Pub Date : 2013-05-06DOI: 10.1109/BSN.2013.6575482
B. O’flynn, Javier Torres Sanchez, P. Angove, J. Connolly, J. Condell, K. Curran, P. Gardiner
Rheumatoid Arthritis (RA) is a disease which attacks the synovial tissue lubricating skeletal joints. This systemic condition affects the musculoskeletal system, including bones, joints, muscles and tendons that contribute to loss of function and Range of Motion (ROM). Traditional measurement of arthritis requires labour intensive personal examination by medical staff which through their objective measures may hinder the enactment and analysis of arthritis rehabilitation. This paper presents the development of a smart glove to facilitate this rehabilitative process through the integration of sensors, processors and wireless technology to empirically measure ROM. The Tyndall/University of Ulster glove uses a combination of 20 bend sensors, 16 tri-axial accelerometers and 11 force sensors to detect joint movement. All sensors are placed on a flexible PCB to provide high levels of flexibility and sensor stability. The system operation means that the glove does not require calibration for each glove wearer.
{"title":"Novel smart sensor glove for arthritis rehabiliation","authors":"B. O’flynn, Javier Torres Sanchez, P. Angove, J. Connolly, J. Condell, K. Curran, P. Gardiner","doi":"10.1109/BSN.2013.6575482","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575482","url":null,"abstract":"Rheumatoid Arthritis (RA) is a disease which attacks the synovial tissue lubricating skeletal joints. This systemic condition affects the musculoskeletal system, including bones, joints, muscles and tendons that contribute to loss of function and Range of Motion (ROM). Traditional measurement of arthritis requires labour intensive personal examination by medical staff which through their objective measures may hinder the enactment and analysis of arthritis rehabilitation. This paper presents the development of a smart glove to facilitate this rehabilitative process through the integration of sensors, processors and wireless technology to empirically measure ROM. The Tyndall/University of Ulster glove uses a combination of 20 bend sensors, 16 tri-axial accelerometers and 11 force sensors to detect joint movement. All sensors are placed on a flexible PCB to provide high levels of flexibility and sensor stability. The system operation means that the glove does not require calibration for each glove wearer.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127637020","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575487
Hua-I Chang, Chieh Chien, James Y. Xu, G. Pottie
Obtaining accurate measurements of human activities is important for a broad set of health applications. We propose a context-based hybrid decision tree classifier with a real-time portable solution for reliably classifying daily life activities and for providing instant feedback. At first, to determine user contexts, we utilize sensors typically found on smart phones or tablets to collect environment data. Then, we select different types of hybrid decision tree classifiers based on detected human context. The tree classifier can flexibly implement different decision rules at its internal nodes, and can be adapted from a population-based model when supplemented by training data for individuals. In addition, with the introduction of portable devices, the users can receive instant feedback of their current mobility status.
{"title":"Context-guided universal hybrid decision tree for activity classification","authors":"Hua-I Chang, Chieh Chien, James Y. Xu, G. Pottie","doi":"10.1109/BSN.2013.6575487","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575487","url":null,"abstract":"Obtaining accurate measurements of human activities is important for a broad set of health applications. We propose a context-based hybrid decision tree classifier with a real-time portable solution for reliably classifying daily life activities and for providing instant feedback. At first, to determine user contexts, we utilize sensors typically found on smart phones or tablets to collect environment data. Then, we select different types of hybrid decision tree classifiers based on detected human context. The tree classifier can flexibly implement different decision rules at its internal nodes, and can be adapted from a population-based model when supplemented by training data for individuals. In addition, with the introduction of portable devices, the users can receive instant feedback of their current mobility status.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"400 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133910840","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575468
S. Vaddiraju, Yan Wang, L. Qiang, D. Burgess, F. Papadimitrakopoulos
The function of implantable glucose sensor is hindered by post-implantation effects such as biofouling and negative tissue responses both of which lead to permeability reducing fibrous encapsulation. Utilization of drug-eluting composite coatings based on dexamethasone-containing poly (lactic-co-glycolic) acid (PLGA) microspheres and poly (vinyl alcohol) (PVA) hydrogel matrix has been shown to suppress inflammation over a period of 1–3 months. Herein, it is shown that these coatings provide another auxiliary venue to offset the negative effects of protein adsorption through generation of macroscopic porosity following microsphere degradation. Long-term studies in serum have indicated that, while biofouling clogs the microporosity of the hydrogel, it has been offset by the generated macroscopic porosity following microsphere degradation. This resulted in a two-fold recovery in sensor sensitivity as compared to controls. These findings suggest that the use of macroscopic porosity can reduce biofouling-induced sensitivity losses, an approach synergistic with drug-delivery based methodologies to mitigate negative tissue responses.
{"title":"Macroscopic porosity generation in outer hydrogel membranes to offset sensitivity loss in implantable glucose sensors","authors":"S. Vaddiraju, Yan Wang, L. Qiang, D. Burgess, F. Papadimitrakopoulos","doi":"10.1109/BSN.2013.6575468","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575468","url":null,"abstract":"The function of implantable glucose sensor is hindered by post-implantation effects such as biofouling and negative tissue responses both of which lead to permeability reducing fibrous encapsulation. Utilization of drug-eluting composite coatings based on dexamethasone-containing poly (lactic-co-glycolic) acid (PLGA) microspheres and poly (vinyl alcohol) (PVA) hydrogel matrix has been shown to suppress inflammation over a period of 1–3 months. Herein, it is shown that these coatings provide another auxiliary venue to offset the negative effects of protein adsorption through generation of macroscopic porosity following microsphere degradation. Long-term studies in serum have indicated that, while biofouling clogs the microporosity of the hydrogel, it has been offset by the generated macroscopic porosity following microsphere degradation. This resulted in a two-fold recovery in sensor sensitivity as compared to controls. These findings suggest that the use of macroscopic porosity can reduce biofouling-induced sensitivity losses, an approach synergistic with drug-delivery based methodologies to mitigate negative tissue responses.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132292624","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575489
Filippo Casamassima, Elisabetta Farella, L. Benini
Wireless Body Area Networks (WBANs) can take advantage of many wireless protocols. Among them, Bluetooth is a good candidate since its widespread adoption guarantees compatibility with a number of devices and significantly reduces development time. In most cases data collected from different sensors on different nodes need to be synchronized. We present a synchronization protocol that makes use of Bluetooth piconet internal clock to achieve near-millisecond accuracy with minimal radio communication overhead. Experimental results show that Bluetooth low power modes does not affect negatively accuracy, but improves it, obtaining less power consumption and higher synchronization accuracy.
{"title":"Synchronization methods for Bluetooth based WBANs","authors":"Filippo Casamassima, Elisabetta Farella, L. Benini","doi":"10.1109/BSN.2013.6575489","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575489","url":null,"abstract":"Wireless Body Area Networks (WBANs) can take advantage of many wireless protocols. Among them, Bluetooth is a good candidate since its widespread adoption guarantees compatibility with a number of devices and significantly reduces development time. In most cases data collected from different sensors on different nodes need to be synchronized. We present a synchronization protocol that makes use of Bluetooth piconet internal clock to achieve near-millisecond accuracy with minimal radio communication overhead. Experimental results show that Bluetooth low power modes does not affect negatively accuracy, but improves it, obtaining less power consumption and higher synchronization accuracy.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134396675","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575526
A. Vicente, A. Faisal
Our hands generic precision and agility is yet unmatched by technology, hence the quantitative study of its daily life kinematics is fundamental to neurology/prosthetics & robotics and creative industries. State-of-the-art solutions capturing hand movements ‘in the wild’ requires wearable body sensor networks: data gloves. Yet, fast-accurate calibration is challenging due to variability in hand anatomy and complexity of finger joints. We present here novel methods for calibration using streaming information from depth cameras (Microsoft Kinect). Our low-cost system calibrates the data glove by observing a user wiggling their hands while wearing data gloves. Using inverse kinematics we reconstruct in real-time hand configuration, enabling augmented reality by superimposing the virtual and real hand veridically. We achieve accuracies of ±5 degrees RMSE over all 21 joints, almost 20% more accurate than standard calibration methods and accurately capture touching of fingertips and thumb — our benchmark test unmatched by other calibration methods.
{"title":"Calibration of kinematic body sensor networks: Kinect-based gauging of data gloves “in the wild”","authors":"A. Vicente, A. Faisal","doi":"10.1109/BSN.2013.6575526","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575526","url":null,"abstract":"Our hands generic precision and agility is yet unmatched by technology, hence the quantitative study of its daily life kinematics is fundamental to neurology/prosthetics & robotics and creative industries. State-of-the-art solutions capturing hand movements ‘in the wild’ requires wearable body sensor networks: data gloves. Yet, fast-accurate calibration is challenging due to variability in hand anatomy and complexity of finger joints. We present here novel methods for calibration using streaming information from depth cameras (Microsoft Kinect). Our low-cost system calibrates the data glove by observing a user wiggling their hands while wearing data gloves. Using inverse kinematics we reconstruct in real-time hand configuration, enabling augmented reality by superimposing the virtual and real hand veridically. We achieve accuracies of ±5 degrees RMSE over all 21 joints, almost 20% more accurate than standard calibration methods and accurately capture touching of fingertips and thumb — our benchmark test unmatched by other calibration methods.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130349473","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575518
Y. Mendelson, D. Dao, K. Chon
Pulse oximetry is a widely accepted clinical method for noninvasive monitoring of arterial oxygen saturation and pulse rate. Significant improvements aimed at curbing motion artifacts and improving reliability in detecting sufficiently strong photoplethysmographic signals are required to reduce errant measurements before the pulse oximeter can be considered for wider mobile applications. The present work describes the development of a wearable multi-channel reflectance pulse oximeter to investigate if a motion artifact-free signal can be obtained in at least one of the multichannels at any given time. Pilot findings provided a proof of concept to support the hypothesis that photoplethysmograms acquired concurrently from independent channels in a multi-channel pulse oximeter sensor respond differently to motion artifacts, thus laying the foundation for future development of robust active noise cancellation and data fusion based algorithms to mitigate the effects of motion artifacts.
{"title":"Multi-channel pulse oximetry for wearable physiological monitoring","authors":"Y. Mendelson, D. Dao, K. Chon","doi":"10.1109/BSN.2013.6575518","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575518","url":null,"abstract":"Pulse oximetry is a widely accepted clinical method for noninvasive monitoring of arterial oxygen saturation and pulse rate. Significant improvements aimed at curbing motion artifacts and improving reliability in detecting sufficiently strong photoplethysmographic signals are required to reduce errant measurements before the pulse oximeter can be considered for wider mobile applications. The present work describes the development of a wearable multi-channel reflectance pulse oximeter to investigate if a motion artifact-free signal can be obtained in at least one of the multichannels at any given time. Pilot findings provided a proof of concept to support the hypothesis that photoplethysmograms acquired concurrently from independent channels in a multi-channel pulse oximeter sensor respond differently to motion artifacts, thus laying the foundation for future development of robust active noise cancellation and data fusion based algorithms to mitigate the effects of motion artifacts.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115433502","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575475
Jason J. Liu, Ming-chun Huang, Wenyao Xu, N. Alshurafa, M. Sarrafzadeh
This paper presents the design of an on-bed rehabilitation exercise monitoring system that utilizes a high density sensor bedsheet to evaluate active range of motion exercises. We propose and develop a novel framework to analyze the progression of pressure image sequences using manifold learning. The image sequences are reduced to a low dimensional subspace that can be measured against expected prior data for each of the rehabilitation exercises. We also present a metric to compare manifold similarities. Our experimental results on five on-bed exercises show that this system can accurately track compliance of patients to prescribed treatment programs. It allows physical therapists to evaluate how well patients adhere to the rehabilitation exercises. The system is convenient to setup, unobtrusive, and can be used for reliable, long term monitoring.
{"title":"On-bed monitoring for range of motion exercises with a pressure sensitive bedsheet","authors":"Jason J. Liu, Ming-chun Huang, Wenyao Xu, N. Alshurafa, M. Sarrafzadeh","doi":"10.1109/BSN.2013.6575475","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575475","url":null,"abstract":"This paper presents the design of an on-bed rehabilitation exercise monitoring system that utilizes a high density sensor bedsheet to evaluate active range of motion exercises. We propose and develop a novel framework to analyze the progression of pressure image sequences using manifold learning. The image sequences are reduced to a low dimensional subspace that can be measured against expected prior data for each of the rehabilitation exercises. We also present a metric to compare manifold similarities. Our experimental results on five on-bed exercises show that this system can accurately track compliance of patients to prescribed treatment programs. It allows physical therapists to evaluate how well patients adhere to the rehabilitation exercises. The system is convenient to setup, unobtrusive, and can be used for reliable, long term monitoring.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128676315","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575507
Zhiqiang Zhang, Charence Wong, Guang-Zhong Yang
Myoelectric signal analysis provides insight into neural control during muscle contraction and it has been widely used to identify the intention of performing different movements for patients with disabilities. Previous studies have demonstrated that detailed neural control information could be extracted from high-density surface electromyography (EMG) signals. However, this imposes practical constraints for routine applications. In this paper, we present an analysis framework using low-density EMG with example experiments demonstrating the control of forearm functional movement Eight channel surface EMG signals are used with subjects performing 6 different forearm and hand movements. Data analysis consisting of feature selection and pattern classification based on KNN, linear discriminant analysis and support vector machine is then performed. High classification accuracy has been achieved for all the subjects, illustrating the practical value of the method proposed.
{"title":"Forearm functional movement recognition using spare channel surface electromyography","authors":"Zhiqiang Zhang, Charence Wong, Guang-Zhong Yang","doi":"10.1109/BSN.2013.6575507","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575507","url":null,"abstract":"Myoelectric signal analysis provides insight into neural control during muscle contraction and it has been widely used to identify the intention of performing different movements for patients with disabilities. Previous studies have demonstrated that detailed neural control information could be extracted from high-density surface electromyography (EMG) signals. However, this imposes practical constraints for routine applications. In this paper, we present an analysis framework using low-density EMG with example experiments demonstrating the control of forearm functional movement Eight channel surface EMG signals are used with subjects performing 6 different forearm and hand movements. Data analysis consisting of feature selection and pattern classification based on KNN, linear discriminant analysis and support vector machine is then performed. High classification accuracy has been achieved for all the subjects, illustrating the practical value of the method proposed.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116525697","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575485
Christian Bollmeyer, Tim Esemann, H. Gehring, H. Hellbrück
Some medical applications require precise information of position and orientation of a patient as changes affect pressure condition inside the body. In this paper we focus on altitude estimation, where altitude is a distance, in vertical direction, between a reference and a point of a human body. We suggest equipping wireless sensor nodes with high resolution pressure sensors to calculate the altitude with the barometric formula. We implement a body sensor network based on IEEE 802.15.4 and synchronization mechanism with a reference. Pressure variations due to environmental effects are compensated by cancellation with this differential measurement setup. We demonstrate the need for differential measurements and show with a series of measurements that environmental pressure variations have no significant effect on the proposed altitude estimation. Compared to existing systems, our solution is cost effective, easy to deploy and provides a flexible tradeoff between precision and location lag by adjusting a filter constant.
{"title":"Precise indoor altitude estimation based on differential barometric sensing for wireless medical applications","authors":"Christian Bollmeyer, Tim Esemann, H. Gehring, H. Hellbrück","doi":"10.1109/BSN.2013.6575485","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575485","url":null,"abstract":"Some medical applications require precise information of position and orientation of a patient as changes affect pressure condition inside the body. In this paper we focus on altitude estimation, where altitude is a distance, in vertical direction, between a reference and a point of a human body. We suggest equipping wireless sensor nodes with high resolution pressure sensors to calculate the altitude with the barometric formula. We implement a body sensor network based on IEEE 802.15.4 and synchronization mechanism with a reference. Pressure variations due to environmental effects are compensated by cancellation with this differential measurement setup. We demonstrate the need for differential measurements and show with a series of measurements that environmental pressure variations have no significant effect on the proposed altitude estimation. Compared to existing systems, our solution is cost effective, easy to deploy and provides a flexible tradeoff between precision and location lag by adjusting a filter constant.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130330007","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575465
M. Caldara, C. Colleoni, E. Guido, G. Rosace, V. Re, A. Vitali
This work presents a wearable sensing system, aimed to monitor sweat pH and skin temperature in a noninvasive way. The wireless interface and the body coupling via a smart textile make it particularly comfortable and unobtrusive for the wearer; the applications extend from high risks patients hydration monitoring in a home-care environment, to fitness and wellness applications.
{"title":"A wearable sensor platform to monitor sweat pH and skin temperature","authors":"M. Caldara, C. Colleoni, E. Guido, G. Rosace, V. Re, A. Vitali","doi":"10.1109/BSN.2013.6575465","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575465","url":null,"abstract":"This work presents a wearable sensing system, aimed to monitor sweat pH and skin temperature in a noninvasive way. The wireless interface and the body coupling via a smart textile make it particularly comfortable and unobtrusive for the wearer; the applications extend from high risks patients hydration monitoring in a home-care environment, to fitness and wellness applications.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131635338","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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