Pub Date : 2017-05-09DOI: 10.1109/BSN.2017.7936011
B. Rosa, Guang-Zhong Yang
Wearable technology has become ubiquitous in recent years due to the miniaturization of circuit electronics and advances in smart materials that can conform to the requirements posed by the human body, behaviour and experience. Sensors of this type are found attached almost to every body segment, capable of delivering signals even in harsh activity scenarios. The reliability and relevance of the physiological data retrieved by wearables have yet to surpass the conventional technologies in the healthcare system today. In this paper we present a small device incorporated inside an headphone set that continuously monitors the ECG, impedance and acceleration of the head. As opposed to most biometric sensors, ECG measurement relies on non-optical methods by capturing the electrical potential around the ear in both sides of the head, whereas impedance monitoring involves AC stimulation instead of DC, the latter commonly involved in skin galvanic response estimation. Signal processing of impedance parameters is performed in situ using a fast variant of the Discrete Fourier Transform in order to save computational resources and power expenditure from a microcontroller equipped with Bluetooth Low Energy. Applications that can benefit from this device include cardiovascular and stress level assessment of individuals for whom an hearable is a requirement for work or leisure.
{"title":"Smart wireless headphone for cardiovascular and stress monitoring","authors":"B. Rosa, Guang-Zhong Yang","doi":"10.1109/BSN.2017.7936011","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936011","url":null,"abstract":"Wearable technology has become ubiquitous in recent years due to the miniaturization of circuit electronics and advances in smart materials that can conform to the requirements posed by the human body, behaviour and experience. Sensors of this type are found attached almost to every body segment, capable of delivering signals even in harsh activity scenarios. The reliability and relevance of the physiological data retrieved by wearables have yet to surpass the conventional technologies in the healthcare system today. In this paper we present a small device incorporated inside an headphone set that continuously monitors the ECG, impedance and acceleration of the head. As opposed to most biometric sensors, ECG measurement relies on non-optical methods by capturing the electrical potential around the ear in both sides of the head, whereas impedance monitoring involves AC stimulation instead of DC, the latter commonly involved in skin galvanic response estimation. Signal processing of impedance parameters is performed in situ using a fast variant of the Discrete Fourier Transform in order to save computational resources and power expenditure from a microcontroller equipped with Bluetooth Low Energy. Applications that can benefit from this device include cardiovascular and stress level assessment of individuals for whom an hearable is a requirement for work or leisure.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127470954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-09DOI: 10.1109/BSN.2017.7936004
Marc Hesse, A. Krause, Ludwig Vogel, B. Chamadiya, Michael Schilling, T. Schack, T. Jungeblut
The connected chair is part of the Supportive Personal Coach in the KogniHome project, which offers guided fitness training, relaxation, and assistive functions. The chair comes with integrated sensors, actuators, control logic and wireless transceiver. The sensors are able to measure respiration and heart rate as well as the user's actions. The actuators are used to adjust the chair to the actual user's needs and the transceiver is used to connect wireless sensor nodes and to exchange data with a base station. Additional value is generated by connecting the chair to the smart home environment, which enables and expands novel features and applications.
{"title":"A connected chair as part of a smart home environment","authors":"Marc Hesse, A. Krause, Ludwig Vogel, B. Chamadiya, Michael Schilling, T. Schack, T. Jungeblut","doi":"10.1109/BSN.2017.7936004","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936004","url":null,"abstract":"The connected chair is part of the Supportive Personal Coach in the KogniHome project, which offers guided fitness training, relaxation, and assistive functions. The chair comes with integrated sensors, actuators, control logic and wireless transceiver. The sensors are able to measure respiration and heart rate as well as the user's actions. The actuators are used to adjust the chair to the actual user's needs and the transceiver is used to connect wireless sensor nodes and to exchange data with a base station. Additional value is generated by connecting the chair to the smart home environment, which enables and expands novel features and applications.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133652891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-09DOI: 10.1109/BSN.2017.7936005
Diana P. Tobón, Srinivasan Jayaraman, T. Falk
Wearable device usage is burgeoning, with representative applications ranging from patient/athelete monitoring to stress/fatigue identification to the so-called quantified self movement. Typically, cardiac information is monitored via electrocardiograms (ECG) and information such as heart rate (HR) and heart rate variability (HRV) are used as key health-related metrics. With many wearable devices, however, lower quality sensors are used, thus resulting in devices that are highly sensible to artifacts due to e.g., user's movement. The introduced artifacts hamper HR/HRV analyses, thus ECG enhancement has been the focus of recent research. Existing enhancement algorithms, however, do not perform well in very noisy conditions, as well as add additional computational processing to already battery-hungry wearable applications. Here, we propose to overcome these limitations by describing a new ECG signal representation called the modulation spectrum. By quantifying the rate-of-change of ECG spectral components, signal and artifactual components become separable, thus allowing for accurate HR and HRV measurement from the noisy signal, even in very extreme conditions typically seen in athletic performance training. The proposed MD-HRV (modulation-domain HRV) metric is tested with noise-corrupted synthetic ECG signals and is compared to ‘true’ HRV values obtained from the clean signals. Experimental results show the proposed metric significantly outperforming conventional HRV indices computed on both the noisy, as well as enhanced ECG signals processed by a state-of-the-art wavelet-based algorithm. The obtained findings suggest that the proposed metric is well suited for wearable applications, particularly those involved with intense movement (e.g., in elite athletic training).
{"title":"Improved heart rate variability measurement based on modulation spectral processing of noisy electrocardiogram signals","authors":"Diana P. Tobón, Srinivasan Jayaraman, T. Falk","doi":"10.1109/BSN.2017.7936005","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936005","url":null,"abstract":"Wearable device usage is burgeoning, with representative applications ranging from patient/athelete monitoring to stress/fatigue identification to the so-called quantified self movement. Typically, cardiac information is monitored via electrocardiograms (ECG) and information such as heart rate (HR) and heart rate variability (HRV) are used as key health-related metrics. With many wearable devices, however, lower quality sensors are used, thus resulting in devices that are highly sensible to artifacts due to e.g., user's movement. The introduced artifacts hamper HR/HRV analyses, thus ECG enhancement has been the focus of recent research. Existing enhancement algorithms, however, do not perform well in very noisy conditions, as well as add additional computational processing to already battery-hungry wearable applications. Here, we propose to overcome these limitations by describing a new ECG signal representation called the modulation spectrum. By quantifying the rate-of-change of ECG spectral components, signal and artifactual components become separable, thus allowing for accurate HR and HRV measurement from the noisy signal, even in very extreme conditions typically seen in athletic performance training. The proposed MD-HRV (modulation-domain HRV) metric is tested with noise-corrupted synthetic ECG signals and is compared to ‘true’ HRV values obtained from the clean signals. Experimental results show the proposed metric significantly outperforming conventional HRV indices computed on both the noisy, as well as enhanced ECG signals processed by a state-of-the-art wavelet-based algorithm. The obtained findings suggest that the proposed metric is well suited for wearable applications, particularly those involved with intense movement (e.g., in elite athletic training).","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116059299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-09DOI: 10.1109/BSN.2017.7936017
Ruoxi Yu, T. Mak, Ruikai Zhang, S. Wong, Yali Zheng, J. Lau, Carmen C. Y. Poon
Body sensors are now commonly used as ingestible, wearable and implantable devices for clinical diagnosis and continuous physiological monitoring. Nevertheless, they usually have limited resources. Recent advancements in technologies provide a possible solution to mitigate the resource limitation of these devices by connecting them with mobile devices and cloud services. To evaluate the feasibility of the cloud-enabled body sensor networks, this paper presents simulation results on testing the feasibility of 24-hour operating time and concurrent user support for the cloud-enabled applications.
{"title":"Smart healthcare: Cloud-enabled body sensor networks","authors":"Ruoxi Yu, T. Mak, Ruikai Zhang, S. Wong, Yali Zheng, J. Lau, Carmen C. Y. Poon","doi":"10.1109/BSN.2017.7936017","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936017","url":null,"abstract":"Body sensors are now commonly used as ingestible, wearable and implantable devices for clinical diagnosis and continuous physiological monitoring. Nevertheless, they usually have limited resources. Recent advancements in technologies provide a possible solution to mitigate the resource limitation of these devices by connecting them with mobile devices and cloud services. To evaluate the feasibility of the cloud-enabled body sensor networks, this paper presents simulation results on testing the feasibility of 24-hour operating time and concurrent user support for the cloud-enabled applications.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126568237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-09DOI: 10.1109/BSN.2017.7935994
Sebastian Scheurer, Salvatore Tedesco, Kenneth N. Brown, B. O’flynn
Every year over 75 000 firefighters are injured and 159 die in the line of duty. Some of these accidents could be averted if first response team leaders had better information about the situation on the ground. The SAFESENS project is developing a novel monitoring system for first responders designed to provide response team leaders with timely and reliable information about their firefighters' status during operations, based on data from wireless inertial measurement units. In this paper we investigate if Gradient Boosted Trees (GBT) could be used for recognising 17 activities, selected in consultation with first responders, from inertial data. By arranging these into more general groups we generate three additional classification problems which are used for comparing GBT with k-Nearest Neighbours (kNN) and Support Vector Machines (SVM). The results show that GBT outperforms both kNN and SVM for three of these four problems with a mean absolute error of less than 7%, which is distributed more evenly across the target activities than that from either kNN or SVM.
{"title":"Human activity recognition for emergency first responders via body-worn inertial sensors","authors":"Sebastian Scheurer, Salvatore Tedesco, Kenneth N. Brown, B. O’flynn","doi":"10.1109/BSN.2017.7935994","DOIUrl":"https://doi.org/10.1109/BSN.2017.7935994","url":null,"abstract":"Every year over 75 000 firefighters are injured and 159 die in the line of duty. Some of these accidents could be averted if first response team leaders had better information about the situation on the ground. The SAFESENS project is developing a novel monitoring system for first responders designed to provide response team leaders with timely and reliable information about their firefighters' status during operations, based on data from wireless inertial measurement units. In this paper we investigate if Gradient Boosted Trees (GBT) could be used for recognising 17 activities, selected in consultation with first responders, from inertial data. By arranging these into more general groups we generate three additional classification problems which are used for comparing GBT with k-Nearest Neighbours (kNN) and Support Vector Machines (SVM). The results show that GBT outperforms both kNN and SVM for three of these four problems with a mean absolute error of less than 7%, which is distributed more evenly across the target activities than that from either kNN or SVM.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"353 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122786929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-09DOI: 10.1109/BSN.2017.7936042
Yingnan Sun, Charence Wong, Guang-Zhong Yang, Benny P. L. Lo
With increasing popularity of wearable and Body Sensor Networks technologies, there is a growing concern on the security and data protection of such low-power pervasive devices. With very limited computational power, BSN sensors often cannot provide the necessary data protection to collect and process sensitive personal information. Since conventional network security schemes are too computationally demanding for miniaturized BSN sensors, new methods of securing BSNs have proposed, in which Biometric Cryptosystem (BCS) appears to be an effective solution. With regards to BCS security solutions, physiological traits, such as an individual's face, iris, fingerprint, electrocardiogram (ECG), and photoplethysmogram (PPG) have been widely exploited. However, behavioural traits such as gait are rarely studied. In this paper, a novel lightweight symmetric key generation scheme based on the timing information of gait is proposed. By extracting similar timing information from gait acceleration signals simultaneously from body worn sensors, symmetric keys can be generated on all the sensor nodes at the same time. Based on the characteristics of generated keys and BSNs, a fuzzy commitment based key distribution scheme is also developed to distribute the keys amongst the sensor nodes.
{"title":"Secure key generation using gait features for Body Sensor Networks","authors":"Yingnan Sun, Charence Wong, Guang-Zhong Yang, Benny P. L. Lo","doi":"10.1109/BSN.2017.7936042","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936042","url":null,"abstract":"With increasing popularity of wearable and Body Sensor Networks technologies, there is a growing concern on the security and data protection of such low-power pervasive devices. With very limited computational power, BSN sensors often cannot provide the necessary data protection to collect and process sensitive personal information. Since conventional network security schemes are too computationally demanding for miniaturized BSN sensors, new methods of securing BSNs have proposed, in which Biometric Cryptosystem (BCS) appears to be an effective solution. With regards to BCS security solutions, physiological traits, such as an individual's face, iris, fingerprint, electrocardiogram (ECG), and photoplethysmogram (PPG) have been widely exploited. However, behavioural traits such as gait are rarely studied. In this paper, a novel lightweight symmetric key generation scheme based on the timing information of gait is proposed. By extracting similar timing information from gait acceleration signals simultaneously from body worn sensors, symmetric keys can be generated on all the sensor nodes at the same time. Based on the characteristics of generated keys and BSNs, a fuzzy commitment based key distribution scheme is also developed to distribute the keys amongst the sensor nodes.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"41 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120868798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-09DOI: 10.1109/BSN.2017.7935999
Hongyu Chen, Xiao Gu, Zhenning Mei, Ke Xu, Kai Yan, Chunmei Lu, Laishuan Wang, Feng Shu, Qixin Xu, S. Oetomo, Wei Chen
A novel wearable sensor system for seizure monitoring of neonates comprised of smart clothing, video recording and cloud platform is presented. Textile electrodes and Inertial Measurement Unit (IMU) are embedded in the smart clothing to obtain ECG signal and motion signal whereby epileptic seizure detection algorithm is performed. Moreover, a video monitoring module provides real-time information about patients. The cloud platform receives the pre-processed data and enables remote monitoring, centralized signal processing and data management. Comparison with commercial instruments shows that the smart clothing is capable of acquiring high-quality signals. Pilot tests under disinfection operations at Children's Hospital of Fudan University confirm clinical feasibility of the proposed system. The scalability and modularity of the unobtrusive wearable front end and the design of system architecture based on cloud enable the whole system with great potential in clinical practice and home monitoring scenarios.
{"title":"A wearable sensor system for neonatal seizure monitoring","authors":"Hongyu Chen, Xiao Gu, Zhenning Mei, Ke Xu, Kai Yan, Chunmei Lu, Laishuan Wang, Feng Shu, Qixin Xu, S. Oetomo, Wei Chen","doi":"10.1109/BSN.2017.7935999","DOIUrl":"https://doi.org/10.1109/BSN.2017.7935999","url":null,"abstract":"A novel wearable sensor system for seizure monitoring of neonates comprised of smart clothing, video recording and cloud platform is presented. Textile electrodes and Inertial Measurement Unit (IMU) are embedded in the smart clothing to obtain ECG signal and motion signal whereby epileptic seizure detection algorithm is performed. Moreover, a video monitoring module provides real-time information about patients. The cloud platform receives the pre-processed data and enables remote monitoring, centralized signal processing and data management. Comparison with commercial instruments shows that the smart clothing is capable of acquiring high-quality signals. Pilot tests under disinfection operations at Children's Hospital of Fudan University confirm clinical feasibility of the proposed system. The scalability and modularity of the unobtrusive wearable front end and the design of system architecture based on cloud enable the whole system with great potential in clinical practice and home monitoring scenarios.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123341521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-09DOI: 10.1109/BSN.2017.7936020
Ruizhe Zhang, D. Ravì, Guang-Zhong Yang, Benny P. L. Lo
Recent studies have shown that air pollution has a negative impact on people's health, especially for patients with respiratory and cardiac diseases (e.g. COPD, asthma, ischemic heart disease). Although there are already many air quality monitoring stations in major cities, such as London, these stations are sparsely located, and the periodic collection of information is insufficient to provide the granularity needed to assess the environmental risk for an individual (e.g. to avoid exacerbation). Wearable devices, on the other hand, are more suitable in this context, providing a better estimation of the air quality in the proximity of the person. Therefore, relevant warnings and information on health risks can be provided in real-time. As a proof of concept, we have developed a wearable sensor for continuous monitoring of air quality around the user, and a preliminary study was conducted to validate the sensor and assess the air quality in London underground stations. Based on the PM2.5 (particulate matter with a diameter of 2.5 µm), temperature and location information, a model is generated for predicting the air quality of each station at different times. Our preliminary results have shown that there are significant differences in air quality among stations and metro lines. It also demonstrates that wearable sensors can provide necessary information for users to make travel arrangements that minimize their exposure to polluted air.
{"title":"A personalized air quality sensing system - a preliminary study on assessing the air quality of London underground stations","authors":"Ruizhe Zhang, D. Ravì, Guang-Zhong Yang, Benny P. L. Lo","doi":"10.1109/BSN.2017.7936020","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936020","url":null,"abstract":"Recent studies have shown that air pollution has a negative impact on people's health, especially for patients with respiratory and cardiac diseases (e.g. COPD, asthma, ischemic heart disease). Although there are already many air quality monitoring stations in major cities, such as London, these stations are sparsely located, and the periodic collection of information is insufficient to provide the granularity needed to assess the environmental risk for an individual (e.g. to avoid exacerbation). Wearable devices, on the other hand, are more suitable in this context, providing a better estimation of the air quality in the proximity of the person. Therefore, relevant warnings and information on health risks can be provided in real-time. As a proof of concept, we have developed a wearable sensor for continuous monitoring of air quality around the user, and a preliminary study was conducted to validate the sensor and assess the air quality in London underground stations. Based on the PM2.5 (particulate matter with a diameter of 2.5 µm), temperature and location information, a model is generated for predicting the air quality of each station at different times. Our preliminary results have shown that there are significant differences in air quality among stations and metro lines. It also demonstrates that wearable sensors can provide necessary information for users to make travel arrangements that minimize their exposure to polluted air.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114236452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-09DOI: 10.1109/BSN.2017.7936015
Junchao Wang, Z. Zilic, Yutian Shu
In this paper, we validate a novel wearable device for real time non-invasive hydration monitoring. An experiment was carried out on three sets of 12 subjects under various exercise intensities to identify how the received signal strength indicator (RSSI) captured with the device relates to the change in the body hydration, body water loss percentage (BWL%). A linear regression correlation R2 of 0.77 and a third degree polynomials correlation (R2=0.83) between ΔRSSI and BWL% is established based on the observation of the experiment, which implies that the hydration status can be quantized as body water loss by measuring ΔRSSI. Therefore, the technique and device is verified to be a potentially solid solution for wearable non-invasive real-time hydration monitoring device.
{"title":"Evaluation of an RF wearable device for non-invasive real-time hydration monitoring","authors":"Junchao Wang, Z. Zilic, Yutian Shu","doi":"10.1109/BSN.2017.7936015","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936015","url":null,"abstract":"In this paper, we validate a novel wearable device for real time non-invasive hydration monitoring. An experiment was carried out on three sets of 12 subjects under various exercise intensities to identify how the received signal strength indicator (RSSI) captured with the device relates to the change in the body hydration, body water loss percentage (BWL%). A linear regression correlation R2 of 0.77 and a third degree polynomials correlation (R2=0.83) between ΔRSSI and BWL% is established based on the observation of the experiment, which implies that the hydration status can be quantized as body water loss by measuring ΔRSSI. Therefore, the technique and device is verified to be a potentially solid solution for wearable non-invasive real-time hydration monitoring device.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132928549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-09DOI: 10.1109/BSN.2017.7936008
A. Thulasi, D. Bhatia, P. Balsara, S. Prasad
The future of disease diagnostics and health care wearables lies in the development of low-cost sensors that can detect minute traces of pathogens or antigens from body fluids. Developments in nanotechnology and biomedical research have already shown us that a nanosensor can be specifically tailored to detect a specific biomolecule. These sensors would allow patients to run point of care diagnostic tests, thereby saving time and cost of running clinical tests and can give early stage disease diagnosis and help physicians to provide personalized treatment. This work involves the development of a configurable electronic sensor platform that will interface with these sensors. The device is tested by quantification of glucose from sweat using a nanosensor developed in the Biomedical Microdevices and Nanotechnology Lab in the University of Texas at Dallas. The platform can be easily configured to run Electrochemical Impedance Spectroscopy based detection test for other biomolecules by using sensor tailored for it.
{"title":"Portable impedance measurement device for sweat based glucose detection","authors":"A. Thulasi, D. Bhatia, P. Balsara, S. Prasad","doi":"10.1109/BSN.2017.7936008","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936008","url":null,"abstract":"The future of disease diagnostics and health care wearables lies in the development of low-cost sensors that can detect minute traces of pathogens or antigens from body fluids. Developments in nanotechnology and biomedical research have already shown us that a nanosensor can be specifically tailored to detect a specific biomolecule. These sensors would allow patients to run point of care diagnostic tests, thereby saving time and cost of running clinical tests and can give early stage disease diagnosis and help physicians to provide personalized treatment. This work involves the development of a configurable electronic sensor platform that will interface with these sensors. The device is tested by quantification of glucose from sweat using a nanosensor developed in the Biomedical Microdevices and Nanotechnology Lab in the University of Texas at Dallas. The platform can be easily configured to run Electrochemical Impedance Spectroscopy based detection test for other biomolecules by using sensor tailored for it.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116716979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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