Pub Date : 2013-05-06DOI: 10.1109/BSN.2013.6575530
Adam Spanbauer, A. Wahab, Brian D. Hemond, I. Hunter, L. Jones
A modular system of small digital sensors and generators that includes a user interface with a graphical display and auditory feedback is being developed to address the limitations associated with measuring various aspects of human performance using bulky and often incompatible wired sensors and associated instrumentation. The initiative is called MICA (Measurement, Instrumentation, Control and Analysis) has entailed developing a high data rate, low latency wireless protocol. Optimized for real-time measurement and control, the protocol has been developed to link a network of sensors and generators to a central data acquisition system. The goal in designing MICA was simplicity and modularity at minimum cost without sacrificing instrumentation quality. The performance of the MICA sensor system is described in the context of measuring electrophysiological variables from active humans and the motion of objects under human control.
{"title":"Measurement, instrumentation, control and analysis (MICA): A modular system of wireless sensors","authors":"Adam Spanbauer, A. Wahab, Brian D. Hemond, I. Hunter, L. Jones","doi":"10.1109/BSN.2013.6575530","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575530","url":null,"abstract":"A modular system of small digital sensors and generators that includes a user interface with a graphical display and auditory feedback is being developed to address the limitations associated with measuring various aspects of human performance using bulky and often incompatible wired sensors and associated instrumentation. The initiative is called MICA (Measurement, Instrumentation, Control and Analysis) has entailed developing a high data rate, low latency wireless protocol. Optimized for real-time measurement and control, the protocol has been developed to link a network of sensors and generators to a central data acquisition system. The goal in designing MICA was simplicity and modularity at minimum cost without sacrificing instrumentation quality. The performance of the MICA sensor system is described in the context of measuring electrophysiological variables from active humans and the motion of objects under human control.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"8 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":"126249372","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.2013.6575523
J. Williamson, D. Bliss, David W. Browne, P. Indic, E. Bloch-Salisbury, D. Paydarfar
Apnea of prematurity is a common developmental disorder in preterm infants that is implicated in a number of acute and long-term complications. Therapeutic stochastic resonance (TSR) is a noninvasive preventative intervention for stabilizing breathing patterns and reducing the incidence of apnea and hypoxia. Because the stabilizing effect of TSR lags its initiation, it can be used most effectively if it is linked to a system for apnea prediction. We present a real-time algorithm for generating apnea predictions based on cardio-respiratory and movement features extracted from multiple physiological sensors. The features are used to create patient-specific statistical models of apnea precursors. The state parameters generated by these models are evaluated over time to form apnea predictions. The algorithms predictions are evaluated using a short, 5.5 minute prediction horizon. The algorithm obtains highly accurate predictions, with statistical significance obtained on five out of the six patients that it is evaluated on.
{"title":"Individualized apnea prediction in preterm infants using cardio-respiratory and movement signals","authors":"J. Williamson, D. Bliss, David W. Browne, P. Indic, E. Bloch-Salisbury, D. Paydarfar","doi":"10.1109/BSN.2013.6575523","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575523","url":null,"abstract":"Apnea of prematurity is a common developmental disorder in preterm infants that is implicated in a number of acute and long-term complications. Therapeutic stochastic resonance (TSR) is a noninvasive preventative intervention for stabilizing breathing patterns and reducing the incidence of apnea and hypoxia. Because the stabilizing effect of TSR lags its initiation, it can be used most effectively if it is linked to a system for apnea prediction. We present a real-time algorithm for generating apnea predictions based on cardio-respiratory and movement features extracted from multiple physiological sensors. The features are used to create patient-specific statistical models of apnea precursors. The state parameters generated by these models are evaluated over time to form apnea predictions. The algorithms predictions are evaluated using a short, 5.5 minute prediction horizon. The algorithm obtains highly accurate predictions, with statistical significance obtained on five out of the six patients that it is evaluated on.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"16 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":"125165081","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.2013.6575513
O. Sarbishei, Benjamin Nahill, Atena Roshan Fekr, Majid Janidarmian, K. Radecka, Z. Zilic, B. Karajica
Accelerometers are vital parts of many industrial and biomedical applications. Such applications have high demands for accuracy. Multi-sensor fusion is an efficient approach to deliver accurate sensor readouts that are tolerant to multiple faults. This paper proposes an efficient data fusion algorithm, which minimizes Mean-Square-Error (MSE) and keeps the overall precision of the system high. We make use of a convex optimization scheme to tackle the problem. Furthermore, a pre-processing step called screening is used to exclude the potentially faulty sensors from the data fusion. The screening process makes it possible to quickly detect multiple faulty sensors. Our data fusion approach is applicable to any multi-sensor system, for which the post-calibration statistical characteristics of sensors can be measured experimentally. However, the results are presented for accelerometers.
{"title":"An efficient fault-tolerant sensor fusion algorithm for accelerometers","authors":"O. Sarbishei, Benjamin Nahill, Atena Roshan Fekr, Majid Janidarmian, K. Radecka, Z. Zilic, B. Karajica","doi":"10.1109/BSN.2013.6575513","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575513","url":null,"abstract":"Accelerometers are vital parts of many industrial and biomedical applications. Such applications have high demands for accuracy. Multi-sensor fusion is an efficient approach to deliver accurate sensor readouts that are tolerant to multiple faults. This paper proposes an efficient data fusion algorithm, which minimizes Mean-Square-Error (MSE) and keeps the overall precision of the system high. We make use of a convex optimization scheme to tackle the problem. Furthermore, a pre-processing step called screening is used to exclude the potentially faulty sensors from the data fusion. The screening process makes it possible to quickly detect multiple faulty sensors. Our data fusion approach is applicable to any multi-sensor system, for which the post-calibration statistical characteristics of sensors can be measured experimentally. However, the results are presented for accelerometers.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"119 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":"134569090","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.2013.6575506
R. Ali, Benny P. L. Lo, Guang-Zhong Yang
In activity recognition and behaviour profiling studies, wearable inertial sensors are commonly used to monitor the subjects' daily activities. However, the need of carrying the sensing devices in addition to personal belongings may prohibit the widespread use of the technologies. On the other hand, smartphones have become ubiquitous and most smartphones are already equipped with similar inertial sensors. Recent studies have proposed the use of smartphone for quantifying the activity and behaviour of the users. A smartphone based long-term routine profiling system is proposed. To simplify the user interface and facilitate the ubiquitous use of the system, unsupervised and optimized techniques have been developed and integrated into a mobile phone application. By running the application continuously in the background of the phone, the system captures and processes the sensing information to infer the activities of the users, and the results are forwarded to the server for profiling the routines using pattern mining techniques. The proposed system is validated through a study of six users over two weeks. The ability of the proposed system in capturing routine behavior is demonstrated in the results of the study.
{"title":"Unsupervised routine profiling in free-living conditions — Can smartphone apps provide insights?","authors":"R. Ali, Benny P. L. Lo, Guang-Zhong Yang","doi":"10.1109/BSN.2013.6575506","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575506","url":null,"abstract":"In activity recognition and behaviour profiling studies, wearable inertial sensors are commonly used to monitor the subjects' daily activities. However, the need of carrying the sensing devices in addition to personal belongings may prohibit the widespread use of the technologies. On the other hand, smartphones have become ubiquitous and most smartphones are already equipped with similar inertial sensors. Recent studies have proposed the use of smartphone for quantifying the activity and behaviour of the users. A smartphone based long-term routine profiling system is proposed. To simplify the user interface and facilitate the ubiquitous use of the system, unsupervised and optimized techniques have been developed and integrated into a mobile phone application. By running the application continuously in the background of the phone, the system captures and processes the sensing information to infer the activities of the users, and the results are forwarded to the server for profiling the routines using pattern mining techniques. The proposed system is validated through a study of six users over two weeks. The ability of the proposed system in capturing routine behavior is demonstrated in the results of the study.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"3 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":"133394963","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.2013.6575509
Qiang Li, Shanshan Chen, J. Stankovic
Various sensing systems have been exploited to monitor in-person interactions, one of the most important indicators of mental health. However, existing solutions either require deploying in-situ infrastructure or fail to provide detailed information about a person's involvement during interactions. In this paper, we use smartphones and on-body sensors to monitor in-person interactions without relying on any in-situ infrastructure. By using state-of-art smartphones and on-body sensors, we implement a multi-modal system that collects a battery of features to better monitor in-person interactions. In addition, unlike existing work that monitors interactions only based on data collected from one person, we emphasize that in-person interactions intrinsically involve multiple participants, and thus we aggregate information from nearby people to identify more interaction details. Evaluation shows our solution accurately detects various in-person interactions and provides insights absent in existing systems.
{"title":"Multi-modal in-person interaction monitoring using smartphone and on-body sensors","authors":"Qiang Li, Shanshan Chen, J. Stankovic","doi":"10.1109/BSN.2013.6575509","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575509","url":null,"abstract":"Various sensing systems have been exploited to monitor in-person interactions, one of the most important indicators of mental health. However, existing solutions either require deploying in-situ infrastructure or fail to provide detailed information about a person's involvement during interactions. In this paper, we use smartphones and on-body sensors to monitor in-person interactions without relying on any in-situ infrastructure. By using state-of-art smartphones and on-body sensors, we implement a multi-modal system that collects a battery of features to better monitor in-person interactions. In addition, unlike existing work that monitors interactions only based on data collected from one person, we emphasize that in-person interactions intrinsically involve multiple participants, and thus we aggregate information from nearby people to identify more interaction details. Evaluation shows our solution accurately detects various in-person interactions and provides insights absent in existing systems.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"2021 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":"121329562","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.2013.6575457
B. Eskofier, Ed Musho, H. Schlarb
The automatic classification of foot strike patterns into the three basic categories forefoot, midfoot and rearfoot striking plays an important role for applications like shoe fitting with instant feedback. This paper presents methods for this classification based on body worn accelerometers that allow giving the required direct feedback to the user. For our study, we collected data from 40 runners who had a standard accelerometer in a custom-built sensor pod attached to the laces of their running shoes. The acceleration in three axes was recorded continuously while the runners conducted their runs. Data for repeated runs at two different speed levels were collected in order to have sufficient sensor data for classification. The data was analyzed using features computed for individual steps of the runners to distinguish the three foot strike pattern classes. The labels for the strike pattern classes were established using high-speed video that was concurrently collected. We could show that the classification of the strike types based on the measured accelerations and the extracted features was up to 95.3% accurate. The established classification system can be used to support runners, for example by giving running shoe recommendations that ideally match the prevailing strike type of the runner.
{"title":"Pattern classification of foot strike type using body worn accelerometers","authors":"B. Eskofier, Ed Musho, H. Schlarb","doi":"10.1109/BSN.2013.6575457","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575457","url":null,"abstract":"The automatic classification of foot strike patterns into the three basic categories forefoot, midfoot and rearfoot striking plays an important role for applications like shoe fitting with instant feedback. This paper presents methods for this classification based on body worn accelerometers that allow giving the required direct feedback to the user. For our study, we collected data from 40 runners who had a standard accelerometer in a custom-built sensor pod attached to the laces of their running shoes. The acceleration in three axes was recorded continuously while the runners conducted their runs. Data for repeated runs at two different speed levels were collected in order to have sufficient sensor data for classification. The data was analyzed using features computed for individual steps of the runners to distinguish the three foot strike pattern classes. The labels for the strike pattern classes were established using high-speed video that was concurrently collected. We could show that the classification of the strike types based on the measured accelerations and the extracted features was up to 95.3% accurate. The established classification system can be used to support runners, for example by giving running shoe recommendations that ideally match the prevailing strike type of the runner.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"3 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":"129150613","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.2013.6575519
M. Kreil, Kristof Van Laerhoven, P. Lukowicz
The output delivered by body-wide inertial sensing systems has proven to contain sufficient information to distinguish between a large number of complex physical activities. The bottlenecks in these systems are in particular the parts of such systems that calculate and select features, as the high dimensionality of the raw sensor signals with the large set of possible features tends to increase rapidly. This paper presents a novel method using a hierarchical clustering method on raw trajectory and angular segments from inertial data to detect and analyze the data from such a distributed set of inertial sensors. We illustrate on a public dataset, how this novel way of modeling can be of assistance in the process of designing a fitting activity recognition system. We show that our method is capable of highlighting class-representative modalities in such high-dimensional data and can be applied to pinpoint target classes that might be problematic to classify at an early stage.
{"title":"Allowing early inspection of activity data from a highly distributed bodynet with a hierarchical-clustering-of-segments approach","authors":"M. Kreil, Kristof Van Laerhoven, P. Lukowicz","doi":"10.1109/BSN.2013.6575519","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575519","url":null,"abstract":"The output delivered by body-wide inertial sensing systems has proven to contain sufficient information to distinguish between a large number of complex physical activities. The bottlenecks in these systems are in particular the parts of such systems that calculate and select features, as the high dimensionality of the raw sensor signals with the large set of possible features tends to increase rapidly. This paper presents a novel method using a hierarchical clustering method on raw trajectory and angular segments from inertial data to detect and analyze the data from such a distributed set of inertial sensors. We illustrate on a public dataset, how this novel way of modeling can be of assistance in the process of designing a fitting activity recognition system. We show that our method is capable of highlighting class-representative modalities in such high-dimensional data and can be applied to pinpoint target classes that might be problematic to classify at an early stage.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"40 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":"125656573","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.2013.6575471
T. Tamura, Yutaka Kimira, Yuichi Kimura, Soichi Maeno, T. Hattori, K. Minato
A pedometer is a popular wearable sensor used to enumerate walking steps taken per day and in this way determines the approximate distance traveled. In this study, we used blood pressure and walking step data, obtained from 48 patients in a home healthcare system, to investigate the effectiveness of the pedometer in a clinical setting. Changes in blood pressure and walking steps per day were compared. Our results indicate that walking, as a regular form of exercise, contributed to lowering of blood pressure. Thus the pedometer is useful for improving the quality of life of patients in the home healthcare setting.
{"title":"Application of a pedometer in a clinical setting: Is the number of walking steps predictive of changes in blood pressure?: Prediction of blood pressure changes in blod presure by a peadmeter","authors":"T. Tamura, Yutaka Kimira, Yuichi Kimura, Soichi Maeno, T. Hattori, K. Minato","doi":"10.1109/BSN.2013.6575471","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575471","url":null,"abstract":"A pedometer is a popular wearable sensor used to enumerate walking steps taken per day and in this way determines the approximate distance traveled. In this study, we used blood pressure and walking step data, obtained from 48 patients in a home healthcare system, to investigate the effectiveness of the pedometer in a clinical setting. Changes in blood pressure and walking steps per day were compared. Our results indicate that walking, as a regular form of exercise, contributed to lowering of blood pressure. Thus the pedometer is useful for improving the quality of life of patients in the home healthcare setting.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"92 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":"133426208","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.2013.6575483
Brian D. Mayton, Nan Zhao, M. Aldrich, N. Gillian, J. Paradiso
WristQue combines environmental and inertial sensing with precise indoor localization into a wristband wearable device that serves as the user's personal control interface to networked infrastructure. WristQue enables users to take control of devices around them by pointing to select and gesturing to control. At the same time, it uniquely identifies and locates users to deliver personalized automatic control of the user's environment. In this paper, the hardware and software components of the WristQue system are introduced, and a number of applications for lighting and HVAC control are presented, using pointing and gesturing as a new human interface to these networked systems.
{"title":"WristQue: A personal sensor wristband","authors":"Brian D. Mayton, Nan Zhao, M. Aldrich, N. Gillian, J. Paradiso","doi":"10.1109/BSN.2013.6575483","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575483","url":null,"abstract":"WristQue combines environmental and inertial sensing with precise indoor localization into a wristband wearable device that serves as the user's personal control interface to networked infrastructure. WristQue enables users to take control of devices around them by pointing to select and gesturing to control. At the same time, it uniquely identifies and locates users to deliver personalized automatic control of the user's environment. In this paper, the hardware and software components of the WristQue system are introduced, and a number of applications for lighting and HVAC control are presented, using pointing and gesturing as a new human interface to these networked systems.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"86 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":"131570505","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.2013.6575464
A. Gribok, W. Rumpler, R. Hoyt, M. Buller
The paper describes application of different types of functional regression for analysis and modeling of the data collected by wearable sensor systems. The data have been recorded from human subjects while they were staying in whole room calorimeter chamber for 48 hours. This allowed very accurate measurements of their oxygen consumption, energy expenditure and substrate oxidation. These physiological parameters are notorious for their inaccuracy when measured in field conditions. The subjects wore two types of body sensors: the Hidalgo Equivital™ (Cambridge, UK) physiological monitors with a telemetry thermometer pill and iPro Professional Continuous Glucose Monitoring System (CGMS) (Medtronic MiniMed, Inc, Northridge, CA). The data collected by these two systems and by the calorimeter chamber were subsequently analyzed off-line using the functional regression techniques. The energy expenditure, substrate oxidation, and body core temperature were used as response variables, while heart rate, respiratory rate, subcutaneous glucose concentration, and skin temperature were used as predictors. The results show that the 24-hours and instantaneous energy expenditure values can be inferred from instantaneous measurements of heart rate, respiratory rate and glucose concentrations. Also, the body core temperature can be inferred from heart rate, respiratory rate, glucose concentration, and skin temperature. The substrate oxidation was the most difficult parameter to infer and it can only be accomplished during the exercise activity.
{"title":"Functional regression for data fusion and indirect measurements of physiological variables collected by wearable sensor systems and indirect calorimetry","authors":"A. Gribok, W. Rumpler, R. Hoyt, M. Buller","doi":"10.1109/BSN.2013.6575464","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575464","url":null,"abstract":"The paper describes application of different types of functional regression for analysis and modeling of the data collected by wearable sensor systems. The data have been recorded from human subjects while they were staying in whole room calorimeter chamber for 48 hours. This allowed very accurate measurements of their oxygen consumption, energy expenditure and substrate oxidation. These physiological parameters are notorious for their inaccuracy when measured in field conditions. The subjects wore two types of body sensors: the Hidalgo Equivital™ (Cambridge, UK) physiological monitors with a telemetry thermometer pill and iPro Professional Continuous Glucose Monitoring System (CGMS) (Medtronic MiniMed, Inc, Northridge, CA). The data collected by these two systems and by the calorimeter chamber were subsequently analyzed off-line using the functional regression techniques. The energy expenditure, substrate oxidation, and body core temperature were used as response variables, while heart rate, respiratory rate, subcutaneous glucose concentration, and skin temperature were used as predictors. The results show that the 24-hours and instantaneous energy expenditure values can be inferred from instantaneous measurements of heart rate, respiratory rate and glucose concentrations. Also, the body core temperature can be inferred from heart rate, respiratory rate, glucose concentration, and skin temperature. The substrate oxidation was the most difficult parameter to infer and it can only be accomplished during the exercise activity.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"32 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":"132251222","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：