This paper presents a Body Area Network (BAN) gateway to Android mobile phones for mobile health applications. The proposed approach is based on a Secure Digital Input Output (SDIO) interface, which allows for long-term monitoring since the mobile phone hardware can be extended in order to operate with ultra low-power radios. The software architecture implemented on the mobile phone enables different features; data can be displayed, further processed or sent to a remote server exploiting the WLAN or 3G networks. Moreover, the system allows to configure thresholds on the measured parameters and to automatically send alerts such as SMS messages and emails based on these values. The system is illustrated for the case of ambulatory ECG monitoring.
{"title":"An Android-based body area network gateway for mobile health applications","authors":"M. Altini, J. Penders, Herman W. Roebbers","doi":"10.1145/1921081.1921105","DOIUrl":"https://doi.org/10.1145/1921081.1921105","url":null,"abstract":"This paper presents a Body Area Network (BAN) gateway to Android mobile phones for mobile health applications. The proposed approach is based on a Secure Digital Input Output (SDIO) interface, which allows for long-term monitoring since the mobile phone hardware can be extended in order to operate with ultra low-power radios. The software architecture implemented on the mobile phone enables different features; data can be displayed, further processed or sent to a remote server exploiting the WLAN or 3G networks. Moreover, the system allows to configure thresholds on the measured parameters and to automatically send alerts such as SMS messages and emails based on these values. The system is illustrated for the case of ambulatory ECG monitoring.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"90 1","pages":"188-189"},"PeriodicalIF":0.0,"publicationDate":"2010-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86967534","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}
Adam T. Barth, Benjamin Boudaoud, Jeff S. Brantley, Shanshan Chen, Christopher L. Cunningham, Taeyoung Kim, H. Powell, Samuel A. Ridenour, J. Lach, B. Bennett
Gait analysis has long been used for various medical and healthcare assessments [1]. In orthopedics and prosthetics, gait analysis is essential for identifying the pathology and assessing the efficacy of the orthopedic assistants or prosthetics prescribed. For example, the efficacy of ankle-foot orthoses (AFOs), usually prescribed to patients with muscle disorders, (e.g., cerebral palsy, spinal cord injury, muscular dystrophy, etc.) to prevent contractures [2], remains unclear. Studies on recovery and rehabilitation from knee surgery have shown that gait analysis focusing on knee joint angles is the key to evaluating the efficacy of treatment. In elderly healthcare, gait analysis has also played an important role in studies of fall risks and fall prevention [3]. Even in cognitive and neuropsychology studies, gait analysis becomes an important parameter because of the close relationship between human cognitive skills and motor function. For example, [4] and [5] have shown the research value of gait analysis in Parkinson's disease and early childhood autism diagnosis, respectively.
{"title":"Longitudinal high-fidelity gait analysis with wireless inertial body sensors","authors":"Adam T. Barth, Benjamin Boudaoud, Jeff S. Brantley, Shanshan Chen, Christopher L. Cunningham, Taeyoung Kim, H. Powell, Samuel A. Ridenour, J. Lach, B. Bennett","doi":"10.1145/1921081.1921107","DOIUrl":"https://doi.org/10.1145/1921081.1921107","url":null,"abstract":"Gait analysis has long been used for various medical and healthcare assessments [1]. In orthopedics and prosthetics, gait analysis is essential for identifying the pathology and assessing the efficacy of the orthopedic assistants or prosthetics prescribed. For example, the efficacy of ankle-foot orthoses (AFOs), usually prescribed to patients with muscle disorders, (e.g., cerebral palsy, spinal cord injury, muscular dystrophy, etc.) to prevent contractures [2], remains unclear. Studies on recovery and rehabilitation from knee surgery have shown that gait analysis focusing on knee joint angles is the key to evaluating the efficacy of treatment. In elderly healthcare, gait analysis has also played an important role in studies of fall risks and fall prevention [3]. Even in cognitive and neuropsychology studies, gait analysis becomes an important parameter because of the close relationship between human cognitive skills and motor function. For example, [4] and [5] have shown the research value of gait analysis in Parkinson's disease and early childhood autism diagnosis, respectively.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"47 1","pages":"192-193"},"PeriodicalIF":0.0,"publicationDate":"2010-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82236748","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}
Body Sensor Networks (BSNs) consist of sensor nodes deployed on the human body for health monitoring. Each sensor node is implemented by interfacing a physiological sensor with a sensor platform consisting of components such as microcontroller, radio and memory. Diverse needs of BSN applications require customized platform development for optimizing performance. In this paper, we propose a two-phase framework to evaluate the performance of sensor platforms to match a BSN's computation, communication and sensing requirements: 1) Design Space Determination, wherein we investigate salient features of BSN platforms and quantify them as design coordinates through evaluation metrics such as SPSW (Samples Processed per Second per Watt) and EPC (Expected Power Consumption). To measure these metrics for a platform under typical BSN application workloads, we propose BSN-Bench, a benchmarking suite composed of basic tasks that occur in diverse BSN applications. BSNBench enables an accurate profiling of platforms based on the design coordinates; 2) Design Space Exploration, wherein we explore the design space to find the most suitable platform for a given application. We demonstrate the usage of our framework through a case study, where we consider two practical BSN applications and choose suitable platforms for them.
{"title":"Evaluation of body sensor network platforms: a design space and benchmarking analysis","authors":"S. Nabar, Ayan Banerjee, S. Gupta, R. Poovendran","doi":"10.1145/1921081.1921096","DOIUrl":"https://doi.org/10.1145/1921081.1921096","url":null,"abstract":"Body Sensor Networks (BSNs) consist of sensor nodes deployed on the human body for health monitoring. Each sensor node is implemented by interfacing a physiological sensor with a sensor platform consisting of components such as microcontroller, radio and memory. Diverse needs of BSN applications require customized platform development for optimizing performance. In this paper, we propose a two-phase framework to evaluate the performance of sensor platforms to match a BSN's computation, communication and sensing requirements: 1) Design Space Determination, wherein we investigate salient features of BSN platforms and quantify them as design coordinates through evaluation metrics such as SPSW (Samples Processed per Second per Watt) and EPC (Expected Power Consumption). To measure these metrics for a platform under typical BSN application workloads, we propose BSN-Bench, a benchmarking suite composed of basic tasks that occur in diverse BSN applications. BSNBench enables an accurate profiling of platforms based on the design coordinates; 2) Design Space Exploration, wherein we explore the design space to find the most suitable platform for a given application. We demonstrate the usage of our framework through a case study, where we consider two practical BSN applications and choose suitable platforms for them.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"304 1","pages":"118-127"},"PeriodicalIF":0.0,"publicationDate":"2010-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82772242","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}
We demonstrate a patient-centered health monitoring system that promotes patient-centered healthcare and self-management. Many patients with chronic diseases require daily measurements and medical attention over a long period of time. Patients often forget to take readings and medications on time. We have developed a patient-centered health monitoring system that interfaces two health monitoring devices, a blood pressure monitor and a digital weight scale, to a laptop computer via Bluetooth wireless communication. Each device takes the patient's measurements, and automatically transmits the measured data to the laptop. The patient and/or a healthcare professional can see the historical data from the blood pressure monitor and the digital weight scale as a list or a graph on the laptop. The system not only performs a statistical analysis of the measured data but also displays the results, to enable the patient to understand his/her health conditions and to become engaged in monitoring his/her health.
{"title":"A patient-centered health monitoring system","authors":"Y. Chuang, P. Melliar-Smith, L. Moser","doi":"10.1145/1921081.1921109","DOIUrl":"https://doi.org/10.1145/1921081.1921109","url":null,"abstract":"We demonstrate a patient-centered health monitoring system that promotes patient-centered healthcare and self-management. Many patients with chronic diseases require daily measurements and medical attention over a long period of time. Patients often forget to take readings and medications on time. We have developed a patient-centered health monitoring system that interfaces two health monitoring devices, a blood pressure monitor and a digital weight scale, to a laptop computer via Bluetooth wireless communication. Each device takes the patient's measurements, and automatically transmits the measured data to the laptop. The patient and/or a healthcare professional can see the historical data from the blood pressure monitor and the digital weight scale as a list or a graph on the laptop. The system not only performs a statistical analysis of the measured data but also displays the results, to enable the patient to understand his/her health conditions and to become engaged in monitoring his/her health.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"12 1","pages":"196-197"},"PeriodicalIF":0.0,"publicationDate":"2010-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82949435","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}
K. Plarre, A. Raij, Santanu Guha, M. al’Absi, Emre Ertin, Santosh Kumar
Body area sensor networks measure biomedical signals from subjects continuously, as they go about their daily lives. Signals measured in these conditions are affected by anomalies, such as artifacts and noise. Some anomalies can be corrected, if detected in real-time, for example, ECG electrode detachment. We present energy and computationally efficient algorithms for the detection of sensor detachment, developed for the AutoSense system.
{"title":"Automated detection of sensor detachments for physiological sensing in the wild","authors":"K. Plarre, A. Raij, Santanu Guha, M. al’Absi, Emre Ertin, Santosh Kumar","doi":"10.1145/1921081.1921119","DOIUrl":"https://doi.org/10.1145/1921081.1921119","url":null,"abstract":"Body area sensor networks measure biomedical signals from subjects continuously, as they go about their daily lives. Signals measured in these conditions are affected by anomalies, such as artifacts and noise. Some anomalies can be corrected, if detected in real-time, for example, ECG electrode detachment. We present energy and computationally efficient algorithms for the detection of sensor detachment, developed for the AutoSense system.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"37 1","pages":"216-217"},"PeriodicalIF":0.0,"publicationDate":"2010-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82005845","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}
Sleep monitoring is very important for elderly people as inadequate and irregular sleep are often related to serious diseases such as depression and diabetes. In many cases, it is necessary to monitor the body positions and movements made while sleeping because of their relationships to particular diseases (i.e., sleep apnea and restless legs syndrome). Analyzing movements during sleep also helps in determining sleep quality and irregular sleeping patterns. This paper presents a sleep monitoring system based on the WISP platform - active RFID-based sensors equipped with accelerometers. We show how our system accurately infers fine-grained body positions from accelerometer data collected from the WISPs attached to the bed mattress. Movements and their duration are also detected by the system. We present the results of our empirical study from 10 subjects on three different mattresses in controlled experiments to show the accuracy of our inference algorithms. Finally, we evaluate the accuracy of the movement detection and body position inference for six nights on one subject, and compare these results with two baseline systems: one that uses bed pressure sensors and the other is an iPhone application.
{"title":"Monitoring body positions and movements during sleep using WISPs","authors":"Enamul Hoque, Robert F. Dickerson, J. Stankovic","doi":"10.1145/1921081.1921088","DOIUrl":"https://doi.org/10.1145/1921081.1921088","url":null,"abstract":"Sleep monitoring is very important for elderly people as inadequate and irregular sleep are often related to serious diseases such as depression and diabetes. In many cases, it is necessary to monitor the body positions and movements made while sleeping because of their relationships to particular diseases (i.e., sleep apnea and restless legs syndrome). Analyzing movements during sleep also helps in determining sleep quality and irregular sleeping patterns. This paper presents a sleep monitoring system based on the WISP platform - active RFID-based sensors equipped with accelerometers. We show how our system accurately infers fine-grained body positions from accelerometer data collected from the WISPs attached to the bed mattress. Movements and their duration are also detected by the system. We present the results of our empirical study from 10 subjects on three different mattresses in controlled experiments to show the accuracy of our inference algorithms. Finally, we evaluate the accuracy of the movement detection and body position inference for six nights on one subject, and compare these results with two baseline systems: one that uses bed pressure sensors and the other is an iPhone application.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"7 3","pages":"44-53"},"PeriodicalIF":0.0,"publicationDate":"2010-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72631746","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}
Each year, countless children die in underdeveloped countries as a result of water-borne illness. We present a prototype system, currently in pilot testing by a Haiti-based NGO, that supports increased transparency and scalability for data assimilation efforts in the context of a distributed water sanitation project. Wireless technologies such as Short Messaging System (SMS) and Near Field Communication (NFC) are integral to the system. Due to the use of low-cost, off-the-shelf mobile hardware and open-source software, and the selection of SMS for the network transport layer, the system is affordable yet reliable enough to be deployed where power and connectivity may be extremely intermittent. This includes regions such as rural Haiti, home to over half the country's population, as well as disaster areas, such as Port-au-Prince, the urban epicenter of a recent devastating earthquake---both locations where the system is currently being deployed.
{"title":"Wireless monitoring of a distributed environmental health intervention in Haiti","authors":"D. Holstius, Jofish Kaye, E. Seto","doi":"10.1145/1921081.1921113","DOIUrl":"https://doi.org/10.1145/1921081.1921113","url":null,"abstract":"Each year, countless children die in underdeveloped countries as a result of water-borne illness. We present a prototype system, currently in pilot testing by a Haiti-based NGO, that supports increased transparency and scalability for data assimilation efforts in the context of a distributed water sanitation project. Wireless technologies such as Short Messaging System (SMS) and Near Field Communication (NFC) are integral to the system. Due to the use of low-cost, off-the-shelf mobile hardware and open-source software, and the selection of SMS for the network transport layer, the system is affordable yet reliable enough to be deployed where power and connectivity may be extremely intermittent. This includes regions such as rural Haiti, home to over half the country's population, as well as disaster areas, such as Port-au-Prince, the urban epicenter of a recent devastating earthquake---both locations where the system is currently being deployed.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"547 1","pages":"204-205"},"PeriodicalIF":0.0,"publicationDate":"2010-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75732987","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}
P. Ganapathy, Shantanu H. Joshi, J. Yadegar, Niranjan Kamat, C. Caluser
We propose to develop a portable, handheld, noninvasive solution for accurate screening and real-time monitoring of traumatic brain injury (BI) in ambulatory/emergency response scenarios. A layered sensing concept that unifies alternate modalities such as a) ultrasound (US), b) near infrared spectroscopy (NIRS), c) tonometry (IOP), to predict BI, their severity and mode of recommendations for emergency medical service (EMS) personnel is offered. Specifically, we aim to determine i) novel 3D morphometric parameters of optic nerve sheath that can predict elevated intracranial pressure from US data, ii) incidence of intracranial hematomas using NIRS, iii) intraocular pressure using a tonometer, iv) cerebral blood flow and blood oxygen content using other auxiliary non-invasive sensing modes and v) finally provide a sensor fused outcome of all i)-iv) combined. This decision-support system (DSS) will improve BI detection by incorporating accurate on-site measurements that accounts for individual baseline variations and monitors temporal manifestation of the injury. The data collected and the preliminary analysis performed by the DSS will be sent to an emergency department (ED) physician stationed at a nearby trauma center via a wireless 3G network. Based on the available bandwidth, either all the data including the preliminary analysis (US video, images, 1D measurements, etc) or only the refined signals (feature vector extracted during screening) along with the DSS diagnosis will be sent to the physician. If the DSS determined output is agreeable to the physician then the screening can be terminated and the physician/ED staff can prepare to perform advanced interventions (intubation, cerebralspinal fluid (CSF) drainage, etc). If not, the on-call physician can inform the medic to repeat the scans/take additional measurements to obtain a more concrete outcome via the DSS. In summary, such a knowledge-driven system will equip a novice or a trained medic with an easy-to-use tool to detect traumatic BI and reduce the diagnosis time involved (i.e., computed tomography (CT) scan, clinical evaluation) in ED before performing advanced interventions and thereby improve the prognosis.
{"title":"An intelligent and portable ambulatory medical toolkit for automatic detection and assessment of traumatic brain injuries","authors":"P. Ganapathy, Shantanu H. Joshi, J. Yadegar, Niranjan Kamat, C. Caluser","doi":"10.1145/1921081.1921086","DOIUrl":"https://doi.org/10.1145/1921081.1921086","url":null,"abstract":"We propose to develop a portable, handheld, noninvasive solution for accurate screening and real-time monitoring of traumatic brain injury (BI) in ambulatory/emergency response scenarios. A layered sensing concept that unifies alternate modalities such as a) ultrasound (US), b) near infrared spectroscopy (NIRS), c) tonometry (IOP), to predict BI, their severity and mode of recommendations for emergency medical service (EMS) personnel is offered. Specifically, we aim to determine i) novel 3D morphometric parameters of optic nerve sheath that can predict elevated intracranial pressure from US data, ii) incidence of intracranial hematomas using NIRS, iii) intraocular pressure using a tonometer, iv) cerebral blood flow and blood oxygen content using other auxiliary non-invasive sensing modes and v) finally provide a sensor fused outcome of all i)-iv) combined. This decision-support system (DSS) will improve BI detection by incorporating accurate on-site measurements that accounts for individual baseline variations and monitors temporal manifestation of the injury. The data collected and the preliminary analysis performed by the DSS will be sent to an emergency department (ED) physician stationed at a nearby trauma center via a wireless 3G network. Based on the available bandwidth, either all the data including the preliminary analysis (US video, images, 1D measurements, etc) or only the refined signals (feature vector extracted during screening) along with the DSS diagnosis will be sent to the physician. If the DSS determined output is agreeable to the physician then the screening can be terminated and the physician/ED staff can prepare to perform advanced interventions (intubation, cerebralspinal fluid (CSF) drainage, etc). If not, the on-call physician can inform the medic to repeat the scans/take additional measurements to obtain a more concrete outcome via the DSS. In summary, such a knowledge-driven system will equip a novice or a trained medic with an easy-to-use tool to detect traumatic BI and reduce the diagnosis time involved (i.e., computed tomography (CT) scan, clinical evaluation) in ED before performing advanced interventions and thereby improve the prognosis.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"15 1","pages":"24-33"},"PeriodicalIF":0.0,"publicationDate":"2010-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80785802","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}
Frank Wang, Yeung Lam, A. Mehrnia, B. Bates-Jensen, M. Sarrafzadeh, W. Kaiser
Patients' skin integrity has long been an issue of concern in nursing homes and hospitals. Overall incidence of pressure ulcers for hospitalized patients is as high as 50%. The estimated cost of treating pressure ulcers ranges from $5,000 to $40,000 for each ulcer, depending on severity. A smart compact capacitive sensing wireless handheld system is presented which measures Sub-Epidermal Moisture (SEM) as a mean to detect and monitor early symptoms of ulcer development. The system was successfully verified in trials with 30 volunteers and is currently deployed for clinical trials in four nursing homes.
{"title":"A wireless biomedical instrument for evidence-based tissue wound characterization","authors":"Frank Wang, Yeung Lam, A. Mehrnia, B. Bates-Jensen, M. Sarrafzadeh, W. Kaiser","doi":"10.1145/1921081.1921122","DOIUrl":"https://doi.org/10.1145/1921081.1921122","url":null,"abstract":"Patients' skin integrity has long been an issue of concern in nursing homes and hospitals. Overall incidence of pressure ulcers for hospitalized patients is as high as 50%. The estimated cost of treating pressure ulcers ranges from $5,000 to $40,000 for each ulcer, depending on severity. A smart compact capacitive sensing wireless handheld system is presented which measures Sub-Epidermal Moisture (SEM) as a mean to detect and monitor early symptoms of ulcer development. The system was successfully verified in trials with 30 volunteers and is currently deployed for clinical trials in four nursing homes.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"6 1","pages":"222-223"},"PeriodicalIF":0.0,"publicationDate":"2010-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89315170","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}
Nan Hua, Ashwin Lall, J. Romberg, Jun Xu, M. al’Absi, Emre Ertin, Santosh Kumar, Shikhar Suri
Continuous monitoring of human physiology and behavior in natural environments via unobtrusively wearable wireless sensors is witnessing rapid adoption in both consumer health-care and in scientific studies, since those portable and long-running devices can provide critical information for diagnosis and early prevention of disease, as well as invaluable data for scientific studies. Due to the requirement of continuous monitoring, these sensors, all operating on small wearable batteries, require frequent recharging. Lowering this recharging burden is essential for their widespread adoption. In this paper we explore mechanisms for significantly enhancing the lifetime of these wearable sensors at the cost of a small loss in their sensing accuracy. We propose two ideas that build upon our observation that collecting bursts of samples over short periods of time is sufficient to capture the most interesting and informative part of the signal. In the first part of this paper, we propose a general methodology for reconstructing bandlimited signals accurately from such short bursts of samples. While this reconstruction task is in nature an ill-conditioned problem, we show that the insertion of an analog "modulated pre-filter" hardware module before the ADC can almost surely alleviate this conditioning problem. In the second part of this paper, we describe just-in-time sampling, which by sampling in short bursts at the "right" times, can accurately track R-wave peaks in ECG signals. Using simulations on publicly available traces as well as self-collected data, we show the efficacy of this technique.
{"title":"Just-in-time sampling and pre-filtering for wearable physiological sensors: going from days to weeks of operation on a single charge","authors":"Nan Hua, Ashwin Lall, J. Romberg, Jun Xu, M. al’Absi, Emre Ertin, Santosh Kumar, Shikhar Suri","doi":"10.1145/1921081.1921089","DOIUrl":"https://doi.org/10.1145/1921081.1921089","url":null,"abstract":"Continuous monitoring of human physiology and behavior in natural environments via unobtrusively wearable wireless sensors is witnessing rapid adoption in both consumer health-care and in scientific studies, since those portable and long-running devices can provide critical information for diagnosis and early prevention of disease, as well as invaluable data for scientific studies. Due to the requirement of continuous monitoring, these sensors, all operating on small wearable batteries, require frequent recharging. Lowering this recharging burden is essential for their widespread adoption.\u0000 In this paper we explore mechanisms for significantly enhancing the lifetime of these wearable sensors at the cost of a small loss in their sensing accuracy. We propose two ideas that build upon our observation that collecting bursts of samples over short periods of time is sufficient to capture the most interesting and informative part of the signal. In the first part of this paper, we propose a general methodology for reconstructing bandlimited signals accurately from such short bursts of samples. While this reconstruction task is in nature an ill-conditioned problem, we show that the insertion of an analog \"modulated pre-filter\" hardware module before the ADC can almost surely alleviate this conditioning problem. In the second part of this paper, we describe just-in-time sampling, which by sampling in short bursts at the \"right\" times, can accurately track R-wave peaks in ECG signals. Using simulations on publicly available traces as well as self-collected data, we show the efficacy of this technique.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"92 1 Suppl 1","pages":"54-63"},"PeriodicalIF":0.0,"publicationDate":"2010-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88695233","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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