS for HM, focusing on multi-parameter physiological sensor systems and activity and mobility measurement system designs that reliably measure mobility or vital signs and integrate real-time decision support processing for disease prevention, symptom detection, and diagnosis. For this literature review, we have chosen specific selection criteria to include papers in which wearable systems or devices are covered. We describe the state of the art in SWS and provide a survey of recent implementations of wearable health-care systems. We describe current issues, challenges, and prospects of SWS. We conclude by identifying the future challenges facing SWS for HM. Copyright © 2012 Elsevier B.V. All rights reserved.

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

Science.gov (United States)

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

2017-01-01

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

Soft Sensors and Actuators based on Nanomaterials

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Yao, Shanshan

The focus of this research is using novel bottom-up synthesized nanomaterials and structures to build up devices for wearable sensors and soft actuators. The applications of the wearable sensors towards motion detection and health monitoring are investigated. In addition, flexible heaters for bimorph actuators and stretchable patches made of microgel depots containing drug-loaded nanoparticles (NPs) for stretch-triggered wearable drug delivery are studied. Considerable efforts have been made to achieve highly sensitive and wearable sensors that can simultaneously detect multiple stimuli such as stretch, pressure, temperature or touch. Highly stretchable multifunctional sensors that can detect strain (up to 50%), pressure (up to 1 MPa) and finger touch with good sensitivity, fast response time ( 40 ms) and good pressure mapping function were developed. The sensors were demonstrated for several wearable applications including monitoring thumb movements and knee motions, illustrating the potential utilities of such sensors in robotic systems, prosthetics, healthcare and flexible touch panels. In addition to mechanical sensors, a wearable skin hydration sensor made of silver nanowires (AgNWs) in a polydimethylsiloxane (PDMS) matrix was demonstrated based on skin impedance measurement. The hydration sensors were packaged into a flexible wristband for skin hydration monitoring and a chest patch consisting of a strain sensor, three electrocardiogram (ECG) electrodes and a skin hydration sensor for multimodal sensing. The wearable wristband and chest patch may be used for low-cost, wireless and continuous sensing of skin hydration and other health parameters. Two representative applications of the nanomaterials for soft actuators were investigated. In the first application on bimorph actuation, low-voltage and extremely flexible electrothermal bimorph actuators were fabricated in a simple, efficient and scalable process. The bimorph actuators were made of flexible Ag

Interconnecting wearable devices with nano-biosensing implants through optical wireless communications

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Johari, Pedram; Pandey, Honey; Jornet, Josep M.

2018-02-01

Major advancements in the fields of electronics, photonics and wireless communication have enabled the development of compact wearable devices, with applications in diverse domains such as fitness, wellness and medicine. In parallel, nanotechnology is enabling the development of miniature sensors that can detect events at the nanoscale with unprecedented accuracy. On this matter, in vivo implantable Surface Plasmon Resonance (SPR) nanosensors have been proposed to analyze circulating biomarkers in body fluids for the early diagnosis of a myriad of diseases, ranging from cardiovascular disorders to different types of cancer. In light of these results, in this paper, an architecture is proposed to bridge the gap between these two apparently disjoint paradigms, namely, the commercial wearable devices and the advanced nano-biosensing technologies. More specifically, this paper thoroughly assesses the feasibility of the wireless optical intercommunications of an SPR-based nanoplasmonic biochip -implanted subcutaneously in the wrist-, with a nanophotonic wearable smart band which is integrated by an array of nano-lasers and photon-detectors for distributed excitation and measurement of the nanoplasmonic biochip. This is done through a link budget analysis which captures the peculiarities of the intra-body optical channel at (sub) cellular level, the strength of the SPR nanosensor reflection, as well as the capabilities of the nanolasers (emission power, spectrum) and the nano photon-detectors (sensitivity and noise equivalent power). The proposed analysis guides the development of practical communication designs between the wearable devices and nano-biosensing implants, which paves the way through early-stage diagnosis of severe diseases.

A wearable point-of-care system for home use that incorporates plug-and-play and wireless standards.

Science.gov (United States)

Yao, Jianchu; Schmitz, Ryan; Warren, Steve

2005-09-01

A point-of-care system for continuous health monitoring should be wearable, easy to use, and affordable to promote patient independence and facilitate acceptance of new home healthcare technology. Reconfigurability, interoperability, and scalability are important. Standardization supports these requirements, and encourages an open market where lower product prices result from vendor competition. This paper first discusses candidate standards for wireless communication, plug-and-play device interoperability, and medical information exchange in point-of-care systems. It then addresses the design and implementation of a wearable, plug-and-play system for home care which adopts the IEEE 1073 Medical Information Bus (MIB) standards, and uses Bluetooth as the wireless communication protocol. This standards-based system maximizes user mobility by incorporating a three-level architecture populated by base stations, wearable data loggers, and wearable sensors. Design issues include the implementation of the MIB standards on microcontroller-driven embedded devices, low power consumption, wireless data exchange, and data storage and transmission in a reconfigurable body-area network.

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

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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

Performance of a wearable acoustic system for fetal movement discrimination.

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Jonathan Lai

Full Text Available Fetal movements (FM are a key factor in clinical management of high-risk pregnancies such as fetal growth restriction. While maternal perception of reduced FM can trigger self-referral to obstetric services, maternal sensation is highly subjective. Objective, reliable monitoring of fetal movement patterns outside clinical environs is not currently possible. A wearable and non-transmitting system capable of sensing fetal movements over extended periods of time would be extremely valuable, not only for monitoring individual fetal health, but also for establishing normal levels of movement in the population at large. Wearable monitors based on accelerometers have previously been proposed as a means of tracking FM, but such systems have difficulty separating maternal and fetal activity and have not matured to the level of clinical use. We introduce a new wearable system based on a novel combination of accelerometers and bespoke acoustic sensors as well as an advanced signal processing architecture to identify and discriminate between types of fetal movements. We validate the system with concurrent ultrasound tests on a cohort of 44 pregnant women and demonstrate that the garment is capable of both detecting and discriminating the vigorous, whole-body 'startle' movements of a fetus. These results demonstrate the promise of multimodal sensing for the development of a low-cost, non-transmitting wearable monitor for fetal movements.

Flexible Nanowire Cluster as a Wearable Colorimetric Humidity Sensor.

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

2017-07-01

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

Wearable technology for spine movement assessment: A systematic review.

Science.gov (United States)

Papi, Enrica; Koh, Woon Senn; McGregor, Alison H

2017-11-07

Continuous monitoring of spine movement function could enhance our understanding of low back pain development. Wearable technologies have gained popularity as promising alternative to laboratory systems in allowing ambulatory movement analysis. This paper aims to review the state of art of current use of wearable technology to assess spine kinematics and kinetics. Four electronic databases and reference lists of relevant articles were searched to find studies employing wearable technologies to assess the spine in adults performing dynamic movements. Two reviewers independently identified relevant papers. Customised data extraction and quality appraisal form were developed to extrapolate key details and identify risk of biases of each study. Twenty-two articles were retrieved that met the inclusion criteria: 12 were deemed of medium quality (score 33.4-66.7%), and 10 of high quality (score >66.8%). The majority of articles (19/22) reported validation type studies. Only 6 reported data collection in real-life environments. Multiple sensors type were used: electrogoniometers (3/22), strain gauges based sensors (3/22), textile piezoresistive sensor (1/22) and accelerometers often used with gyroscopes and magnetometers (15/22). Two sensors units were mainly used and placing was commonly reported on the spine lumbar and sacral regions. The sensors were often wired to data transmitter/logger resulting in cumbersome systems. Outcomes were mostly reported relative to the lumbar segment and in the sagittal plane, including angles, range of motion, angular velocity, joint moments and forces. This review demonstrates the applicability of wearable technology to assess the spine, although this technique is still at an early stage of development. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Wearable Antennas for Remote Health Care Monitoring Systems

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Laura Corchia

2017-01-01

Full Text Available Remote monitoring of the elderly in telehealth applications requires that the monitoring must not affect the elderly’s regular habits. To ensure this requirement, the components (i.e., sensor and antenna necessary to carry out such monitoring should blend in with the elderly’s daily routine. To this end, an effective strategy relies on employing wearable antennas that can be fully integrated with clothes and that can be used for remotely transmitting/receiving the sensor data. Starting from these considerations, in this work, two different methods for wearable antenna fabrication are described in detail: the first resorts to the combined use of nonwoven conductive fabrics and of a cutting plotter for shaping the fabric, whereas the second considered fabrication method resorts to the embroidery of conductive threads. To demonstrate the suitability of the considered fabrication techniques and to highlight their pros and cons, numerical and experimental results related to different wearable antennas are also reported and commented on. Results demonstrate that the presented fabrication techniques and strategies are very flexible and can be used to obtain low-cost wearable antennas with performance tailored for the specific application at hand.

System-Level Power Consumption Analysis of the Wearable Asthmatic Wheeze Quantification

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Dinko Oletic

2018-01-01

Full Text Available Long-term quantification of asthmatic wheezing envisions an m-Health sensor system consisting of a smartphone and a body-worn wireless acoustic sensor. As both devices are power constrained, the main criterion guiding the system design comes down to minimization of power consumption, while retaining sufficient respiratory sound classification accuracy (i.e., wheeze detection. Crucial for assessment of the system-level power consumption is the understanding of trade-off between power cost of computationally intensive local processing and communication. Therefore, we analyze power requirements of signal acquisition, processing, and communication in three typical operating scenarios: (1 streaming of uncompressed respiratory signal to a smartphone for classification, (2 signal streaming utilizing compressive sensing (CS for reduction of data rate, and (3 respiratory sound classification onboard the wearable sensor. Study shows that the third scenario featuring the lowest communication cost enables the lowest total sensor system power consumption ranging from 328 to 428 μW. In such scenario, 32-bit ARM Cortex M3/M4 cores typically embedded within Bluetooth 4 SoC modules feature the optimal trade-off between onboard classification performance and consumption. On the other hand, study confirms that CS enables the most power-efficient design of the wearable sensor (216 to 357 μW in the compressed signal streaming, the second scenario. In such case, a single low-power ARM Cortex-A53 core is sufficient for simultaneous real-time CS reconstruction and classification on the smartphone, while keeping the total system power within budget for uncompressed streaming.

Review of Wearable Device Technology and Its Applications to the Mining Industry

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Mokhinabonu Mardonova

2018-03-01

Full Text Available This paper reviews current trends in wearable device technology, and provides an overview of its prevalent and potential deployments in the mining industry. This review includes the classification of wearable devices with some examples of their utilization in various industrial fields as well as the features of sensors used in wearable devices. Existing applications of wearable device technology to the mining industry are reviewed. In addition, a wearable safety management system for miners and other possible applications are proposed. The findings of this review show that by introducing wearable device technology to mining sites, the safety of mining operations can be enhanced. Therefore, wearable devices should be further used in the mining industry.

Wearable Devices in Medical Internet of Things: Scientific Research and Commercially Available Devices.

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Haghi, Mostafa; Thurow, Kerstin; Stoll, Regina

2017-01-01

Wearable devices are currently at the heart of just about every discussion related to the Internet of Things. The requirement for self-health monitoring and preventive medicine is increasing due to the projected dramatic increase in the number of elderly people until 2020. Developed technologies are truly able to reduce the overall costs for prevention and monitoring. This is possible by constantly monitoring health indicators in various areas, and in particular, wearable devices are considered to carry this task out. These wearable devices and mobile apps now have been integrated with telemedicine and telehealth efficiently, to structure the medical Internet of Things. This paper reviews wearable health care devices both in scientific papers and commercial efforts. MIoT is demonstrated through a defined architecture design, including hardware and software dealing with wearable devices, sensors, smart phones, medical application, and medical station analyzers for further diagnosis and data storage. Wearables, with the help of improved technology have been developed greatly and are considered reliable tools for long-term health monitoring systems. These are applied in the observation of a large variety of health monitoring indicators in the environment, vital signs, and fitness. Wearable devices are now used for a wide range of healthcare observation. One of the most important elements essential in data collection is the sensor. During recent years with improvement in semiconductor technology, sensors have made investigation of a full range of parameters closer to realization.

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

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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.

Advances in wearable technology for rehabilitation.

Science.gov (United States)

Bonato, Paolo

2009-01-01

Assessing the impact of rehabilitation interventions on the real life of individuals is a key element of the decision-making process required to choose a rehabilitation strategy. In the past, therapists and physicians inferred the effectiveness of a given rehabilitation approach from observations performed in a clinical setting and self-reports by patients. Recent developments in wearable technology have provided tools to complement the information gathered by rehabilitation personnel via patient's direct observation and via interviews and questionnaires. A new generation of wearable sensors and systems has emerged that allows clinicians to gather measures in the home and community settings that capture patients' activity level and exercise compliance, the effectiveness of pharmacological interventions, and the ability of patients to perform efficiently specific motor tasks. Available unobtrusive sensors allow clinical personnel to monitor patients' movement and physiological data such as heart rate, respiratory rate, and oxygen saturation. Cell phone technology and the widespread access to the Internet provide means to implement systems designed to remotely monitor patients' status and optimize interventions based on individual responses to different rehabilitation approaches. This chapter summarizes recent advances in the field of wearable technology and presents examples of application of this technology in rehabilitation.

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

Science.gov (United States)

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

2016-12-01

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

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.

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.

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.

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

OpenAIRE

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

2018-01-01

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

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

Science.gov (United States)

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

2016-07-01

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

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).

Smart Sensors Enable Smart Air Conditioning Control

Directory of Open Access Journals (Sweden)

Chin-Chi Cheng

2014-06-01

Full Text Available In this study, mobile phones, wearable devices, temperature and human motion detectors are integrated as smart sensors for enabling smart air conditioning control. Smart sensors obtain feedback, especially occupants’ information, from mobile phones and wearable devices placed on human body. The information can be used to adjust air conditioners in advance according to humans’ intentions, in so-called intention causing control. Experimental results show that the indoor temperature can be controlled accurately with errors of less than ±0.1 °C. Rapid cool down can be achieved within 2 min to the optimized indoor capacity after occupants enter a room. It’s also noted that within two-hour operation the total compressor output of the smart air conditioner is 48.4% less than that of the one using On-Off control. The smart air conditioner with wearable devices could detect the human temperature and activity during sleep to determine the sleeping state and adjusting the sleeping function flexibly. The sleeping function optimized by the smart air conditioner with wearable devices could reduce the energy consumption up to 46.9% and keep the human health. The presented smart air conditioner could provide a comfortable environment and achieve the goals of energy conservation and environmental protection.

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)

The Baetylus Theorem-the central disconnect driving consumer behavior and investment returns in Wearable Technologies.

Science.gov (United States)

Levine, James A

2016-08-01

The Wearable Technology market may increase fivefold by the end of the decade. There is almost no academic investigation as to what drives the investment hypothesis in wearable technologies. This paper seeks to examine this issue from an evidence-based perspective. There is a fundamental disconnect in how consumers view wearable sensors and how companies market them; this is called The Baetylus Theorem where people believe (falsely) that by buying a wearable sensor they will receive health benefit; data suggest that this is not the case. This idea is grounded social constructs, psychological theories and marketing approaches. A marketing proposal that fails to recognize The Baetylus Theorem and how it can be integrated into a business offering has not optimized its competitive advantage. More importantly, consumers should not falsely believe that purchasing a wearable technology, improves health.

The Baetylus Theorem—the central disconnect driving consumer behavior and investment returns in Wearable Technologies

Science.gov (United States)

Levine, James A.

2016-01-01

The Wearable Technology market may increase fivefold by the end of the decade. There is almost no academic investigation as to what drives the investment hypothesis in wearable technologies. This paper seeks to examine this issue from an evidence-based perspective. There is a fundamental disconnect in how consumers view wearable sensors and how companies market them; this is called The Baetylus Theorem where people believe (falsely) that by buying a wearable sensor they will receive health benefit; data suggest that this is not the case. This idea is grounded social constructs, psychological theories and marketing approaches. A marketing proposal that fails to recognize The Baetylus Theorem and how it can be integrated into a business offering has not optimized its competitive advantage. More importantly, consumers should not falsely believe that purchasing a wearable technology, improves health. PMID:27617162

Loose Coupling of Wearable-Based INSs with Automatic Heading Evaluation

Directory of Open Access Journals (Sweden)

Dina Bousdar Ahmed

2017-11-01

Full Text Available Position tracking of pedestrians by means of inertial sensors is a highly explored field of research. In fact, there are already many approaches to implement inertial navigation systems (INSs. However, most of them use a single inertial measurement unit (IMU attached to the pedestrian’s body. Since wearable-devices will be given items in the future, this work explores the implementation of an INS using two wearable-based IMUs. A loosely coupled approach is proposed to combine the outputs of wearable-based INSs. The latter are based on a pocket-mounted IMU and a foot-mounted IMU. The loosely coupled fusion combines the output of the two INSs not only when these outputs are least erroneous, but also automatically favoring the best output. This approach is named smart update. The main challenge is determining the quality of the heading estimation of each INS, which changes every time. In order to address this, a novel concept to determine the quality of the heading estimation is presented. This concept is subject to a patent application. The results show that the position error rate of the loosely coupled fusion is 10 cm/s better than either the foot INS’s or pocket INS’s error rate in 95% of the cases.

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

Science.gov (United States)

Bousdar Ahmed, Dina; Munoz Diaz, Estefania

2017-11-03

Position tracking of pedestrians by means of inertial sensors is a highly explored field of research. In fact, there are already many approaches to implement inertial navigation systems (INSs). However, most of them use a single inertial measurement unit (IMU) attached to the pedestrian's body. Since wearable-devices will be given items in the future, this work explores the implementation of an INS using two wearable-based IMUs. A loosely coupled approach is proposed to combine the outputs of wearable-based INSs. The latter are based on a pocket-mounted IMU and a foot-mounted IMU. The loosely coupled fusion combines the output of the two INSs not only when these outputs are least erroneous, but also automatically favoring the best output. This approach is named smart update. The main challenge is determining the quality of the heading estimation of each INS, which changes every time. In order to address this, a novel concept to determine the quality of the heading estimation is presented. This concept is subject to a patent application. The results show that the position error rate of the loosely coupled fusion is 10 cm/s better than either the foot INS's or pocket INS's error rate in 95% of the cases.

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.

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

Science.gov (United States)

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

2016-09-01

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

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

Science.gov (United States)

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

2017-07-01

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

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.

Development of a platform to combine sensor networks and home robots to improve fall detection in the home environment.

Science.gov (United States)

Della Toffola, Luca; Patel, Shyamal; Chen, Bor-rong; Ozsecen, Yalgin M; Puiatti, Alessandro; Bonato, Paolo

2011-01-01

Over the last decade, significant progress has been made in the development of wearable sensor systems for continuous health monitoring in the home and community settings. One of the main areas of application for these wearable sensor systems is in detecting emergency events such as falls. Wearable sensors like accelerometers are increasingly being used to monitor daily activities of individuals at a risk of falls, detect emergency events and send alerts to caregivers. However, such systems tend to have a high rate of false alarms, which leads to low compliance levels. Home robots can enable caregivers with the ability to quickly make an assessment and intervene if an emergency event is detected. This can provide an additional layer for detecting false positives, which can lead to improve compliance. In this paper, we present preliminary work on the development of a fall detection system based on a combination sensor networks and home robots. The sensor network architecture comprises of body worn sensors and ambient sensors distributed in the environment. We present the software architecture and conceptual design home robotic platform. We also perform preliminary characterization of the sensor network in terms of latencies and battery lifetime.

Adaptive cancellation of motion artifact in wearable biosensors.

Science.gov (United States)

Yousefi, Rasoul; Nourani, Mehrdad; Panahi, Issa

2012-01-01

The performance of wearable biosensors is highly influenced by motion artifact. In this paper, a model is proposed for analysis of motion artifact in wearable photoplethysmography (PPG) sensors. Using this model, we proposed a robust real-time technique to estimate fundamental frequency and generate a noise reference signal. A Least Mean Square (LMS) adaptive noise canceler is then designed and validated using our synthetic noise generator. The analysis and results on proposed technique for noise cancellation shows promising performance.

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

Science.gov (United States)

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

2013-01-01

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

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

Science.gov (United States)

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

2018-06-08

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

Axial Permanent Magnet Generator for Wearable Energy Harvesting

DEFF Research Database (Denmark)

Högberg, Stig; Sødahl, Jakob Wagner; Mijatovic, Nenad

2016-01-01

An increasing demand for battery-free electronics is evident by the rapid increase of wearable devices, and the design of wearable energy harvesters follows accordingly. An axial permanent magnet generator was designed to harvest energy from human body motion and supplying it to a wearable......W, respectively) with an iron yoke is subject to losses that exceed the realistic input power, and was therefore deemed infeasible. A generator without the iron yoke was concluded to perform well as a wearable energy harvester. An experimental investigation of a prototype revealed an output power of almost 1 m...

3D Printed "Earable" Smart Devices for Real-Time Detection of Core Body Temperature.

Science.gov (United States)

Ota, Hiroki; Chao, Minghan; Gao, Yuji; Wu, Eric; Tai, Li-Chia; Chen, Kevin; Matsuoka, Yasutomo; Iwai, Kosuke; Fahad, Hossain M; Gao, Wei; Nyein, Hnin Yin Yin; Lin, Liwei; Javey, Ali

2017-07-28

Real-time detection of basic physiological parameters such as blood pressure and heart rate is an important target in wearable smart devices for healthcare. Among these, the core body temperature is one of the most important basic medical indicators of fever, insomnia, fatigue, metabolic functionality, and depression. However, traditional wearable temperature sensors are based upon the measurement of skin temperature, which can vary dramatically from the true core body temperature. Here, we demonstrate a three-dimensional (3D) printed wearable "earable" smart device that is designed to be worn on the ear to track core body temperature from the tympanic membrane (i.e., ear drum) based on an infrared sensor. The device is fully integrated with data processing circuits and a wireless module for standalone functionality. Using this smart earable device, we demonstrate that the core body temperature can be accurately monitored regardless of the environment and activity of the user. In addition, a microphone and actuator are also integrated so that the device can also function as a bone conduction hearing aid. Using 3D printing as the fabrication method enables the device to be customized for the wearer for more personalized healthcare. This smart device provides an important advance in realizing personalized health care by enabling real-time monitoring of one of the most important medical parameters, core body temperature, employed in preliminary medical screening tests.

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

Science.gov (United States)

Gil, Yeongjoon; Wu, Wanqing; Lee, Jungtae

2012-01-01

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

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.

Flexible thermoelectric generator using bulk legs and liquid metal interconnects for wearable electronics

International Nuclear Information System (INIS)

Suarez, Francisco; Parekh, Dishit P.; Ladd, Collin; Vashaee, Daryoosh; Dickey, Michael D.; Öztürk, Mehmet C.

2017-01-01

Highlights: •Flexible thermoelectric generator (TEG) with bulk legs. •Flexible thermoelectric generator with liquid metal interconnects. •Flexible TEG with potential to match the performance of rigid TEGs. •Flexible TEG for wearable electronics. -- Abstract: Interest in wearable electronics for continuous, long-term health and performance monitoring is rapidly increasing. The reduction in power levels consumed by sensors and electronic circuits accompanied by the advances in energy harvesting methods allows for the realization of self-powered monitoring systems that do not have to rely on batteries. For wearable electronics, thermoelectric generators (TEGs) offer the unique ability to continuously convert body heat into usable energy. For body harvesting, it is preferable to have TEGs that are thin, soft and flexible. Unfortunately, the performances of flexible modules reported to date have been far behind those of their rigid counterparts. This is largely due to lower efficiencies of the thermoelectric materials, electrical or thermal parasitic losses and limitations on leg dimensions posed by the synthesis techniques. In this work, we present an entirely new approach and explore the possibility of using standard bulk legs in a flexible package. Bulk thermoelectric legs cut from solid ingots are far superior to thermoelectric materials synthesized using other techniques. A key enabler of the proposed technology is the use of EGaIn liquid metal interconnects, which not only provide extremely low interconnect resistance but also stretchability with self-healing, both of which are essential for flexible TE modules. The results suggest that this novel approach can finally produce flexible TEGs that have the potential to challenge the rigid TEGs and provide a pathway for the realization of self-powered wearable electronics.

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

DEFF Research Database (Denmark)

Jalaliniya, Shahram

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

Research and development of smart wearable health applications: the challenge ahead.

Science.gov (United States)

Lymberis, Andreas

2004-01-01

Continuous monitoring of physiological and physical parameters is necessary for the assessment and management of personal health status. It can significantly contribute to the reduction of healthcare cost by avoiding unnecessary hospitalisations and ensuring that those who need urgent care get it sooner. In conjunction with cost-effective telemedicine platforms, ubiquitous health monitoring can significantly contribute to the enhancement of disease prevention and early diagnosis, disease management, treatment and home rehabilitation. Latest developments in the area of micro and nanotechnologies, information processing and wireless communication offer, today, the possibility for minimally (or non) invasive biomedical measurement but also wearable sensing, processing and data communication. Although the systems are being developed to satisfy specific user needs, a number of common critical issues have to be tackled to achieve reliable and acceptable smart health wearable applications e.g. biomedical sensors, user interface, clinical validation, data security and confidentiality, scenarios of use, decision support, user acceptance and business models. Major technological achievements have been realised the last few years. Cutting edge development combining functional clothing and integrated electronics open a new research area and possibilities for body sensing and communicating health parameters. This paper reviews the current status of research and development on smart wearable health systems and applications and discusses the outstanding issues and future challenges.

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

Science.gov (United States)

Jeon, Jun-Young; Ha, Tae-Jun

2016-02-03

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

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

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

2018-01-10

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

Wearable Eating Habit Sensing System Using Internal Body Sound

Science.gov (United States)

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

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

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

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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.

Detecting gunshots using wearable accelerometers.

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Charles E Loeffler

Full Text Available Gun violence continues to be a staggering and seemingly intractable issue in many communities. The prevalence of gun violence among the sub-population of individuals under court-ordered community supervision provides an opportunity for intervention using remote monitoring technology. Existing monitoring systems rely heavily on location-based monitoring methods, which have incomplete geographic coverage and do not provide information on illegal firearm use. This paper presents the first results demonstrating the feasibility of using wearable inertial sensors to recognize wrist movements and other signals corresponding to firearm usage. Data were collected from accelerometers worn on the wrists of subjects shooting a number of different firearms, conducting routine daily activities, and participating in activities and tasks that could be potentially confused with firearm discharges. A training sample was used to construct a combined detector and classifier for individual gunshots, which achieved a classification accuracy of 99.4 percent when tested against a hold-out sample of observations. These results suggest the feasibility of using inexpensive wearable sensors to detect firearm discharges.

Conceptual design of wearpack with physiology detector feature based on wearable instrumentation

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Sukirman, Melani; Laksono, Pringgo Widyo; Priadythama, Ilham; Susmartini, Susy; Suhardi, Bambang

2017-11-01

Every company in Indonesia is responsible for their worker health and safety condition as mentioned in UU No I year 1970. In manufacturing industries, there are many manual tasks dealing with high work load and risk, so that they require excellent concentration and physical condition. There is no ideal way to guarantee worker safety without a real time physiological monitoring. This paper reports our ongoing study in conceptual design development of worker's clothing which is equipped with a wearable instrumentation system. The system is designed to detect and measure body temperature and pulse in real time. Some electrical components such as, LCD (liquid crystal display), LEDs (light emitting diode), batteries, and physiological sensors were assembled. All components are controlled by a wearable on board controller. LEDs is used as alert which can indicate abnormal physical conditions. The LCD was added to provide more detail information. TMP 36 and XD-58C were selected as the physiological sensors. Finally, an Arduino Lilypad was chosen for the controller. This instrumentation system was verified by accurately detected and inform physiological condition of 3 subjects. Further we are going to attach the system to a worker's clothing which was specifically designed to simplify and comfortable usage.

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

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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.

Biosignal and context monitoring: Distributed multimedia applications of body area networks in healthcare

NARCIS (Netherlands)

Jones, Valerie M.; Huis in 't Veld, M.H.A.; Tonis, T.; Tönis, Thijs; Bults, Richard G.A.; van Beijnum, Bernhard J.F.; Widya, I.A.; Vollenbroek-Hutten, Miriam Marie Rosé; Hermens, Hermanus J.

2008-01-01

We are investigating the use of Body Area Networks (BANs), wearable sensors and wireless communications for measuring, processing, transmission, interpretation and display of biosignals. The goal is to provide telemonitoring and teletreatment services for patients. The remote health professional can

A challenge for higher education: Wearable technology for fashion design departments

Directory of Open Access Journals (Sweden)

Elif Buğra Kuzu Demir

2016-04-01

Full Text Available As the technology is integrated into different disciplines, we witness how powerful it can be. Rather than standing in isolation, technology changes the nature of the field it arrives in. Wearable technologies in fashion design education is a good example for this. Wearable technology defined as lightweight, easy portable and wearable smart devices that have sensors and computing capabilities. The structure of wearable technologies has brought a new trend to fashion design area. Fashion design, as known to be a very dynamic application area, has already accepted the issue and started using the most powerful examples of wearable technologies already. However, although the stages are using wearable technologies, the schools that graduate fashion designers of the future are far beyond the capacity of the stages. It is therefore; this paper brings suggestions for the integration of technology into fashion design departments in Turkey and while doing this it tries to be country specific.

Enzymatic Fuel Cells: Towards Self-Powered Implantable and Wearable Diagnostics.

Science.gov (United States)

Gonzalez-Solino, Carla; Lorenzo, Mirella Di

2018-01-29

With the rapid progress in nanotechnology and microengineering, point-of-care and personalised healthcare, based on wearable and implantable diagnostics, is becoming a reality. Enzymatic fuel cells (EFCs) hold great potential as a sustainable means to power such devices by using physiological fluids as the fuel. This review summarises the fundamental operation of EFCs and discusses the most recent advances for their use as implantable and wearable self-powered sensors.

Enzymatic Fuel Cells: Towards Self-Powered Implantable and Wearable Diagnostics

OpenAIRE

Carla Gonzalez-Solino; Mirella Di Lorenzo

2018-01-01

With the rapid progress in nanotechnology and microengineering, point-of-care and personalised healthcare, based on wearable and implantable diagnostics, is becoming a reality. Enzymatic fuel cells (EFCs) hold great potential as a sustainable means to power such devices by using physiological fluids as the fuel. This review summarises the fundamental operation of EFCs and discusses the most recent advances for their use as implantable and wearable self-powered sensors.

Scalable Production of Graphene-Based Wearable E-Textiles.

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Karim, Nazmul; Afroj, Shaila; Tan, Sirui; He, Pei; Fernando, Anura; Carr, Chris; Novoselov, Kostya S

2017-12-26

Graphene-based wearable e-textiles are considered to be promising due to their advantages over traditional metal-based technology. However, the manufacturing process is complex and currently not suitable for industrial scale application. Here we report a simple, scalable, and cost-effective method of producing graphene-based wearable e-textiles through the chemical reduction of graphene oxide (GO) to make stable reduced graphene oxide (rGO) dispersion which can then be applied to the textile fabric using a simple pad-dry technique. This application method allows the potential manufacture of conductive graphene e-textiles at commercial production rates of ∼150 m/min. The graphene e-textile materials produced are durable and washable with acceptable softness/hand feel. The rGO coating enhanced the tensile strength of cotton fabric and also the flexibility due to the increase in strain% at maximum load. We demonstrate the potential application of these graphene e-textiles for wearable electronics with activity monitoring sensor. This could potentially lead to a multifunctional single graphene e-textile garment that can act both as sensors and flexible heating elements powered by the energy stored in graphene textile supercapacitors.

Business model design for a wearable biofeedback system.

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Hidefjäll, Patrik; Titkova, Dina

2015-01-01

Wearable sensor technologies used to track daily activities have become successful in the consumer market. In order for wearable sensor technology to offer added value in the more challenging areas of stress-rehab care and occupational health stress-related biofeedback parameters need to be monitored and more elaborate business models are needed. To identify probable success factors for a wearable biofeedback system (Affective Health) in the two mentioned market segments in a Swedish setting, we conducted literature studies and interviews with relevant representatives. Data were collected and used first to describe the two market segments and then to define likely feasible business model designs, according to the Business Model Canvas framework. Needs of stakeholders were identified as inputs to business model design. Value propositions, a key building block of a business model, were defined for each segment. The value proposition for occupational health was defined as "A tool that can both identify employees at risk of stress-related disorders and reinforce healthy sustainable behavior" and for healthcare as: "Providing therapists with objective data about the patient's emotional state and motivating patients to better engage in the treatment process".

Recent Progress on Flexible and Wearable Supercapacitors.

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Xue, Qi; Sun, Jinfeng; Huang, Yan; Zhu, Minshen; Pei, Zengxia; Li, Hongfei; Wang, Yukun; Li, Na; Zhang, Haiyan; Zhi, Chunyi

2017-12-01

Recently, wearable electronic devices including electrical sensors, flexible displays, and health monitors have received considerable attention and experienced rapid progress. Wearable supercapacitors attract tremendous attention mainly due to their high stability, low cost, fast charging/discharging, and high efficiency; properties that render them value for developing fully flexible devices. In this Concept, the recent achievements and advances made in flexible and wearable supercapacitors are presented, especially highlighting the promising performances of yarn/fiber-shaped and planar supercapacitors. On the basis of their working mechanism, electrode materials including carbon-based materials, metal oxide-based materials, and conductive polymers with an emphasis on the performance-optimization method are introduced. The latest representative techniques and active materials of recently developed supercapacitors with superior performance are summarized. Furthermore, the designs of 1D and 2D electrodes are discussed according to their electrically conductive supporting materials. Finally, conclusions, challenges, and perspective in optimizing and developing the electrochemical performance and function of wearable supercapacitors for their practical utility are addressed. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Detection of vapor-phase organophosphate threats using wearable conformable integrated epidermal and textile wireless biosensor systems.

Science.gov (United States)

Mishra, Rupesh K; Martín, Aida; Nakagawa, Tatsuo; Barfidokht, Abbas; Lu, Xialong; Sempionatto, Juliane R; Lyu, Kay Mengjia; Karajic, Aleksandar; Musameh, Mustafa M; Kyratzis, Ilias L; Wang, Joseph

2018-03-15

Flexible epidermal tattoo and textile-based electrochemical biosensors have been developed for vapor-phase detection of organophosphorus (OP) nerve agents. These new wearable sensors, based on stretchable organophosphorus hydrolase (OPH) enzyme electrodes, are coupled with a fully integrated conformal flexible electronic interface that offers rapid and selective square-wave voltammetric detection of OP vapor threats and wireless data transmission to a mobile device. The epidermal tattoo and textile sensors display a good reproducibility (with RSD of 2.5% and 4.2%, respectively), along with good discrimination against potential interferences and linearity over the 90-300mg/L range, with a sensitivity of 10.7µA∙cm 3 ∙mg -1 (R 2 = 0.983) and detection limit of 12mg/L in terms of OP air density. Stress-enduring inks, used for printing the electrode transducers, ensure resilience against mechanical deformations associated with textile and skin-based on-body sensing operations. Theoretical simulations are used to estimate the OP air density over the sensor surface. These fully integrated wearable wireless tattoo and textile-based nerve-agent vapor biosensor systems offer considerable promise for rapid warning regarding personal exposure to OP nerve-agent vapors in variety of decentralized security applications. Copyright © 2017 Elsevier B.V. All rights reserved.

Flexible tension sensor based on poly(l-lactic acid) film with coaxial structure

Science.gov (United States)

Yoshida, Mitsunobu; Onishi, Katsuki; Tanimoto, Kazuhiro; Nishikawa, Shigeo

2017-10-01

We have developed a tension sensor with a coaxial structure using a narrow slit ribbon made of a uniaxially stretched poly(l-lactic acid) (PLLA) film for application to a wearable device. The tension sensor is produced as follows. We used tinsel wire as the center conductor of the sensor. The tinsel wire consists of a yarn of synthetic fibers arranged at the center, with a spirally wound rolled copper foil ribbon on the side surface. Next, slit ribbons obtained from a uniaxially oriented film of PLLA are wound helically on the side surface of the center conductor in the direction of a left-handed screw, at an angle of 45° to the central axis. The rolled copper foil is used as an outer conductor and covers the yarn without a gap. The prototype of the fabricated tension sensor has good flexibility, since the sensor is in the form of a filament and consists of a highly flexible material. For the 1 mm tension sensor, it was found that for a tension of 1 N, a charge of 14 pC was output. It was also found that the sensor maintained its room-temperature sensitivity up to 60 °C. Compared with an existing coaxial line sensor using poly(vinylidene fluoride) (PVDF), the sensor using PLLA does not exhibit pyroelectricity, meaning that no undesirable voltage is generated when in contact with body heat, which is a significant advantage as wearable sensors. The result has demonstrated the potential application of the PLLA film to wearable devices for detecting heartbeat and respiration.

Wearable medical systems for p-Health.

Science.gov (United States)

Teng, Xiao-Fei; Zhang, Yuan-Ting; Poon, Carmen C Y; Bonato, Paolo

2008-01-01

Driven by the growing aging population, prevalence of chronic diseases, and continuously rising healthcare costs, the healthcare system is undergoing a fundamental transformation, from the conventional hospital-centered system to an individual-centered system. Current and emerging developments in wearable medical systems will have a radical impact on this paradigm shift. Advances in wearable medical systems will enable the accessibility and affordability of healthcare, so that physiological conditions can be monitored not only at sporadic snapshots but also continuously for extended periods of time, making early disease detection and timely response to health threats possible. This paper reviews recent developments in the area of wearable medical systems for p-Health. Enabling technologies for continuous and noninvasive measurements of vital signs and biochemical variables, advances in intelligent biomedical clothing and body area networks, approaches for motion artifact reduction, strategies for wearable energy harvesting, and the establishment of standard protocols for the evaluation of wearable medical devices are presented in this paper with examples of clinical applications of these technologies.

Stretch sensors for human body motion

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

2014-03-01

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

Wearable ear EEG for brain interfacing

Science.gov (United States)

Schroeder, Eric D.; Walker, Nicholas; Danko, Amanda S.

2017-02-01

Brain-computer interfaces (BCIs) measuring electrical activity via electroencephalogram (EEG) have evolved beyond clinical applications to become wireless consumer products. Typically marketed for meditation and neu- rotherapy, these devices are limited in scope and currently too obtrusive to be a ubiquitous wearable. Stemming from recent advancements made in hearing aid technology, wearables have been shrinking to the point that the necessary sensors, circuitry, and batteries can be fit into a small in-ear wearable device. In this work, an ear-EEG device is created with a novel system for artifact removal and signal interpretation. The small, compact, cost-effective, and discreet device is demonstrated against existing consumer electronics in this space for its signal quality, comfort, and usability. A custom mobile application is developed to process raw EEG from each device and display interpreted data to the user. Artifact removal and signal classification is accomplished via a combination of support matrix machines (SMMs) and soft thresholding of relevant statistical properties.

WEARABLE ELECTRONICS IN THE NEXT YEARS

Directory of Open Access Journals (Sweden)

PRINIOTAKIS George

2015-05-01

Full Text Available The term ‘Wearable Technologies’, ‘Wearable Electronics’, or ‘Smart Garments’, is associated to those clothing and soft or hard accessories which integrate electronic components, or which are made of smart textiles. Smart textiles research represents a new model for generating creative and novel solutions for integrating electronics into unusual environments and will result in new discoveries that push the boundaries of science forward. Last few years there are several hundreds or maybe thousands of research teams that works and develop such materials and products. But the key driver of the success of the wearable electronics is the acceptance from the end user. It is estimates that only for the next three years the sales in the wearable will be almost multiply by ten times. The flexible wearable computer industry's patent applications arrived at 429 in the second quarter of 2014, up 27.7% year on year, and witnessed a record high in the report's tracking period starting from the first quarter of 2012. The market has already in the shelf commercial products as wristbands (Fitness/well-being/sports devices, smart jewels, smart watches, mobile health devices, tech clothing, and augmented reality glasses. The recently developed enabling technologies eliminates the barriers and help the scientists and developers to launch new types of "wearable". The life style of a large share of population, the low cost of 3D printing for rapid prototyping locally, the large available platforms, the lower cost of sensors and components give a an impetus for large scale of products. In the same time the direct ordering channels to manufacturers of components facilitates the small producers and the scientists for prototype development. In this article we identify key challenges for the success of the wearable and we provide an outlook over the field and a prediction for the near future.

Analysis of Indoor Rowing Motion using Wearable Inertial Sensors

NARCIS (Netherlands)

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

2015-01-01

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

Enzymatic Fuel Cells: Towards Self-Powered Implantable and Wearable Diagnostics

Science.gov (United States)

Gonzalez-Solino, Carla; Lorenzo, Mirella Di

2018-01-01

With the rapid progress in nanotechnology and microengineering, point-of-care and personalised healthcare, based on wearable and implantable diagnostics, is becoming a reality. Enzymatic fuel cells (EFCs) hold great potential as a sustainable means to power such devices by using physiological fluids as the fuel. This review summarises the fundamental operation of EFCs and discusses the most recent advances for their use as implantable and wearable self-powered sensors. PMID:29382147

Enzymatic Fuel Cells: Towards Self-Powered Implantable and Wearable Diagnostics

Directory of Open Access Journals (Sweden)

Carla Gonzalez-Solino

2018-01-01

Full Text Available With the rapid progress in nanotechnology and microengineering, point-of-care and personalised healthcare, based on wearable and implantable diagnostics, is becoming a reality. Enzymatic fuel cells (EFCs hold great potential as a sustainable means to power such devices by using physiological fluids as the fuel. This review summarises the fundamental operation of EFCs and discusses the most recent advances for their use as implantable and wearable self-powered sensors.

A wearable fingernail chemical sensing platform: pH sensing at your fingertips.

Science.gov (United States)

Kim, Jayoung; Cho, Thomas N; Valdés-Ramírez, Gabriela; Wang, Joseph

2016-04-01

This article demonstrates an example of a wearable chemical sensor based on a fingernail platform. Fingernails represent an attractive wearable platform, merging beauty products with chemical sensing, to enable monitoring of our surrounding environment. The new colorimetric pH fingernail sensor relies on coating artificial nails with a recognition layer consisted of pH indicators entrapped in a polyvinyl chloride (PVC) matrix. Such color changing fingernails offer fast and reversible response to pH changes, repeated use, and intense color change detected easily with naked eye. The PVC matrix prevents leaching out of the indicator molecules from the fingernail sensor toward such repeated use. The limited narrow working pH range of a single pH indicator has been addressed by multiplexing three different pH indicators: bromothymol blue (pH 6.0-7.6), bromocresol green (pH 3.8-5.4), and cresol red (pH 7.2-8.8), as demonstrated for analyses of real-life samples of acidic, neutral, and basic character. The new concept of an optical wearable chemical sensor on fingernail platforms can be expanded towards diverse analytes for various applications in connection to the judicious design of the recognition layer. Copyright © 2016 Elsevier B.V. All rights reserved.

Estimating three-dimensional orientation of human body parts by inertial/magnetic sensing.

Science.gov (United States)

Sabatini, Angelo Maria

2011-01-01

User-worn sensing units composed of inertial and magnetic sensors are becoming increasingly popular in various domains, including biomedical engineering, robotics, virtual reality, where they can also be applied for real-time tracking of the orientation of human body parts in the three-dimensional (3D) space. Although they are a promising choice as wearable sensors under many respects, the inertial and magnetic sensors currently in use offer measuring performance that are critical in order to achieve and maintain accurate 3D-orientation estimates, anytime and anywhere. This paper reviews the main sensor fusion and filtering techniques proposed for accurate inertial/magnetic orientation tracking of human body parts; it also gives useful recipes for their actual implementation.

A Systematic Review of Wearable Systems for Cancer Detection: Current State and Challenges.

Science.gov (United States)

Ray, Partha Pratim; Dash, Dinesh; De, Debashis

2017-10-02

Rapid growth of sensor and computing platforms have introduced the wearable systems. In recent years, wearable systems have led to new applications across all medical fields. The aim of this review is to present current state-of-the-art approach in the field of wearable system based cancer detection and identify key challenges that resist it from clinical adoption. A total of 472 records were screened and 11 were finally included in this study. Two types of records were studied in this context that includes 45% research articles and 55% manufactured products. The review was performed per PRISMA guidelines where considerations was given to records that were published or reported between 2009 and 2017. The identified records included 4 cancer detecting wearable systems such as breast cancer (36.3%), skin cancer (36.3%), prostate cancer (18.1%), and multi-type cancer (9%). Most works involved sensor based smart systems comprising of microcontroller, Bluetooth module, and smart phone. Few demonstrated Ultra-Wide Band (i.e. UWB) antenna based wearable systems. Skin cancer detecting wearable systems were most comprehensible ones. The current works are gradually progressing with seamless integration of sensory units along with smart networking. However, they lack in cloud computing and long-range communication paradigms. Artificial intelligence and machine learning are key ports that need to be attached with current wearable systems. Further, clinical inertia, lack of awareness, and high cost are altogether pulling back the actual growth of such system. It is well comprehended that upon sincere orientation of all identified challenges, wearable systems would emerge as vital alternative to futuristic cancer detection.

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.

Reviving a medical wearable computer for teaching purposes.

Science.gov (United States)

Frenger, Paul

2014-01-01

In 1978 the author constructed a medical wearable computer using an early CMOS microprocessor and support chips. This device was targeted for use by health-conscious consumers and other early adopters. Its expandable functions included weight management, blood pressure control, diabetes care, medication reminders, smoking cessation, pediatric growth and development, simple medical database, digital communication with a doctor’s office and emergency alert system. Various physiological sensors could be plugged-into the calculator-sized chassis. The device was shown to investor groups but funding was not obtained; by 1992 the author ceased pursuing it. The Computing and Mathematics Chair at a local University, a NASA acquaintance, approached the author to mentor a CS capstone course for Summer 2012. With the author’s guidance, five students proceeded to convert this medical wearable computer design to an iPhone-based implementation using the Apple Xcode Developer Kit and other utilities. The final student device contained a body mass index (BMI) calculator, an emergency alert for 911 or other first responders, a medication reminder, a Doctor’s appointment feature, a medical database, medical Internet links, and a pediatric growth & development guide. The students’ final imple-mentation was successfully demonstrated on an actual iPhone 4 at the CS capstone meeting in mid-Summer.

A Framework for Learning Analytics Using Commodity Wearable Devices.

Science.gov (United States)

Lu, Yu; Zhang, Sen; Zhang, Zhiqiang; Xiao, Wendong; Yu, Shengquan

2017-06-14

We advocate for and introduce LEARNSense, a framework for learning analytics using commodity wearable devices to capture learner's physical actions and accordingly infer learner context (e.g., student activities and engagement status in class). Our work is motivated by the observations that: (a) the fine-grained individual-specific learner actions are crucial to understand learners and their context information; (b) sensor data available on the latest wearable devices (e.g., wrist-worn and eye wear devices) can effectively recognize learner actions and help to infer learner context information; (c) the commodity wearable devices that are widely available on the market can provide a hassle-free and non-intrusive solution. Following the above observations and under the proposed framework, we design and implement a sensor-based learner context collector running on the wearable devices. The latest data mining and sensor data processing techniques are employed to detect different types of learner actions and context information. Furthermore, we detail all of the above efforts by offering a novel and exemplary use case: it successfully provides the accurate detection of student actions and infers the student engagement states in class. The specifically designed learner context collector has been implemented on the commodity wrist-worn device. Based on the collected and inferred learner information, the novel intervention and incentivizing feedback are introduced into the system service. Finally, a comprehensive evaluation with the real-world experiments, surveys and interviews demonstrates the effectiveness and impact of the proposed framework and this use case. The F1 score for the student action classification tasks achieve 0.9, and the system can effectively differentiate the defined three learner states. Finally, the survey results show that the learners are satisfied with the use of our system (mean score of 3.7 with a standard deviation of 0.55).

Wearable Electro-Textiles for Battlefield Awareness

National Research Council Canada - National Science Library

Winterhalter, C. A; Teverovsky, Justyna; Horowitz, Wendy; Sharma, Vikram; Lee, Kang

2004-01-01

This summary describes efforts to develop wearable electronic textiles and connectors to support body worn networking, communications, and battlefield awareness for future service members of the U.S. Army...

A wearable, mobile phone-based respiration monitoring system for sleep apnea syndrome detection.

Science.gov (United States)

Ishida, Ryoichi; Yonezawa, Yoshiharu; Maki, Hiromichi; Ogawa, Hidekuni; Ninomiya, Ishio; Sada, Kouji; Hamada, Shingo; Hahn, Allen W; Caldwell, W Morton

2005-01-01

A new wearable respiration monitoring system has been developed for non-invasive detection of sleep apnea syndrome. The system, which is attached to a shirt, consists of a piezoelectric sensor, a low-power 8-bit single chip microcontroller, EEPROM and a 2.4 GHz low-power transmitting mobile phone (PHS). The piezoelectric sensor, whose electrical polarization voltage is produced by body movements, is installed inside the shirt and closely contacts the patient's chest. The low frequency components of body movements recorded by the sensor are mainly generated by respiration. The microcontroller sequentially stores the movement signal to the EEPROM for 5 minutes and detects, by time-frequency analysis, whether the patient has breathed during that time. When the patient is apneic for 10 sseconds, the microcontroller sends the recorded respiration waveform during and one minute before and after the apnea directly to the hospital server computer via the mobile phone. The server computer then creates apnea "filings" automatically for every patient. The system can be used at home and be self-applied by patients. Moreover, the system does not require any extra equipment such as a personal computer, PDA, or Internet connection.

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

Science.gov (United States)

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

2018-05-08

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

Design and implementation of a wearable healthcare monitoring system.

Science.gov (United States)

Sagahyroon, Assim; Raddy, Hazem; Ghazy, Ali; Suleman, Umair

2009-01-01

A wearable healthcare monitoring unit that integrates various technologies was developed to provide patients with the option of leading a healthy and independent life without risks or confinement to medical facilities. The unit consists of various sensors integrated to a microcontroller and attached to the patient's body, reading vital signs and transmitting these readings via a Bluetooth link to the patient's mobile phone. Short-Messaging-Service (SMS) is incorporated in the design to alert a physician in emergency cases. Additionally, an application program running on the mobile phone uses the internet to update (at regular intervals) the patient records in a hospital database with the most recent readings. To reduce development costs, the components used were both off-the-shelf and affordable.

Application of data fusion techniques and technologies for wearable health monitoring.

Science.gov (United States)

King, Rachel C; Villeneuve, Emma; White, Ruth J; Sherratt, R Simon; Holderbaum, William; Harwin, William S

2017-04-01

Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market. Copyright © 2017. Published by Elsevier Ltd.

Design of wearable health monitoring device

Science.gov (United States)

Devara, Kresna; Ramadhanty, Savira; Abuzairi, Tomy

2018-02-01

Wearable smart health monitoring devices have attracted considerable attention in both research community and industry. Some of the causes are the increasing healthcare costs, along with the growing technology. To address this demand, in this paper, design and evaluation of wearable health monitoring device integrated with smartphone were presented. This device was designed for patients in need of constant health monitoring. The performance of the proposed design has been tested by conducting measurement once in 2 minutes for 10 minutes to obtain heart rate and body temperature data. The comparation between data measured by the proposed device and that measured by the reference device yields only an average error of 1.45% for heart rate and 1.04% for body temperature.

Localization and Tracking of Implantable Biomedical Sensors

Directory of Open Access Journals (Sweden)

Ilknur Umay

2017-03-01

Full Text Available Implantable sensor systems are effective tools for biomedical diagnosis, visualization and treatment of various health conditions, attracting the interest of researchers, as well as healthcare practitioners. These systems efficiently and conveniently provide essential data of the body part being diagnosed, such as gastrointestinal (temperature, pH, pressure parameter values, blood glucose and pressure levels and electrocardiogram data. Such data are first transmitted from the implantable sensor units to an external receiver node or network and then to a central monitoring and control (computer unit for analysis, diagnosis and/or treatment. Implantable sensor units are typically in the form of mobile microrobotic capsules or implanted stationary (body-fixed units. In particular, capsule-based systems have attracted significant research interest recently, with a variety of applications, including endoscopy, microsurgery, drug delivery and biopsy. In such implantable sensor systems, one of the most challenging problems is the accurate localization and tracking of the microrobotic sensor unit (e.g., robotic capsule inside the human body. This article presents a literature review of the existing localization and tracking techniques for robotic implantable sensor systems with their merits and limitations and possible solutions of the proposed localization methods. The article also provides a brief discussion on the connection and cooperation of such techniques with wearable biomedical sensor systems.

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)

Wearable Health Monitoring Systems, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — The objective of this proposal is to demonstrate the feasibility of producing a wearable health monitoring system for the human body that is functional, comfortable,...

Wearable Health Monitoring Systems, Phase II

Data.gov (United States)

National Aeronautics and Space Administration — The objective of this proposal is to demonstrate the feasibility of producing a wearable health monitoring system for the human body that is functional, comfortable,...

Development of Wearable Systems for Ubiquitous Healthcare Service Provisioning

OpenAIRE

Ogunduyile, O.O.; Olugbara, O.O.; Lall, M.

2013-01-01

This paper reports on the development of a wearable system using wireless biomedical sensors for ubiquitous healthcare service provisioning. The prototype system is developed to address current healthcare challenges such as increasing cost of services, inability to access diverse services, low quality services and increasing population of elderly as experienced globally. The biomedical sensors proactively collect physiological data of remote patients to recommend diagnostic services. The prot...

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

Directory of Open Access Journals (Sweden)

Luca Palmerini

2017-08-01

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

A method to deal with installation errors of wearable accelerometers for human activity recognition

International Nuclear Information System (INIS)

Jiang, Ming; Wang, Zhelong; Shang, Hong; Li, Hongyi; Wang, Yuechao

2011-01-01

Human activity recognition (HAR) by using wearable accelerometers has gained significant interest in recent years in a range of healthcare areas, including inferring metabolic energy expenditure, predicting falls, measuring gait parameters and monitoring daily activities. The implementation of HAR relies heavily on the correctness of sensor fixation. The installation errors of wearable accelerometers may dramatically decrease the accuracy of HAR. In this paper, a method is proposed to improve the robustness of HAR to the installation errors of accelerometers. The method first calculates a transformation matrix by using Gram–Schmidt orthonormalization in order to eliminate the sensor's orientation error and then employs a low-pass filter with a cut-off frequency of 10 Hz to eliminate the main effect of the sensor's misplacement. The experimental results showed that the proposed method obtained a satisfactory performance for HAR. The average accuracy rate from ten subjects was 95.1% when there were no installation errors, and was 91.9% when installation errors were involved in wearable accelerometers

Signal transmission in a human body medium-based body sensor network using a Mach-Zehnder electro-optical sensor.

Science.gov (United States)

Song, Yong; Hao, Qun; Zhang, Kai; Wang, Jingwen; Jin, Xuefeng; Sun, He

2012-11-30

The signal transmission technology based on the human body medium offers significant advantages in Body Sensor Networks (BSNs) used for healthcare and the other related fields. In previous works we have proposed a novel signal transmission method based on the human body medium using a Mach-Zehnder electro-optical (EO) sensor. In this paper, we present a signal transmission system based on the proposed method, which consists of a transmitter, a Mach-Zehnder EO sensor and a corresponding receiving circuit. Meanwhile, in order to verify the frequency response properties and determine the suitable parameters of the developed system, in-vivo measurements have been implemented under conditions of different carrier frequencies, baseband frequencies and signal transmission paths. Results indicate that the proposed system will help to achieve reliable and high speed signal transmission of BSN based on the human body medium.

Wearable Smart System for Visually Impaired People

OpenAIRE

Ali Jasim Ramadhan

2018-01-01

In this paper, we present a wearable smart system to help visually impaired persons (VIPs) walk by themselves through the streets, navigate in public places, and seek assistance. The main components of the system are a microcontroller board, various sensors, cellular communication and GPS modules, and a solar panel. The system employs a set of sensors to track the path and alert the user of obstacles in front of them. The user is alerted by a sound emitted through a buzzer and by vibrations o...

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.

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.

Nano-enabled sensors, electronics and energy source on polymer, paper and thread substrates

Science.gov (United States)

Mostafalu, Pooria

Over the past decades, design and development of portable devices for monitoring of biomarkers especially for at risk patients is receiving considerable attention. These devices are either single use diagnostic platforms, wearable on body or on fabric, or they are implanted close to the tissue and organ that it monitors and cures. Sensors, energy sources, and data acquisition devices are the main components of a such monitoring platform. Sensors collect the information using bio-recognition tools such as enzymes and antibodies. Then, the transducers (electrodes, fluorophore, etc) convert it to the appropriate format, for instance electrical and optical signals. After that, data acquisition system amplifies and digitizes the signal and transfers the data to the recording instruments for further processing. Moreover, energy sources are necessary for powering the sensors and electronics. In wearable and implantable applications, these devices need to be flexible, light weight and biocompatible, and their performance should be similar to their rigid counterparts. In this dissertation we address these requirement for wearable and implantable devices. We showed integrated sensors, electronics, and energy sources on flexible polymers, paper, and thread. These devices provide many advantages for monitoring of the physiological condition of a patient and treatment accordingly. Real-time capability of the platform was enabled using wireless telemetry. One of the major innovations of this dissertation is the use of thread as a substrate for making medical diagnostic devices. While conventional substrates (glass, silicon, polyimide, PDMS etc) hold great promise for making wearable and implantable devices, their overall structure and form has remained essentially two dimensional, limiting their function to tissue surfaces such as skin. However, the ability to integrate functional components such as sensors, actuators, and electronics in a way that they penetrate multiple layers

A wearable wireless ECG monitoring system with dynamic transmission power control for long-term homecare.

Science.gov (United States)

Wang, Yishan; Doleschel, Sammy; Wunderlich, Ralf; Heinen, Stefan

2015-03-01

This paper presents a wearable wireless ECG monitoring system based on novel 3-Lead electrode placements for long-term homecare. The experiment for novel 3-Lead electrode placements is carried out, and the results show that the distance between limb electrodes can be significantly reduced. Based on the new electrode position, a small size sensor node, which is powered by a rechargeable battery, is designed to detect, amplify, filter and transmit the ECG signals. The coordinator receives the data and sends it to PC. Finally the signals are displayed on the GUI. In order to control the power consumption of sensor node, a dynamic power adjustment method is applied to automatically adjust the transmission power of the sensor node according to the received signal strength indicator (RSSI), which is related to the distance and obstacle between sensor node and coordinator. The system is evaluated when the user, who wears the sensor, is walking and running. A promising performance is achieved even under body motion. The power consumption can be significantly reduced with this dynamic power adjustment method.

NATURAL USER INTERFACE SENSORS FOR HUMAN BODY MEASUREMENT

Directory of Open Access Journals (Sweden)

J. Boehm

2012-08-01

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

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.

Analysis of Public Datasets for Wearable Fall Detection Systems.

Science.gov (United States)

Casilari, Eduardo; Santoyo-Ramón, José-Antonio; Cano-García, José-Manuel

2017-06-27

Due to the boom of wireless handheld devices such as smartwatches and smartphones, wearable Fall Detection Systems (FDSs) have become a major focus of attention among the research community during the last years. The effectiveness of a wearable FDS must be contrasted against a wide variety of measurements obtained from inertial sensors during the occurrence of falls and Activities of Daily Living (ADLs). In this regard, the access to public databases constitutes the basis for an open and systematic assessment of fall detection techniques. This paper reviews and appraises twelve existing available data repositories containing measurements of ADLs and emulated falls envisaged for the evaluation of fall detection algorithms in wearable FDSs. The analysis of the found datasets is performed in a comprehensive way, taking into account the multiple factors involved in the definition of the testbeds deployed for the generation of the mobility samples. The study of the traces brings to light the lack of a common experimental benchmarking procedure and, consequently, the large heterogeneity of the datasets from a number of perspectives (length and number of samples, typology of the emulated falls and ADLs, characteristics of the test subjects, features and positions of the sensors, etc.). Concerning this, the statistical analysis of the samples reveals the impact of the sensor range on the reliability of the traces. In addition, the study evidences the importance of the selection of the ADLs and the need of categorizing the ADLs depending on the intensity of the movements in order to evaluate the capability of a certain detection algorithm to discriminate falls from ADLs.

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

Science.gov (United States)

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

2005-01-01

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

Real-Time Classification of Patients with Balance Disorders vs. Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor

Directory of Open Access Journals (Sweden)

Bhargava Teja Nukala

2016-11-01

Full Text Available Gait analysis using wearable wireless sensors can be an economical, convenient and effective way to provide diagnostic and clinical information for various health-related issues. In this work, our custom designed low-cost wireless gait analysis sensor that contains a basic inertial measurement unit (IMU was used to collect the gait data for four patients diagnosed with balance disorders and additionally three normal subjects, each performing the Dynamic Gait Index (DGI tests while wearing the custom wireless gait analysis sensor (WGAS. The small WGAS includes a tri-axial accelerometer integrated circuit (IC, two gyroscopes ICs and a Texas Instruments (TI MSP430 microcontroller and is worn by each subject at the T4 position during the DGI tests. The raw gait data are wirelessly transmitted from the WGAS to a near-by PC for real-time gait data collection and analysis. In order to perform successful classification of patients vs. normal subjects, we used several different classification algorithms, such as the back propagation artificial neural network (BP-ANN, support vector machine (SVM, k-nearest neighbors (KNN and binary decision trees (BDT, based on features extracted from the raw gait data of the gyroscopes and accelerometers. When the range was used as the input feature, the overall classification accuracy obtained is 100% with BP-ANN, 98% with SVM, 96% with KNN and 94% using BDT. Similar high classification accuracy results were also achieved when the standard deviation or other values were used as input features to these classifiers. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real time using various classifiers, the success of which may eventually lead to accurate and objective diagnosis of abnormal human gaits and their underlying etiologies in the future, as more patient data are being collected.

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

Science.gov (United States)

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

2018-01-10

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

Towards the development of a wearable Electrical Impedance Tomography system: A study about the suitability of a low power bioimpedance front-end.

Science.gov (United States)

Menolotto, Matteo; Rossi, Stefano; Dario, Paolo; Della Torre, Luigi

2015-01-01

Wearable systems for remote monitoring of physiological parameter are ready to evolve towards wearable imaging systems. The Electrical Impedance Tomography (EIT) allows the non-invasive investigation of the internal body structure. The characteristics of this low-resolution and low-cost technique match perfectly with the concept of a wearable imaging device. On the other hand low power consumption, which is a mandatory requirement for wearable systems, is not usually discussed for standard EIT applications. In this work a previously developed low power architecture for a wearable bioimpedance sensor is applied to EIT acquisition and reconstruction, to evaluate the impact on the image of the limited signal to noise ratio (SNR), caused by low power design. Some anatomical models of the chest, with increasing geometric complexity, were developed, in order to evaluate and calibrate, through simulations, the parameters of the reconstruction algorithms provided by Electrical Impedance Diffuse Optical Reconstruction Software (EIDORS) project. The simulation results were compared with experimental measurements taken with our bioimpedance device on a phantom reproducing chest tissues properties. The comparison was both qualitative and quantitative through the application of suitable figures of merit; in this way the impact of the noise of the low power front-end on the image quality was assessed. The comparison between simulation and measurement results demonstrated that, despite the limited SNR, the device is accurate enough to be used for the development of an EIT based imaging wearable system.

Wearable textile platform for assessing stroke patient treatment in daily life conditions

Directory of Open Access Journals (Sweden)

Federico eLorussi

2016-03-01

Full Text Available Monitoring physical activities during post-stroke rehabilitation in daily life may help physicians to optimize and tailor the training program for patients. The European research project INTERACTION (FP7-ICT-2011-7-287351 evaluated motor capabilities in stroke patients during the recovery treatment period. We developed wearable sensing platform based on the sensor fusion among inertial, knitted piezoresistive sensors and textile EMG electrodes . The device was conceived in modular form and consists of a separate shirt, trousers, glove and shoe. Thanks to the novel fusion approach it has been possible to develop a model for the shoulder taking into account the scapulo-thoracic joint of the scapular girdle, considerably improving the estimation of the hand position in reaching activities. In order to minimize the sensor set used to monitor gait, a single inertial sensor fused with a textile goniometer proved to reconstruct the orientation of all the body segments of the leg. Finally, the sensing glove, endowed with three textile goniometers and three force sensors showed good capabilities in the reconstruction of grasping activities and evaluating the interaction of the hand with the environment, according to the project specifications. This paper reports on the design and the technical evaluation of the performance of the sensing platform, tested on healthy subjects.

Augmented reality som wearable technology

DEFF Research Database (Denmark)

Rahn, Annette

“How Augmented reality can facilitate learning in visualizing human anatomy “ At this station I demonstrate how Augmented reality can be used to visualize the human lungs in situ and as a wearable technology which establish connection between body, image and technology in education. I will show...

A Dual-Core System Solution for Wearable Health Monitors

NARCIS (Netherlands)

Santana Arnaiz, O.A.; Bouwens, F.; Huisken, J.A.; De Groot, H.; Bennebroek, M.T.; Van Meerbergen, J.L.; Abbo, A.A.; Fraboulet, A.

2011-01-01

This paper presents a system design study for wearable sensor devices intended for healthcare and lifestyle applications based on ECG,EEG and activity monitoring. In order to meet the low-power requirement of these applications, a dual-core signal processing system is proposed which combines an

Wearable System for Acquisition and Monitoring of Biological Signals

Science.gov (United States)

Piccinini, D. J.; Andino, N. B.; Ponce, S. D.; Roberti, MA; López, y. N.

2016-04-01

This paper presents a modular, wearable system for acquisition and wireless transmission of biological signals. Configurable slaves for different signals (such as ECG, EMG, inertial sensors, and temperature) based in the ADS1294 Medical Analog Front End are connected to a Master, based in the CC3200 microcontroller, both from Texas Instruments. The slaves are configurable according to the specific application, providing versatility to the wearable system. The battery consumption is reduced, through a couple of Li-ion batteries and the circuit has also a battery charger. A custom made box was designed and fabricated in a 3D printer, preserving the requirements of low cost, low weight and safety recommendations.

Large displacement haptic stimulus actuator using piezoelectric pump for wearable devices.

Science.gov (United States)

Kodama, Taisuke; Izumi, Shintaro; Masaki, Kana; Kawaguchi, Hiroshi; Maenaka, Kazusuke; Yoshimoto, Masahiko

2015-08-01

Recently, given Japan's aging society background, wearable healthcare devices have increasingly attracted attention. Many devices have been developed, but most devices have only a sensing function. To expand the application area of wearable healthcare devices, an interactive communication function with the human body is required using an actuator. For example, a device must be useful for medication assistance, predictive alerts of a disease such as arrhythmia, and exercise. In this work, a haptic stimulus actuator using a piezoelectric pump is proposed to realize a large displacement in wearable devices. The proposed actuator drives tactile sensation of the human body. The measurement results obtained using a sensory examination demonstrate that the proposed actuator can generate sufficient stimuli even if adhered to the chest, which has fewer tactile receptors than either the fingertip or wrist.

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…

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.

Quantitative Analysis of Motor Status in Parkinson’s Disease Using Wearable Devices: From Methodological Considerations to Problems in Clinical Applications

Directory of Open Access Journals (Sweden)

Masahiko Suzuki

2017-01-01

Full Text Available Long-term and objective monitoring is necessary for full assessment of the condition of patients with Parkinson’s disease (PD. Recent advances in biotechnology have seen the development of various types of wearable (body-worn sensor systems. By using accelerometers and gyroscopes, these devices can quantify motor abnormalities, including decreased activity and gait disturbances, as well as nonmotor signs, such as sleep disturbances and autonomic dysfunctions in PD. This review discusses methodological problems inherent in wearable devices. Until now, analysis of the mean values of motion-induced signals on a particular day has been widely applied in the clinical management of PD patients. On the other hand, the reliability of these devices to detect various events, such as freezing of gait and dyskinesia, has been less than satisfactory. Quantification of disease-specific changes rather than nonspecific changes is necessary.

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.

Analysis of Public Datasets for Wearable Fall Detection Systems

Directory of Open Access Journals (Sweden)

Eduardo Casilari

2017-06-01

Full Text Available Due to the boom of wireless handheld devices such as smartwatches and smartphones, wearable Fall Detection Systems (FDSs have become a major focus of attention among the research community during the last years. The effectiveness of a wearable FDS must be contrasted against a wide variety of measurements obtained from inertial sensors during the occurrence of falls and Activities of Daily Living (ADLs. In this regard, the access to public databases constitutes the basis for an open and systematic assessment of fall detection techniques. This paper reviews and appraises twelve existing available data repositories containing measurements of ADLs and emulated falls envisaged for the evaluation of fall detection algorithms in wearable FDSs. The analysis of the found datasets is performed in a comprehensive way, taking into account the multiple factors involved in the definition of the testbeds deployed for the generation of the mobility samples. The study of the traces brings to light the lack of a common experimental benchmarking procedure and, consequently, the large heterogeneity of the datasets from a number of perspectives (length and number of samples, typology of the emulated falls and ADLs, characteristics of the test subjects, features and positions of the sensors, etc.. Concerning this, the statistical analysis of the samples reveals the impact of the sensor range on the reliability of the traces. In addition, the study evidences the importance of the selection of the ADLs and the need of categorizing the ADLs depending on the intensity of the movements in order to evaluate the capability of a certain detection algorithm to discriminate falls from ADLs.

Stretchable, Twisted Conductive Microtubules for Wearable Computing, Robotics, Electronics, and Healthcare

OpenAIRE

Thanh Nho Do; Yon Visell

2017-01-01

Stretchable and flexible multifunctional electronic components, including sensors and actuators, have received increasing attention in robotics, electronics, wearable, and healthcare applications. Despite advances, it has remained challenging to design analogs of many electronic components to be highly stretchable, to be efficient to fabricate, and to provide control over electronic performance. Here, we describe highly elastic sensors and interconnects formed from thin, twisted conductive mi...

SPHERE's 'Dress Sense' a Multi-disciplinary approach to designing wearable technology to improve health

Directory of Open Access Journals (Sweden)

Amelia Martin

2015-10-01

Full Text Available Background Healthcare systems face financial challenges, with a greater number of the population living longer with long term conditions. In order to deliver safe, high quality care within these constraints, we must place more emphasis on preventing illness than treating illness. We can use the science of behaviour change, health, and technology to create solutions to these problems. An example of innovation in this field include SPHERE’s multidisciplinary project entitled ‘Dress Sense’, with the aim to create a piece of wearable technology to improve health. Description This competition was run by SPHERE (Sensor Platform for HEalthcare in a Residential Environment which focuses on developing sensor systems to monitor health and wellbeing in the home. The teams comprised of computer scientists, medical professionals, students and school children, engineers, fashion designers and textile artists. They were provided with a kit including Arduino software, sensors and other items to make wearable computers. There were no constraints on what they could produce, except for a deadline of 3 weeks. The end project was judged according to the innovation and usefulness of the overall concept; the functionality, usability and desirability (aesthetics of the constructed device and the multi-displinary component. The idea was to foster open innovation in order to share ideas between specialities. Conclusions Ideas included tackling diabetic medical adherence through a wearable band to sensors woven into socks to prevent deep vein thrombosis. The winning concept was, ‘Yo’ a support system to aid cognitive behaviour therapy, through wearable products and accessories, to help those who begin CBT, stay motivated to engage effectively with therapy. This project highlighs how through multi-disciplinary collaboration and including the younger generation, innovative effective ideas can be found to harness digital technology to improve health.

Recyclable Nonfunctionalized Paper-Based Ultralow-Cost Wearable Health Monitoring System

KAUST Repository

Nassar, Joanna M.; Mishra, Kush; Lau, Kirklann; Aguirre-Pablo, Andres A.; Hussain, Muhammad Mustafa

2017-01-01

A wearable health monitor using low-cost and recyclable paper continuously supervises and assesses body vital conditions simultaneously and in real time, such as blood pressure, heart rate, body temperature, and skin hydration. The affordability

Wearable Photoplethysmographic Sensors—Past and Present

OpenAIRE

Toshiyo Tamura; Yuka Maeda; Masaki Sekine; Masaki Yoshida

2014-01-01

Photoplethysmography (PPG) technology has been used to develop small, wearable, pulse rate sensors. These devices, consisting of infrared light-emitting diodes (LEDs) and photodetectors, offer a simple, reliable, low-cost means of monitoring the pulse rate noninvasively. Recent advances in optical technology have facilitated the use of high-intensity green LEDs for PPG, increasing the adoption of this measurement technique. In this review, we briefly present the history of PPG and recent deve...

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.

Soft pneumatic actuator skin with piezoelectric sensors for vibrotactile feedback

Directory of Open Access Journals (Sweden)

Harshal Arun Sonar

2016-01-01

Full Text Available The latest wearable technologies demand more intuitive and sophisticated interfaces for communication, sensing, and feedback closer to the body. Evidently, such interfaces require flexibility and conformity without losing their functionality even on rigid surfaces. Although there has been various research efforts in creating tactile feedback to improve various haptic interfaces and master-slave manipulators, we are yet to see a comprehensive device that can both supply vibratory actuation and tactile sensing. This paper describes a soft pneumatic actuator (SPA based, SPA-skin prototype that allows bidirectional tactile information transfer to facilitate simpler and responsive wearable interface. We describe the design and fabrication of a 1.4 mm-thick vibratory SPA - skin that is integrated with piezoelectric sensors. We examine in detail the mechanical performance compared to the SPA model and the sensitivity of the sensors for the application in vibrotactile feedback. Experimental findings show that this ultra-thin SPA and the unique integration process of the discrete lead zirconate titanate (PZT based piezoelectric sensors achieve high resolution of soft contact sensing as well as accurate control on vibrotactile feedback by closing the control loop.

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

Science.gov (United States)

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

2018-02-24

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

On the security of consumer wearable devices in the Internet of Things.

Science.gov (United States)

Tahir, Hasan; Tahir, Ruhma; McDonald-Maier, Klaus

2018-01-01

Miniaturization of computer hardware and the demand for network capable devices has resulted in the emergence of a new class of technology called wearable computing. Wearable devices have many purposes like lifestyle support, health monitoring, fitness monitoring, entertainment, industrial uses, and gaming. Wearable devices are hurriedly being marketed in an attempt to capture an emerging market. Owing to this, some devices do not adequately address the need for security. To enable virtualization and connectivity wearable devices sense and transmit data, therefore it is essential that the device, its data and the user are protected. In this paper the use of novel Integrated Circuit Metric (ICMetric) technology for the provision of security in wearable devices has been suggested. ICMetric technology uses the features of a device to generate an identification which is then used for the provision of cryptographic services. This paper explores how a device ICMetric can be generated by using the accelerometer and gyroscope sensor. Since wearable devices often operate in a group setting the work also focuses on generating a group identification which is then used to deliver services like authentication, confidentiality, secure admission and symmetric key generation. Experiment and simulation results prove that the scheme offers high levels of security without compromising on resource demands.

Human-Computer Interfaces for Wearable Computers: A Systematic Approach to Development and Evaluation

OpenAIRE

Witt, Hendrik

2007-01-01

The research presented in this thesis examines user interfaces for wearable computers.Wearable computers are a special kind of mobile computers that can be worn on the body. Furthermore, they integrate themselves even more seamlessly into different activities than a mobile phone or a personal digital assistant can.The thesis investigates the development and evaluation of user interfaces for wearable computers. In particular, it presents fundamental research results as well as supporting softw...

Wear is Your Mobile? Investigating Phone Carrying and Use Habits with a Wearable Device

Directory of Open Access Journals (Sweden)

Kristof eVan Laerhoven

2015-05-01

Full Text Available This article explores properties and suitability of mobile and wearable platforms for continuous activity recognition and monitoring. Mobile phones have become generic computing platforms, and even though they might not always be with the user, they are increasingly easy to develop for and have an unmatched variety of on-board sensors. Wearable units in contrast tend to be purpose-built, and require a certain degree of user adaptation, but they are increasingly used to do continuous sensing. We explore the trade-offs for both device types in a study that compares their sensor data and that explicitly examines how often these devices are being worn by the user. To this end, we have recorded a dataset from 51 participants, who were given a wrist-worn sensor and an app to be used on their Smartphone for two weeks continuously, totalling 638 days (or over 15300 hours of wearable and mobile data. Results confirm findings of previous studies from North America and show that Smartphones are on average being on their user less than 23% of the time, mostly during working hours. Just as noteworthy is the high variance in Smartphone use (in carrying, interacting with, and charging the phone among participants.

Highly sensitive strain sensors based on fragmentized carbon nanotube/polydimethylsiloxane composites

Science.gov (United States)

Gao, Yang; Fang, Xiaoliang; Tan, Jianping; Lu, Ting; Pan, Likun; Xuan, Fuzhen

2018-06-01

Wearable strain sensors based on nanomaterial/elastomer composites have potential applications in flexible electronic skin, human motion detection, human–machine interfaces, etc. In this research, a type of high performance strain sensors has been developed using fragmentized carbon nanotube/polydimethylsiloxane (CNT/PDMS) composites. The CNT/PDMS composites were ground into fragments, and a liquid-induced densification method was used to fabricate the strain sensors. The strain sensors showed high sensitivity with gauge factors (GFs) larger than 200 and a broad strain detection range up to 80%, much higher than those strain sensors based on unfragmentized CNT/PDMS composites (GF composite particles during mechanical deformation, which causes significant resistance change in the strain sensors. The strain sensors can differentiate mechanical stimuli and monitor various human body motions, such as bending of the fingers, human breathing, and blood pulsing.

Noninvasive, three-dimensional full-field body sensor for surface deformation monitoring of human body in vivo

Science.gov (United States)

Chen, Zhenning; Shao, Xinxing; He, Xiaoyuan; Wu, Jialin; Xu, Xiangyang; Zhang, Jinlin

2017-09-01

Noninvasive, three-dimensional (3-D), full-field surface deformation measurements of the human body are important for biomedical investigations. We proposed a 3-D noninvasive, full-field body sensor based on stereo digital image correlation (stereo-DIC) for surface deformation monitoring of the human body in vivo. First, by applying an improved water-transfer printing (WTP) technique to transfer optimized speckle patterns onto the skin, the body sensor was conveniently and harmlessly fabricated directly onto the human body. Then, stereo-DIC was used to achieve 3-D noncontact and noninvasive surface deformation measurements. The accuracy and efficiency of the proposed body sensor were verified and discussed by considering different complexions. Moreover, the fabrication of speckle patterns on human skin, which has always been considered a challenging problem, was shown to be feasible, effective, and harmless as a result of the improved WTP technique. An application of the proposed stereo-DIC-based body sensor was demonstrated by measuring the pulse wave velocity of human carotid artery.

Parallel Microcracks-based Ultrasensitive and Highly Stretchable Strain Sensors.

Science.gov (United States)

Amjadi, Morteza; Turan, Mehmet; Clementson, Cameron P; Sitti, Metin

2016-03-02

There is an increasing demand for flexible, skin-attachable, and wearable strain sensors due to their various potential applications. However, achieving strain sensors with both high sensitivity and high stretchability is still a grand challenge. Here, we propose highly sensitive and stretchable strain sensors based on the reversible microcrack formation in composite thin films. Controllable parallel microcracks are generated in graphite thin films coated on elastomer films. Sensors made of graphite thin films with short microcracks possess high gauge factors (maximum value of 522.6) and stretchability (ε ≥ 50%), whereas sensors with long microcracks show ultrahigh sensitivity (maximum value of 11,344) with limited stretchability (ε ≤ 50%). We demonstrate the high performance strain sensing of our sensors in both small and large strain sensing applications such as human physiological activity recognition, human body large motion capturing, vibration detection, pressure sensing, and soft robotics.

Testing and evaluation of a wearable augmented reality system for natural outdoor environments

Science.gov (United States)

Roberts, David; Menozzi, Alberico; Cook, James; Sherrill, Todd; Snarski, Stephen; Russler, Pat; Clipp, Brian; Karl, Robert; Wenger, Eric; Bennett, Matthew; Mauger, Jennifer; Church, William; Towles, Herman; MacCabe, Stephen; Webb, Jeffrey; Lupo, Jasper; Frahm, Jan-Michael; Dunn, Enrique; Leslie, Christopher; Welch, Greg

2013-05-01

This paper describes performance evaluation of a wearable augmented reality system for natural outdoor environments. Applied Research Associates (ARA), as prime integrator on the DARPA ULTRA-Vis (Urban Leader Tactical, Response, Awareness, and Visualization) program, is developing a soldier-worn system to provide intuitive `heads-up' visualization of tactically-relevant geo-registered icons. Our system combines a novel pose estimation capability, a helmet-mounted see-through display, and a wearable processing unit to accurately overlay geo-registered iconography (e.g., navigation waypoints, sensor points of interest, blue forces, aircraft) on the soldier's view of reality. We achieve accurate pose estimation through fusion of inertial, magnetic, GPS, terrain data, and computer-vision inputs. We leverage a helmet-mounted camera and custom computer vision algorithms to provide terrain-based measurements of absolute orientation (i.e., orientation of the helmet with respect to the earth). These orientation measurements, which leverage mountainous terrain horizon geometry and mission planning landmarks, enable our system to operate robustly in the presence of external and body-worn magnetic disturbances. Current field testing activities across a variety of mountainous environments indicate that we can achieve high icon geo-registration accuracy (<10mrad) using these vision-based methods.

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

DEFF Research Database (Denmark)

Jalaliniya, Shahram

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

A wearable 3D motion sensing system integrated with a Bluetooth smart phone application: A system level overview

KAUST Repository

Karimi, Muhammad Akram

2018-01-02

An era of ubiquitous motion sensing has just begun. All electronic gadgets ranging from game consoles to mobile phones have some sort of motion sensors in them. In contrast to rigid motion sensing systems, this paper presents a system level description of a wearable 3D motion sensor. The sensing mechanism is based upon well-established magnetic and inertial measurement unit (MIMU), which integrates accelerometer, gyroscope and magnetometer data. Two sensor boards have been integrated within a wearable arm sleeve to capture 3D orientation of the human arm. The sensors have been interfaced with a Bluetooth transceiver chip, which transmits data to a mobile phone app using standard Bluetooth protocol. An android mobile phone app has been developed to display the human arm motion in real time.

Mechanically Compliant Electronic Materials for Wearable Photovoltaics and Human-Machine Interfaces

Science.gov (United States)

O'Connor, Timothy Francis, III

Applications of stretchable electronic materials for human-machine interfaces are described herein. Intrinsically stretchable organic conjugated polymers and stretchable electronic composites were used to develop stretchable organic photovoltaics (OPVs), mechanically robust wearable OPVs, and human-machine interfaces for gesture recognition, American Sign Language Translation, haptic control of robots, and touch emulation for virtual reality, augmented reality, and the transmission of touch. The stretchable and wearable OPVs comprise active layers of poly-3-alkylthiophene:phenyl-C61-butyric acid methyl ester (P3AT:PCBM) and transparent conductive electrodes of poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) and devices could only be fabricated through a deep understanding of the connection between molecular structure and the co-engineering of electronic performance with mechanical resilience. The talk concludes with the use of composite piezoresistive sensors two smart glove prototypes. The first integrates stretchable strain sensors comprising a carbon-elastomer composite, a wearable microcontroller, low energy Bluetooth, and a 6-axis accelerometer/gyroscope to construct a fully functional gesture recognition glove capable of wirelessly translating American Sign Language to text on a cell phone screen. The second creates a system for the haptic control of a 3D printed robot arm, as well as the transmission of touch and temperature information.

Novel fabric pressure sensors: design, fabrication, and characterization

International Nuclear Information System (INIS)

Wang, Yangyong; Hua, Tao; Zhu, Bo; Li, Qiao; Yi, Weijing; Tao, Xiaoming

2011-01-01

Soft and pliable pressure sensors are essential elements in wearable electronics which have wide applications in modern daily lives. This paper presents a family of fabric pressure sensors made by sandwiching a piece of resistive fabric strain sensing element between two tooth-structured layers of soft elastomers. The pressure sensors are capable of measuring pressure from 0 to 2000 kPa, covering the whole range of human–machine interactions. A pressure sensitivity of up to 2.98 × 10 −3 kPa −1 was obtained. Theoretical modeling was conducted based on an energy method to predict the load–displacement relationship for various sensor configurations. By adjusting the Young's modulus of the two conversion layers, as well as the geometrical dimensions, the measurement ranges, and sensitivities of the sensors can be quantitatively determined. The sensors are being used for pressure measurements between the human body and garments, shoes, beds, and chairs

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.

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

A Wearable-Based and Markerless Human-Manipulator Interface with Feedback Mechanism and Kalman Filters

Directory of Open Access Journals (Sweden)

Ping Zhang

2015-11-01

Full Text Available The objective of this paper is to develop a novel human-manipulator interface which incorporates wearable-based and markerless tracking to interact with the continuous movements of a human operator's hand. Unlike traditional approaches, which usually include contacting devices or physical markers to track the human-limb movements, this interface enables registration of natural movement through a wireless wearable watch and a leap motion sensor. Due to sensor error and tracking failure, the measurements are not made with sufficient accuracy. Two Kalman filters are employed to compensate the noisy and incomplete measurements in real time. Furthermore, due to perceptive limitations and abnormal state signals, the operator is unable to achieve high precision and efficiency in robot manipulation; an adaptive multispace transformation method (AMT is therefore introduced, which serves as a secondary treatment. In addition, in order to allow two-way human-robot interaction, the proposed method provides a vibration feedback mechanism triggered by the wearable watch to call the operator's attention to robot collision incidents or moments where the operator's hand is in a transboundary state. This improves teleoperation.

A wearable 12-lead ECG acquisition system with fabric electrodes.

Science.gov (United States)

Haoshi Zhang; Lan Tian; Huiyang Lu; Ming Zhou; Haiqing Zou; Peng Fang; Fuan Yao; Guanglin Li

2017-07-01

Continuous electrocardiogram (ECG) monitoring is significant for prevention of heart disease and is becoming an important part of personal and family health care. In most of the existing wearable solutions, conventional metal sensors and corresponding chips are simply integrated into clothes and usually could only collect few leads of ECG signals that could not provide enough information for diagnosis of cardiac diseases such as arrhythmia and myocardial ischemia. In this study, a wearable 12-lead ECG acquisition system with fabric electrodes was developed and could simultaneously process 12 leads of ECG signals. By integrating the fabric electrodes into a T-shirt, the wearable system would provide a comfortable and convenient user interface for ECG recording. For comparison, the proposed fabric electrode and the gelled traditional metal electrodes were used to collect ECG signals on a subject, respectively. The approximate entropy (ApEn) of ECG signals from both types of electrodes were calculated. The experimental results show that the fabric electrodes could achieve similar performance as the gelled metal electrodes. This preliminary work has demonstrated that the developed ECG system with fabric electrodes could be utilized for wearable health management and telemedicine applications.

A CMOS-compatible large-scale monolithic integration of heterogeneous multi-sensors on flexible silicon for IoT applications

KAUST Repository

Nassar, Joanna M.

2017-02-07

We report CMOS technology enabled fabrication and system level integration of flexible bulk silicon (100) based multi-sensors platform which can simultaneously sense pressure, temperature, strain and humidity under various physical deformations. We also show an advanced wearable version for body vital monitoring which can enable advanced healthcare for IoT applications.

A CMOS-compatible large-scale monolithic integration of heterogeneous multi-sensors on flexible silicon for IoT applications

KAUST Repository

Nassar, Joanna M.; Sevilla, Galo T.; Velling, Seneca J.; Cordero, Marlon D.; Hussain, Muhammad Mustafa

2017-01-01

We report CMOS technology enabled fabrication and system level integration of flexible bulk silicon (100) based multi-sensors platform which can simultaneously sense pressure, temperature, strain and humidity under various physical deformations. We also show an advanced wearable version for body vital monitoring which can enable advanced healthcare for IoT applications.

Highly sensitive strain sensors based on fragmentized carbon nanotube/polydimethylsiloxane composites.

Science.gov (United States)

Gao, Yang; Fang, Xiaoliang; Tan, Jianping; Lu, Ting; Pan, Likun; Xuan, Fuzhen

2018-06-08

Wearable strain sensors based on nanomaterial/elastomer composites have potential applications in flexible electronic skin, human motion detection, human-machine interfaces, etc. In this research, a type of high performance strain sensors has been developed using fragmentized carbon nanotube/polydimethylsiloxane (CNT/PDMS) composites. The CNT/PDMS composites were ground into fragments, and a liquid-induced densification method was used to fabricate the strain sensors. The strain sensors showed high sensitivity with gauge factors (GFs) larger than 200 and a broad strain detection range up to 80%, much higher than those strain sensors based on unfragmentized CNT/PDMS composites (GF sensors is ascribed to the sliding of individual fragmentized-CNT/PDMS-composite particles during mechanical deformation, which causes significant resistance change in the strain sensors. The strain sensors can differentiate mechanical stimuli and monitor various human body motions, such as bending of the fingers, human breathing, and blood pulsing.

Optical sensor array platform based on polymer electronic devices

Science.gov (United States)

Koetse, Marc M.; Rensing, Peter A.; Sharpe, Ruben B. A.; van Heck, Gert T.; Allard, Bart A. M.; Meulendijks, Nicole N. M. M.; Kruijt, Peter G. M.; Tijdink, Marcel W. W. J.; De Zwart, René M.; Houben, René J.; Enting, Erik; van Veen, Sjaak J. J. F.; Schoo, Herman F. M.

2007-10-01

Monitoring of personal wellbeing and optimizing human performance are areas where sensors have only begun to be used. One of the reasons for this is the specific demands that these application areas put on the underlying technology and system properties. In many cases these sensors will be integrated in clothing, be worn on the skin, or may even be placed inside the body. This implies that flexibility and wearability of the systems is essential for their success. Devices based on polymer semiconductors allow for these demands since they can be fabricated with thin film technology. The use of thin film device technology allows for the fabrication of very thin sensors (e.g. integrated in food product packaging), flexible or bendable sensors in wearables, large area/distributed sensors, and intrinsically low-cost applications in disposable products. With thin film device technology a high level of integration can be achieved with parts that analyze signals, process and store data, and interact over a network. Integration of all these functions will inherently lead to better cost/performance ratios, especially if printing and other standard polymer technology such as high precision moulding is applied for the fabrication. In this paper we present an optical transmission sensor array based on polymer semiconductor devices made by thin film technology. The organic devices, light emitting diodes, photodiodes and selective medium chip, are integrated with classic electronic components. Together they form a versatile sensor platform that allows for the quantitative measurement of 100 channels and communicates wireless with a computer. The emphasis is given to the sensor principle, the design, fabrication technology and integration of the thin film devices.

A Systematic Review of Wearable Patient Monitoring Systems - Current Challenges and Opportunities for Clinical Adoption.

Science.gov (United States)

Baig, Mirza Mansoor; GholamHosseini, Hamid; Moqeem, Aasia A; Mirza, Farhaan; Lindén, Maria

2017-07-01

The aim of this review is to investigate barriers and challenges of wearable patient monitoring (WPM) solutions adopted by clinicians in acute, as well as in community, care settings. Currently, healthcare providers are coping with ever-growing healthcare challenges including an ageing population, chronic diseases, the cost of hospitalization, and the risk of medical errors. WPM systems are a potential solution for addressing some of these challenges by enabling advanced sensors, wearable technology, and secure and effective communication platforms between the clinicians and patients. A total of 791 articles were screened and 20 were selected for this review. The most common publication venue was conference proceedings (13, 54%). This review only considered recent studies published between 2015 and 2017. The identified studies involved chronic conditions (6, 30%), rehabilitation (7, 35%), cardiovascular diseases (4, 20%), falls (2, 10%) and mental health (1, 5%). Most studies focussed on the system aspects of WPM solutions including advanced sensors, wireless data collection, communication platform and clinical usability based on a specific area or disease. The current studies are progressing with localized sensor-software integration to solve a specific use-case/health area using non-scalable and 'silo' solutions. There is further work required regarding interoperability and clinical acceptance challenges. The advancement of wearable technology and possibilities of using machine learning and artificial intelligence in healthcare is a concept that has been investigated by many studies. We believe future patient monitoring and medical treatments will build upon efficient and affordable solutions of wearable technology.

Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors

Science.gov (United States)

2012-01-01

A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel; however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering. PMID:22284235

A wearable 3D motion sensing system integrated with a Bluetooth smart phone application: A system level overview

KAUST Repository

Karimi, Muhammad Akram; Shamim, Atif

2018-01-01

description of a wearable 3D motion sensor. The sensing mechanism is based upon well-established magnetic and inertial measurement unit (MIMU), which integrates accelerometer, gyroscope and magnetometer data. Two sensor boards have been integrated within a

Noninvasive, three-dimensional full-field body sensor for surface deformation monitoring of human body in vivo.

Science.gov (United States)

Chen, Zhenning; Shao, Xinxing; He, Xiaoyuan; Wu, Jialin; Xu, Xiangyang; Zhang, Jinlin

2017-09-01

Noninvasive, three-dimensional (3-D), full-field surface deformation measurements of the human body are important for biomedical investigations. We proposed a 3-D noninvasive, full-field body sensor based on stereo digital image correlation (stereo-DIC) for surface deformation monitoring of the human body in vivo. First, by applying an improved water-transfer printing (WTP) technique to transfer optimized speckle patterns onto the skin, the body sensor was conveniently and harmlessly fabricated directly onto the human body. Then, stereo-DIC was used to achieve 3-D noncontact and noninvasive surface deformation measurements. The accuracy and efficiency of the proposed body sensor were verified and discussed by considering different complexions. Moreover, the fabrication of speckle patterns on human skin, which has always been considered a challenging problem, was shown to be feasible, effective, and harmless as a result of the improved WTP technique. An application of the proposed stereo-DIC-based body sensor was demonstrated by measuring the pulse wave velocity of human carotid artery. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Stretchable, Twisted Conductive Microtubules for Wearable Computing, Robotics, Electronics, and Healthcare.

Science.gov (United States)

Do, Thanh Nho; Visell, Yon

2017-05-11

Stretchable and flexible multifunctional electronic components, including sensors and actuators, have received increasing attention in robotics, electronics, wearable, and healthcare applications. Despite advances, it has remained challenging to design analogs of many electronic components to be highly stretchable, to be efficient to fabricate, and to provide control over electronic performance. Here, we describe highly elastic sensors and interconnects formed from thin, twisted conductive microtubules. These devices consist of twisted assemblies of thin, highly stretchable (>400%) elastomer tubules filled with liquid conductor (eutectic gallium indium, EGaIn), and fabricated using a simple roller coating process. As we demonstrate, these devices can operate as multimodal sensors for strain, rotation, contact force, or contact location. We also show that, through twisting, it is possible to control their mechanical performance and electronic sensitivity. In extensive experiments, we have evaluated the capabilities of these devices, and have prototyped an array of applications in several domains of stretchable and wearable electronics. These devices provide a novel, low cost solution for high performance stretchable electronics with broad applications in industry, healthcare, and consumer electronics, to emerging product categories of high potential economic and societal significance.

Interactive balance training integrating sensor-based visual feedback of movement performance: a pilot study in older adults.

Science.gov (United States)

Schwenk, Michael; Grewal, Gurtej S; Honarvar, Bahareh; Schwenk, Stefanie; Mohler, Jane; Khalsa, Dharma S; Najafi, Bijan

2014-12-13

Wearable sensor technology can accurately measure body motion and provide incentive feedback during exercising. The aim of this pilot study was to evaluate the effectiveness and user experience of a balance training program in older adults integrating data from wearable sensors into a human-computer interface designed for interactive training. Senior living community residents (mean age 84.6) with confirmed fall risk were randomized to an intervention (IG, n = 17) or control group (CG, n = 16). The IG underwent 4 weeks (twice a week) of balance training including weight shifting and virtual obstacle crossing tasks with visual/auditory real-time joint movement feedback using wearable sensors. The CG received no intervention. Outcome measures included changes in center of mass (CoM) sway, ankle and hip joint sway measured during eyes open (EO) and eyes closed (EC) balance test at baseline and post-intervention. Ankle-hip postural coordination was quantified by a reciprocal compensatory index (RCI). Physical performance was quantified by the Alternate-Step-Test (AST), Timed-up-and-go (TUG), and gait assessment. User experience was measured by a standardized questionnaire. After the intervention sway of CoM, hip, and ankle were reduced in the IG compared to the CG during both EO and EC condition (p = .007-.042). Improvement was obtained for AST (p = .037), TUG (p = .024), fast gait speed (p = . 010), but not normal gait speed (p = .264). Effect sizes were moderate for all outcomes. RCI did not change significantly. Users expressed a positive training experience including fun, safety, and helpfulness of sensor-feedback. Results of this proof-of-concept study suggest that older adults at risk of falling can benefit from the balance training program. Study findings may help to inform future exercise interventions integrating wearable sensors for guided game-based training in home- and community environments. Future studies should evaluate the

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

Directory of Open Access Journals (Sweden)

Frédéric Li

2018-02-01

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

Wearable device for skin contact thermography: design, construction and testing

International Nuclear Information System (INIS)

Giansanti, D.; Maccioni, G.

2008-01-01

The need for wearable devices for thermal monitoring is rising. These devices could be used to continuously monitor patients for breast cancer investigation or vascular, dermatological and rheumatic disorders, in viability studies, or during physical exercise. We designed and constructed a wearable device for skin-contact thermography that uses integrated silicon sensors. The device was validated using a phantom with a dynamic bench test. The thermal resolution was greater than 0.030'C, and the spatial resolution was equal to 1.6x10-5 m'2. We also investigated the device's performance on five clinical subjects. Results of these studies showed a maximal error of less than 0.10'C in each evaluation [it

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

Science.gov (United States)

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

2016-08-01

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

Increasing fall risk awareness using wearables: A fall risk awareness protocol.

Science.gov (United States)

Danielsen, Asbjørn; Olofsen, Hans; Bremdal, Bernt Arild

2016-10-01

Each year about a third of elderly aged 65 or older experience a fall. Many of these falls may have been avoided if fall risk assessment and prevention tools where available in a daily living situation. We identify what kind of information is relevant for doing fall risk assessment and prevention using wearable sensors in a daily living environment by investigating current research, distinguishing between prospective and context-aware fall risk assessment and prevention. Based on our findings, we propose a fall risk awareness protocol as a fall prevention tool integrating both wearables and ambient sensing technology into a single platform. Copyright © 2016. Published by Elsevier Inc.

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

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

Science.gov (United States)

Mastrandrea, Rossana; Fournet, Julie; Barrat, Alain

2015-01-01

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

IBE-Lite: a lightweight identity-based cryptography for body sensor networks.

Science.gov (United States)

Tan, Chiu C; Wang, Haodong; Zhong, Sheng; Li, Qun

2009-11-01

A body sensor network (BSN) is a network of sensors deployed on a person's body for health care monitoring. Since the sensors collect personal medical data, security and privacy are important components in a BSN. In this paper, we developed IBE-Lite, a lightweight identity-based encryption suitable for sensors in a BSN. We present protocols based on IBE-Lite that balance security and privacy with accessibility and perform evaluation using experiments conducted on commercially available sensors.

Designing Wearable Personal Assistants for Surgeons: An Egocentric Approach

DEFF Research Database (Denmark)

Jalaliniya, Shahram; Pederson, Thomas

2015-01-01

The design of general-purpose wearable computers demands particular care for how human perception, cognition, and action work and work together. The authors propose a human body-and-mind centric (egocentric as opposed to device-centric) design framework and present initial findings from deploying...... it in the design of a wearable personal assistant (WPA) for orthopedic surgeons. The result is a Google Glass-based prototype system aimed at facilitating touchless interaction with x-ray images, browsing of electronic patient records (EPR) when on the move, and synchronized ad hoc remote collaboration...

Flexible, highly sensitive pressure sensor with a wide range based on graphene-silk network structure

Science.gov (United States)

Liu, Ying; Tao, Lu-Qi; Wang, Dan-Yang; Zhang, Tian-Yu; Yang, Yi; Ren, Tian-Ling

2017-03-01

In this paper, a flexible, simple-preparation, and low-cost graphene-silk pressure sensor based on soft silk substrate through thermal reduction was demonstrated. Taking silk as the support body, the device had formed a three-dimensional structure with ordered multi-layer structure. Through a simple and low-cost process technology, graphene-silk pressure sensor can achieve the sensitivity value of 0.4 kPa - 1 , and the measurement range can be as high as 140 kPa. Besides, pressure sensor can have a good combination with knitted clothing and textile product. The signal had good reproducibility in response to different pressures. Furthermore, graphene-silk pressure sensor can not only detect pressure higher than 100 kPa, but also can measure weak body signals. The characteristics of high-sensitivity, good repeatability, flexibility, and comfort for skin provide the high possibility to fit on various wearable electronics.

Noninvasive wearable sensor for indirect glucometry.

Science.gov (United States)

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

2018-04-02

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

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

Flexible carbon nanotube nanocomposite sensor for multiple physiological parameter monitoring

KAUST Repository

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

2016-01-01

The paper presents the design, development, and fabrication of a flexible and wearable sensor based on carbon nanotube nanocomposite for monitoring specific physiological parameters. Polydimethylsiloxane (PDMS) was used as the substrate with a thin layer of a nanocomposite comprising functionalized multi-walled carbon nanotubes (MWCNTs) and PDMS as electrodes. The sensor patch functionalized on strain-sensitive capacitive sensing from interdigitated electrodes which were patterned with a laser on the nanocomposite layer. The thickness of the electrode layer was optimized regarding strain and conductivity. The sensor patch was connected to a monitoring device from one end and attached to the body on the other for examining purposes. Experimental results show the capability of the sensor patch used to detect respiration and limb movements. This work is a stepping stone of the sensing system to be developed for multiple physiological parameters.

Flexible carbon nanotube nanocomposite sensor for multiple physiological parameter monitoring

KAUST Repository

Nag, Anindya

2016-10-16

The paper presents the design, development, and fabrication of a flexible and wearable sensor based on carbon nanotube nanocomposite for monitoring specific physiological parameters. Polydimethylsiloxane (PDMS) was used as the substrate with a thin layer of a nanocomposite comprising functionalized multi-walled carbon nanotubes (MWCNTs) and PDMS as electrodes. The sensor patch functionalized on strain-sensitive capacitive sensing from interdigitated electrodes which were patterned with a laser on the nanocomposite layer. The thickness of the electrode layer was optimized regarding strain and conductivity. The sensor patch was connected to a monitoring device from one end and attached to the body on the other for examining purposes. Experimental results show the capability of the sensor patch used to detect respiration and limb movements. This work is a stepping stone of the sensing system to be developed for multiple physiological parameters.

Wearable Multi-Frequency and Multi-Segment Bioelectrical Impedance Spectroscopy for Unobtrusively Tracking Body Fluid Shifts during Physical Activity in Real-Field Applications: A Preliminary Study

Directory of Open Access Journals (Sweden)

Federica Villa

2016-05-01

Full Text Available Bioelectrical Impedance Spectroscopy (BIS allows assessing the composition of body districts noninvasively and quickly, potentially providing important physiological/clinical information. However, neither portable commercial instruments nor more advanced wearable prototypes simultaneously satisfy the demanding needs of unobtrusively tracking body fluid shifts in different segments simultaneously, over a broad frequency range, for long periods and with high measurements rate. These needs are often required to evaluate exercise tests in sports or rehabilitation medicine, or to assess gravitational stresses in aerospace medicine. Therefore, the aim of this work is to present a new wearable prototype for monitoring multi-segment and multi-frequency BIS unobtrusively over long periods. Our prototype guarantees low weight, small size and low power consumption. An analog board with current-injecting and voltage-sensing electrodes across three body segments interfaces a digital board that generates square-wave current stimuli and computes impedance at 10 frequencies from 1 to 796 kHz. To evaluate the information derivable from our device, we monitored the BIS of three body segments in a volunteer before, during and after physical exercise and postural shift. We show that it can describe the dynamics of exercise-induced changes and the effect of a sit-to-stand maneuver in active and inactive muscular districts separately and simultaneously.

Real-time Human Activity Recognition using a Body Sensor Network

DEFF Research Database (Denmark)

Wang, Liang; Gu, Tao; Chen, Hanhua

2010-01-01

Real-time activity recognition using body sensor networks is an important and challenging task and it has many potential applications. In this paper, we propose a realtime, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this mo...

cHRV Uncovering Daily Stress Dynamics Using Bio-Signal from Consumer Wearables.

Science.gov (United States)

Hao, Tian; Chang, Henry; Ball, Marion; Lin, Kun; Zhu, Xinxin

2017-01-01

Knowing the dynamics of one's daily stress is essential to effective stress management in the context of smart and connected health. However, there lacks a practical and unobtrusive means to obtain real-time and longitudinal stress information. In this paper, we attempt to derive a convenient HRV-based (heart rate variability) biomarker named cHRV, which can be used to reliably reflect stress dynamics. cHRV's key advantage lies in its low maintenance and high practicality. It can be efficiently calculated only using data from photoplethysmography (PPG) sensors, the mainstream heart rate sensor embedded in most of the consumer wearables like Apple Watch. Benefiting from the proliferation of wearables, cHRV is ideal for day-to-day stress monitoring. To evaluate its feasibility and performance, we have conducted 14 in-lab controlled experiments. The result shows that the proposed cHRV has strong correlation with the stress dynamics (r > 0.95), therefore exhibits great potential for continuous stress assessment.

Temperature measurement systems in wearable electronics

Science.gov (United States)

Walczak, S.; Gołebiowski, J.

2014-08-01

The aim of this paper is to present the concept of temperature measurement system, adapted to wearable electronics applications. Temperature is one of the most commonly monitored factor in smart textiles, especially in sportswear, medical and rescue products. Depending on the application, measured temperature could be used as an initial value of alert, heating, lifesaving or analysis system. The concept of the temperature measurement multi-point system, which consists of flexible screen-printed resistive sensors, placed on the T-shirt connected with the central unit and the power supply is elaborated in the paper.

An arm wearable haptic interface for impact sensing on unmanned aerial vehicles

Science.gov (United States)

Choi, Yunshil; Hong, Seung-Chan; Lee, Jung-Ryul

2017-04-01

In this paper, an impact monitoring system using fiber Bragg grating (FBG) sensors and vibro-haptic actuators has been introduced. The system is suggested for structural health monitoring (SHM) for unmanned aerial vehicles (UAVs), by making a decision with human-robot interaction. The system is composed with two major subsystems; an on-board system equipped on UAV and an arm-wearable interface for ground pilot. The on-board system acquires impact-induced wavelength changes and performs localization process, which was developed based on arrival time calculation. The arm-wearable interface helps ground pilots to make decision about impact location themselves by stimulating their tactile-sense with motor vibration.

3D inkjet printed flexible and wearable antenna systems

KAUST Repository

Shamim, Atif

2017-12-22

With the advent of wearable sensors and internet of things (IoT), there is a new focus on electronics which can be bent so that they can be worn or mounted on non-planar objects. Moreover, there is a requirement that these electronics become extremely low cost, to the extent that they become disposable. The flexible and low cost aspects can be addressed by adapting additive manufacturing technologies such as inkjet printing and 3D printing. This paper presents inkjet printing as an emerging new technique to realize low cost, flexible and wearable antenna systems. The ability of inkjet printing to realize electronics on unconventional mediums such as plastics, papers, and textiles has opened up a plethora of new applications. A variety of antennas such as wide-band, multiband, and wearable, etc, which have been realized through additive manufacturing techniques are shown. Many system level examples are also shown, primarily for wireless sensing applications. The promising results of these designs indicate that the day when electronics can be printed like newspapers and magazines through roll-to-roll and reel-to-reel printing is not far away.

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

The Use of Body Worn Sensors for Detecting the Vibrations Acting on the Lower Back in Alpine Ski Racing

Directory of Open Access Journals (Sweden)

Jörg Spörri

2017-07-01

Full Text Available This study explored the use of body worn sensors to evaluate the vibrations that act on the human body in alpine ski racing from a general and a back overuse injury prevention perspective. In the course of a biomechanical field experiment, six male European Cup-level athletes each performed two runs on a typical giant slalom (GS and slalom (SL course, resulting in a total of 192 analyzed turns. Three-dimensional accelerations were measured by six inertial measurement units placed on the right and left shanks, right and left thighs, sacrum, and sternum. Based on these data, power spectral density (PSD; i.e., the signal's power distribution over frequency was determined for all segments analyzed. Additionally, as a measure expressing the severity of vibration exposure, root-mean-square (RMS acceleration acting on the lower back was calculated based on the inertial acceleration along the sacrum's longitudinal axis. In both GS and SL skiing, the PSD values of the vibrations acting at the shank were found to be largest for frequencies below 30 Hz. While being transmitted through the body, these vibrations were successively attenuated by the knee and hip joint. At the lower back (i.e., sacrum sensor, PSD values were especially pronounced for frequencies between 4 and 10 Hz, whereas a corresponding comparison between GS and SL revealed higher PSD values and larger RMS values for GS. Because vibrations in this particular range (i.e., 4 to 10 Hz include the spine's resonant frequency and are known to increase the risk of structural deteriorations/abnormalities of the spine, they may be considered potential components of mechanisms leading to overuse injuries of the back in alpine ski racing. Accordingly, any measure to control and/or reduce such skiing-related vibrations to a minimum should be recognized and applied. In this connection, wearable sensor technologies might help to better monitor and manage the overall back overuse-relevant vibration

Energy Harvesting Based Body Area Networks for Smart Health.

Science.gov (United States)

Hao, Yixue; Peng, Limei; Lu, Huimin; Hassan, Mohammad Mehedi; Alamri, Atif

2017-07-10

Body area networks (BANs) are configured with a great number of ultra-low power consumption wearable devices, which constantly monitor physiological signals of the human body and thus realize intelligent monitoring. However, the collection and transfer of human body signals consume energy, and considering the comfort demand of wearable devices, both the size and the capacity of a wearable device's battery are limited. Thus, minimizing the energy consumption of wearable devices and optimizing the BAN energy efficiency is still a challenging problem. Therefore, in this paper, we propose an energy harvesting-based BAN for smart health and discuss an optimal resource allocation scheme to improve BAN energy efficiency. Specifically, firstly, considering energy harvesting in a BAN and the time limits of human body signal transfer, we formulate the energy efficiency optimization problem of time division for wireless energy transfer and wireless information transfer. Secondly, we convert the optimization problem into a convex optimization problem under a linear constraint and propose a closed-form solution to the problem. Finally, simulation results proved that when the size of data acquired by the wearable devices is small, the proportion of energy consumed by the circuit and signal acquisition of the wearable devices is big, and when the size of data acquired by the wearable devices is big, the energy consumed by the signal transfer of the wearable device is decisive.

A wearable “electronic patch” for wireless continuous monitoring of chronically diseased patients

DEFF Research Database (Denmark)

Haahr, Rasmus Grønbek; Duun, Sune; Thomsen, Erik Vilain

2008-01-01

We present a wearable health system (WHS) for non-invasive and wireless monitoring of physiological signals. The system is made as an electronic patch where sensors, low power electronics, and radio communication are integrated in an adhesive material of hydrocolloid polymer making it a sticking...

Body/bone-marrow differential-temperature sensor

Science.gov (United States)

Anselmo, V. J.; Berdahl, C. M.

1978-01-01

Differential-temperature sensor developed to compare bone-marrow and body temperature in leukemia patients uses single stable amplifier to monitor temperature difference recorded by thermocouples. Errors are reduced by referencing temperatures to each other, not to separate calibration points.

Estimation and harvesting of human heat power for wearable electronic devices