Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299421
Santhisagar Vaddiraju, M. Kastellorizios, Allen Legassey, D. Burgess, F. Jain, F. Papadimitrakopoulos
Unlike non-invasive and minimally invasive continuous monitoring of glucose (CGM) devices, invasive devices require less rigorous calibration and exhibit smaller subject-to-subject variability. Biorasis, Inc. and the University of Connecticut are developing a totally implantable CGM device. Glucowizzard™ is engineered at the smallest possible footprint (0.5 × 0.5 × 5 mm). This miniaturization is made possible by utilizing light both for powering and wireless communication. In addition, Glucowizzard™ utilizes “smart” hydrogel coatings intended for localized release of various tissue response modifiers for effective control of negative tissue responses. The use of light-based powering and communication together with advanced microelectronic design rules has allowed the fabrication of truly miniaturized CGM device. The drug delivery coating has enabled substantial reduction of negative tissue responses for up to 1 month in small as well as large animals (rats and minipigs). The functionality of Glucowizzard™ has been demonstrated in vivo in both rats and minipigs.
{"title":"Needle-implantable, wireless biosensor for continuous glucose monitoring","authors":"Santhisagar Vaddiraju, M. Kastellorizios, Allen Legassey, D. Burgess, F. Jain, F. Papadimitrakopoulos","doi":"10.1109/BSN.2015.7299421","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299421","url":null,"abstract":"Unlike non-invasive and minimally invasive continuous monitoring of glucose (CGM) devices, invasive devices require less rigorous calibration and exhibit smaller subject-to-subject variability. Biorasis, Inc. and the University of Connecticut are developing a totally implantable CGM device. Glucowizzard™ is engineered at the smallest possible footprint (0.5 × 0.5 × 5 mm). This miniaturization is made possible by utilizing light both for powering and wireless communication. In addition, Glucowizzard™ utilizes “smart” hydrogel coatings intended for localized release of various tissue response modifiers for effective control of negative tissue responses. The use of light-based powering and communication together with advanced microelectronic design rules has allowed the fabrication of truly miniaturized CGM device. The drug delivery coating has enabled substantial reduction of negative tissue responses for up to 1 month in small as well as large animals (rats and minipigs). The functionality of Glucowizzard™ has been demonstrated in vivo in both rats and minipigs.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130388302","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 : 2015-06-09DOI: 10.1109/BSN.2015.7299393
Jian Wu, Zhongjun Tian, Lu Sun, L. Estevez, R. Jafari
A Sign Language Recognition (SLR) system enables communication between hearing disabled individuals and those who can hear and speak. With the prevalence of the wearable computers, this technology is becoming an important human computer interface capable of reading hand gestures and inferring user;s intent. In this paper, we propose a real-time American SLR system leveraging fusion of surface electromyography (sEMG) and a wrist-worn inertial sensor at the feature level. A feature selection is provided for 40 most commonly used words and for four subjects. The experimental results show that after feature selection and conditioning, our system achieves 95.94% recognition rate. The results also illustrate the fusion of two modalities perform better than using only the inertial sensor. We observed that only one channel of sEMG (out of four) located on the wrist and under the wrist-watch is sufficient.
{"title":"Real-time American Sign Language Recognition using wrist-worn motion and surface EMG sensors","authors":"Jian Wu, Zhongjun Tian, Lu Sun, L. Estevez, R. Jafari","doi":"10.1109/BSN.2015.7299393","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299393","url":null,"abstract":"A Sign Language Recognition (SLR) system enables communication between hearing disabled individuals and those who can hear and speak. With the prevalence of the wearable computers, this technology is becoming an important human computer interface capable of reading hand gestures and inferring user;s intent. In this paper, we propose a real-time American SLR system leveraging fusion of surface electromyography (sEMG) and a wrist-worn inertial sensor at the feature level. A feature selection is provided for 40 most commonly used words and for four subjects. The experimental results show that after feature selection and conditioning, our system achieves 95.94% recognition rate. The results also illustrate the fusion of two modalities perform better than using only the inertial sensor. We observed that only one channel of sEMG (out of four) located on the wrist and under the wrist-watch is sufficient.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130815375","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 : 2015-06-09DOI: 10.1109/BSN.2015.7299418
V. Sivaji, D. Bhatia, S. Prasad
Sleep Apnea is a well-documented and chronic problem that can result in various life threatening disorders. Detecting and diagnosing sleep apnea requires a long duration sleep study that makes use of various sensor based monitors. The need for long duration sleep studies at a special care provider facility and lack of simple portable monitors result in many undiagnosed cases. In this paper, we have proposed and implemented a low cost, self-powered, sleep apnea monitor that detects apnea episodes using simple body mounted sensors. The entire system is powered by RF energy that is fetched from the ambient environment.
{"title":"Novel technique for sleep apnea monitoring","authors":"V. Sivaji, D. Bhatia, S. Prasad","doi":"10.1109/BSN.2015.7299418","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299418","url":null,"abstract":"Sleep Apnea is a well-documented and chronic problem that can result in various life threatening disorders. Detecting and diagnosing sleep apnea requires a long duration sleep study that makes use of various sensor based monitors. The need for long duration sleep studies at a special care provider facility and lack of simple portable monitors result in many undiagnosed cases. In this paper, we have proposed and implemented a low cost, self-powered, sleep apnea monitor that detects apnea episodes using simple body mounted sensors. The entire system is powered by RF energy that is fetched from the ambient environment.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"210 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116690871","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 : 2015-06-01DOI: 10.1109/BSN.2015.7299354
Javier Hernández, Daniel J. McDuff, Rosalind W. Picard
During recent years a large variety of wearable devices have become commercially available. As these devices are in close contact with the body, they have the potential to capture sensitive and unexpected personal data even when the wearer is not moving. This work demonstrates that wearable motion sensors such as accelerometers and gyroscopes embedded in head-mounted and wrist-worn wearable devices can be used to identify the wearer (among 12 participants) and his/her body posture (among 3 positions) from only 10 seconds of “still” motion data. Instead of focusing on large and apparent motions such as steps or gait, the proposed methods amplify and analyze very subtle body motions associated with the beating of the heart. Our findings have the potential to increase the value of pervasive wearable motion sensors but also raise important privacy concerns that need to be considered.
{"title":"BioInsights: Extracting personal data from “Still” wearable motion sensors","authors":"Javier Hernández, Daniel J. McDuff, Rosalind W. Picard","doi":"10.1109/BSN.2015.7299354","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299354","url":null,"abstract":"During recent years a large variety of wearable devices have become commercially available. As these devices are in close contact with the body, they have the potential to capture sensitive and unexpected personal data even when the wearer is not moving. This work demonstrates that wearable motion sensors such as accelerometers and gyroscopes embedded in head-mounted and wrist-worn wearable devices can be used to identify the wearer (among 12 participants) and his/her body posture (among 3 positions) from only 10 seconds of “still” motion data. Instead of focusing on large and apparent motions such as steps or gait, the proposed methods amplify and analyze very subtle body motions associated with the beating of the heart. Our findings have the potential to increase the value of pervasive wearable motion sensors but also raise important privacy concerns that need to be considered.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129256118","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 : 2015-06-01DOI: 10.1109/BSN.2015.7299362
Ze-dong Nie, Yuhang Liu, Changjiang Duan, Z. Ruan, Jingzhen Li, Lei Wang
Human body communication (HBC) is a short-range, wireless communication in the vicinity of, or inside a human body. In this paper, biometric authentication based on capacitive coupled HBC is presented for the wearable devices. In-situ experiments were conducted with 20 volunteers to investigate the feasibility. The S21 parameters of the HBC channel from one palm to the other within the frequency range of 300 KHz-50 MHz were measured. A total of 2,561,600 data are acquired. The data are analyzed by the support vector machines (SVM) including C-SVM and nu-SVM, where 2,241,400 data are used to train the SVM model and 320,200 data are used to estimate the authentication rate. Linear, polynomial, and radial basis function (RBF) are adopted as the kernel functions, respectively. In addition, to verify whether the features in low frequency band will affect the performance of HBC authentication, the features in four frequency bands, i.e., from 300 KHz to 50 MHz, from 3.4 MHz to 50 MHz, from 5.6 MHz to 50 MHz, and from 9.6 MHz to 50 MHz are used as the biometric trait, respectively. The experiment results show that, in biometric identification mode, identification rate of 98% is achieved, and in biometric verification mode, the equal error rate (EER) is 0.24%, the average area under the curve (AUC) of receiver operating characteristic (ROC) reaches 0.9993.
{"title":"Wearable biometric authentication based on human body communication","authors":"Ze-dong Nie, Yuhang Liu, Changjiang Duan, Z. Ruan, Jingzhen Li, Lei Wang","doi":"10.1109/BSN.2015.7299362","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299362","url":null,"abstract":"Human body communication (HBC) is a short-range, wireless communication in the vicinity of, or inside a human body. In this paper, biometric authentication based on capacitive coupled HBC is presented for the wearable devices. In-situ experiments were conducted with 20 volunteers to investigate the feasibility. The S21 parameters of the HBC channel from one palm to the other within the frequency range of 300 KHz-50 MHz were measured. A total of 2,561,600 data are acquired. The data are analyzed by the support vector machines (SVM) including C-SVM and nu-SVM, where 2,241,400 data are used to train the SVM model and 320,200 data are used to estimate the authentication rate. Linear, polynomial, and radial basis function (RBF) are adopted as the kernel functions, respectively. In addition, to verify whether the features in low frequency band will affect the performance of HBC authentication, the features in four frequency bands, i.e., from 300 KHz to 50 MHz, from 3.4 MHz to 50 MHz, from 5.6 MHz to 50 MHz, and from 9.6 MHz to 50 MHz are used as the biometric trait, respectively. The experiment results show that, in biometric identification mode, identification rate of 98% is achieved, and in biometric verification mode, the equal error rate (EER) is 0.24%, the average area under the curve (AUC) of receiver operating characteristic (ROC) reaches 0.9993.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125347564","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 : 2015-06-01DOI: 10.1109/BSN.2015.7299378
Aditi Misra, Rohan Banerjee, A. Choudhury, Aniruddha Sinha, A. Pal
Heart rate variability (HRV) measures the instantaneous change in heart rate and is an important marker for checking physical condition as well as mental stress of a person. In this paper, we propose a methodology to calculate HRV of a person using smart phone audio. Heart sound is captured in the inbuilt microphone of a smart phone, by placing the device on the chest of the person. We propose a process flow to make the phone captured noisy audio signal clean and audible. Furthermore, we propose a novel peak detection algorithm for accurately locating the peaks corresponding to heart sound in the noisy audio signal. The algorithm is also capable of rejecting the noisy peaks present in the captured audio that resembles heart sound pattern. Results show that the proposed methodology yields significant improvement in estimating HRV parameters compared to a clinical pulse-oximeter device, that works on the principle of photoplethysmogram (PPG) technique.
{"title":"Novel peak detection to estimate HRV using smartphone audio","authors":"Aditi Misra, Rohan Banerjee, A. Choudhury, Aniruddha Sinha, A. Pal","doi":"10.1109/BSN.2015.7299378","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299378","url":null,"abstract":"Heart rate variability (HRV) measures the instantaneous change in heart rate and is an important marker for checking physical condition as well as mental stress of a person. In this paper, we propose a methodology to calculate HRV of a person using smart phone audio. Heart sound is captured in the inbuilt microphone of a smart phone, by placing the device on the chest of the person. We propose a process flow to make the phone captured noisy audio signal clean and audible. Furthermore, we propose a novel peak detection algorithm for accurately locating the peaks corresponding to heart sound in the noisy audio signal. The algorithm is also capable of rejecting the noisy peaks present in the captured audio that resembles heart sound pattern. Results show that the proposed methodology yields significant improvement in estimating HRV parameters compared to a clinical pulse-oximeter device, that works on the principle of photoplethysmogram (PPG) technique.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121892517","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 : 2015-06-01DOI: 10.1109/BSN.2015.7299387
A. Gaglione, Shanshan Chen, Benny P. L. Lo, Guang-Zhong Yang
Recent trends in wearable applications demand flexible architectures being able to monitor people while they move in free-living environments. Current solutions use either store-download-offline processing or simple communication schemes with real-time streaming of sensor data. This limits the applicability of wearable applications to controlled environments (e.g, clinics, homes, or laboratories), because they need to maintain connectivity with the base station throughout the monitoring process. In this paper, we present the design and implementation of an opportunistic communication framework that simplifies the general use of wearable devices in free-living environments. It relies on a low-power data collection protocol that allows the end user to opportunistically, yet seamlessly manage the transmission of sensor data. We validate the feasibility of the framework by demonstrating its use for swimming, where the normal wireless communication is constantly interfered by the environment.
{"title":"A low-power opportunistic communication protocol for wearable applications","authors":"A. Gaglione, Shanshan Chen, Benny P. L. Lo, Guang-Zhong Yang","doi":"10.1109/BSN.2015.7299387","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299387","url":null,"abstract":"Recent trends in wearable applications demand flexible architectures being able to monitor people while they move in free-living environments. Current solutions use either store-download-offline processing or simple communication schemes with real-time streaming of sensor data. This limits the applicability of wearable applications to controlled environments (e.g, clinics, homes, or laboratories), because they need to maintain connectivity with the base station throughout the monitoring process. In this paper, we present the design and implementation of an opportunistic communication framework that simplifies the general use of wearable devices in free-living environments. It relies on a low-power data collection protocol that allows the end user to opportunistically, yet seamlessly manage the transmission of sensor data. We validate the feasibility of the framework by demonstrating its use for swimming, where the normal wireless communication is constantly interfered by the environment.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125648367","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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