F. Kashani, G. Medioni, Khanh Nguyen, Luciano Nocera, C. Shahabi, Ruizhe Wang, Cesar Blanco, Yi-An Chen, Yu-Chen Chung, Beth Fisher, Sara Mulroy, Phil Requejo, C. Winstein
In this paper, we present PoCM2 (Point-of-Care Mobility Monitoring), a generic and extensible at-home mobility evaluation and monitoring system. PoCM2 uses both 3D visual sensors (such as Microsoft Kinect) and mobile sensors (i.e., internal and external sensors embedded with/connected to a mobile device such as a smartphone) for complementary data acquisition, as well as a series of analytics that allow evaluation of both archived and real-time mobility data. We demonstrate the performance of PoCM2 with a specific application developed for freeze detection and quantification from Parkinson's Disease mobility data, as an approach to estimate the medication level of the PD patients and potentially recommend adjustments.
{"title":"Monitoring mobility disorders at home using 3D visual sensors and mobile sensors","authors":"F. Kashani, G. Medioni, Khanh Nguyen, Luciano Nocera, C. Shahabi, Ruizhe Wang, Cesar Blanco, Yi-An Chen, Yu-Chen Chung, Beth Fisher, Sara Mulroy, Phil Requejo, C. Winstein","doi":"10.1145/2534088.2534097","DOIUrl":"https://doi.org/10.1145/2534088.2534097","url":null,"abstract":"In this paper, we present PoCM2 (Point-of-Care Mobility Monitoring), a generic and extensible at-home mobility evaluation and monitoring system. PoCM2 uses both 3D visual sensors (such as Microsoft Kinect) and mobile sensors (i.e., internal and external sensors embedded with/connected to a mobile device such as a smartphone) for complementary data acquisition, as well as a series of analytics that allow evaluation of both archived and real-time mobility data. We demonstrate the performance of PoCM2 with a specific application developed for freeze detection and quantification from Parkinson's Disease mobility data, as an approach to estimate the medication level of the PD patients and potentially recommend adjustments.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"80 1","pages":"9:1-9:2"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77181885","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}
The increasing trend of miniaturization of mobile electronics is associated with a steady reduction of power consumption enabling the integration of wearable devices into smart textiles and implementation of energy harvesting technology. Wearable thin-film thermoelectric generators (TEGs) are a unique energy harvesting solution currently being developed by Perpetua Power Source Technologies for powering wireless devices using body heat as a reliable power source. Integrated into apparel, such as a jacket, thermoelectric technology can power wireless devices that monitor an individual's movement or activity for safety, medical or location purposes.
移动电子产品日益小型化的趋势与功耗的稳步降低有关，从而使可穿戴设备集成到智能纺织品中，并实施能量收集技术。可穿戴薄膜热电发电机(teg)是一种独特的能量收集解决方案，目前由Perpetua Power Source Technologies开发，用于使用体热作为可靠的电源为无线设备供电。将热电技术集成到服装(如夹克)中，可以为无线设备供电，这些设备可以监控个人的运动或活动，以实现安全、医疗或定位目的。
{"title":"Integrating thermoelectric technology into clothing for generating usable energy to power wireless devices","authors":"I. Stark","doi":"10.1145/2448096.2448113","DOIUrl":"https://doi.org/10.1145/2448096.2448113","url":null,"abstract":"The increasing trend of miniaturization of mobile electronics is associated with a steady reduction of power consumption enabling the integration of wearable devices into smart textiles and implementation of energy harvesting technology.\u0000 Wearable thin-film thermoelectric generators (TEGs) are a unique energy harvesting solution currently being developed by Perpetua Power Source Technologies for powering wireless devices using body heat as a reliable power source. Integrated into apparel, such as a jacket, thermoelectric technology can power wireless devices that monitor an individual's movement or activity for safety, medical or location purposes.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"58 1","pages":"17:1-17:2"},"PeriodicalIF":0.0,"publicationDate":"2012-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74683665","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}
In this paper, we are presenting a new wireless and wearable assistive technology called dual-mode Tongue Drive System (dTDS), which is designed to allow people with severe disabilities use computers more effectively with increased speed, flexibility, usability, and independence through their tongue motion and speech. The dTDS detects users' tongue motion using a magnetic tracer and an array of magnetic sensors embedded in a compact, ergonomic, and stylish wireless headset. It also captures users' voice wirelessly using a small microphone on the same headset in a highly integrated fashion. Preliminary evaluation results based on 14 able-bodied subjects indicate that the dTDS headset combined with a commercially available speech recognition software can provide end users with significantly higher performance than either unimodal forms based on tongue motion or speech alone, particularly in completing tasks that require both pointing and text entry.
{"title":"Dual-mode tongue drive system: using speech and tongue motion to improve computer access for people with disabilities","authors":"Xueliang Huo, Hangue Park, Maysam Ghovanloo","doi":"10.1145/2448096.2448102","DOIUrl":"https://doi.org/10.1145/2448096.2448102","url":null,"abstract":"In this paper, we are presenting a new wireless and wearable assistive technology called dual-mode Tongue Drive System (dTDS), which is designed to allow people with severe disabilities use computers more effectively with increased speed, flexibility, usability, and independence through their tongue motion and speech. The dTDS detects users' tongue motion using a magnetic tracer and an array of magnetic sensors embedded in a compact, ergonomic, and stylish wireless headset. It also captures users' voice wirelessly using a small microphone on the same headset in a highly integrated fashion. Preliminary evaluation results based on 14 able-bodied subjects indicate that the dTDS headset combined with a commercially available speech recognition software can provide end users with significantly higher performance than either unimodal forms based on tongue motion or speech alone, particularly in completing tasks that require both pointing and text entry.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"47 1","pages":"6:1-6:8"},"PeriodicalIF":0.0,"publicationDate":"2012-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78493260","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 wearable sensor system for automatic detection of falls and assessment of risk of falling for the elderly through continuous physical activity monitoring. The demonstrated approach uses data measured by a wearable sensor, PAMSys™, for long-term physical activity monitoring. 3-dimensional acceleration data are analyzed to detect falls of a person in order to inform caregivers of such events. Furthermore, as a preventive mechanism, we propose to assess a person's risk of falling using physical activity information. Relevant physical activities include postural transitions, gait initiation, turning, and history of falls. This approach can enable early detection and identification of patterns indicative of high risk of falling in at-risk elders and allow development of more effective preventive measures. The proposed system has the potential to enhance the quality of life and reduce the overall cost of care for elderly persons by assisting them to maintain an independent living style.
{"title":"Fall detection and risk of falling assessment with wearable sensors","authors":"Bor-rong Chen, Joseph Gwin","doi":"10.1145/2448096.2448109","DOIUrl":"https://doi.org/10.1145/2448096.2448109","url":null,"abstract":"We demonstrate a wearable sensor system for automatic detection of falls and assessment of risk of falling for the elderly through continuous physical activity monitoring. The demonstrated approach uses data measured by a wearable sensor, PAMSys™, for long-term physical activity monitoring. 3-dimensional acceleration data are analyzed to detect falls of a person in order to inform caregivers of such events. Furthermore, as a preventive mechanism, we propose to assess a person's risk of falling using physical activity information. Relevant physical activities include postural transitions, gait initiation, turning, and history of falls. This approach can enable early detection and identification of patterns indicative of high risk of falling in at-risk elders and allow development of more effective preventive measures. The proposed system has the potential to enhance the quality of life and reduce the overall cost of care for elderly persons by assisting them to maintain an independent living style.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"50 1","pages":"13:1-13:2"},"PeriodicalIF":0.0,"publicationDate":"2012-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74038658","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}
B. Mapar, Yeung Lam, A. Mehrnia, B. Bates-Jensen, M. Sarrafzadeh, W. Kaiser
Direct characterization of blood perfusion in tissue is critical to a broad spectrum of applications in assessing circulatory disorders, wound conditions and ensuring outcomes of treatment. The rapid evolution of these conditions and their great risk for subjects require a continuously vigilant monitoring technology. This paper presents a wireless health platform providing the first wearable blood perfusion imager. This system, the Perfusion Oxygenation Monitor (POM), introduces sensing diversity by combining array methods and multispectral methods, as well as sensor and emitter distribution and operation scheduling. The principles of photoplethysmographic (PPG) sensing exploited by new methods will enable care providers to actively monitor blood perfusion at multiple anatomical sites for characterization and tracking of perfusion critical to tissue health, wound status and healing, formation of pressure ulcers, and circulation conditions. The POM system is described here along with its experimental validation. Experimental validation has been provided by a direct probing method based on physiological thermoregulatory response that induces perfusion change and is directly measured by POM. The demonstration of the POM system will also be supplemented by an analysis of the end to end system including sensor information processing, feature detection, Wireless Health data transport, and archive structure.
{"title":"Wearable sensor for continuously vigilant spatial and depth-resolved perfusion imaging","authors":"B. Mapar, Yeung Lam, A. Mehrnia, B. Bates-Jensen, M. Sarrafzadeh, W. Kaiser","doi":"10.1145/2448096.2448111","DOIUrl":"https://doi.org/10.1145/2448096.2448111","url":null,"abstract":"Direct characterization of blood perfusion in tissue is critical to a broad spectrum of applications in assessing circulatory disorders, wound conditions and ensuring outcomes of treatment. The rapid evolution of these conditions and their great risk for subjects require a continuously vigilant monitoring technology. This paper presents a wireless health platform providing the first wearable blood perfusion imager. This system, the Perfusion Oxygenation Monitor (POM), introduces sensing diversity by combining array methods and multispectral methods, as well as sensor and emitter distribution and operation scheduling. The principles of photoplethysmographic (PPG) sensing exploited by new methods will enable care providers to actively monitor blood perfusion at multiple anatomical sites for characterization and tracking of perfusion critical to tissue health, wound status and healing, formation of pressure ulcers, and circulation conditions. The POM system is described here along with its experimental validation. Experimental validation has been provided by a direct probing method based on physiological thermoregulatory response that induces perfusion change and is directly measured by POM. The demonstration of the POM system will also be supplemented by an analysis of the end to end system including sensor information processing, feature detection, Wireless Health data transport, and archive structure.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"56 1","pages":"15:1-15:2"},"PeriodicalIF":0.0,"publicationDate":"2012-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88169153","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}
Mu Lin, N. Lane, Mashfiqui Mohammod, Xiaochao Yang, Hong Lu, Giuseppe Cardone, Shahid Ali, Afsaneh Doryab, E. Berke, A. Campbell, Tanzeem Choudhury
Smartphone sensing and persuasive feedback design is enabling a new generation of wellbeing applications capable of automatically monitoring multiple aspects of physical and mental health. In this paper, we present BeWell+ the next generation of the BeWell smartphone health app, which continuously monitors user behavior along three distinct health dimensions, namely sleep, physical activity, and social interaction. BeWell promotes improved behavioral patterns via feedback rendered as an ambient display on the smartphone's wallpaper. With BeWell+, we introduce new wellbeing mechanisms to address challenges identified during the initial deployment of the BeWell app; specifically, (i) community adaptive wellbeing feedback, which automatically generalize to diverse user communities (e.g., elderly, young adults, children) by balancing the need to promote better behavior yet remains realistic to the user's goals; and, (ii) wellbeing adaptive energy allocation, which prioritizes monitoring fidelity and feedback responsiveness on specific health dimensions of wellbeing (e.g., social interaction) where the user needs most help. We evaluate the performance of these mechanisms as part of an initial deployment and user study that includes 27 people using BeWell+ over a 19 day field trial. Our findings show that not only can BeWell+ operate successfully on consumer-grade smartphones, but users understand feedback and respond by taking positive steps towards leading healthier lifestyles.
{"title":"BeWell+: multi-dimensional wellbeing monitoring with community-guided user feedback and energy optimization","authors":"Mu Lin, N. Lane, Mashfiqui Mohammod, Xiaochao Yang, Hong Lu, Giuseppe Cardone, Shahid Ali, Afsaneh Doryab, E. Berke, A. Campbell, Tanzeem Choudhury","doi":"10.1145/2448096.2448106","DOIUrl":"https://doi.org/10.1145/2448096.2448106","url":null,"abstract":"Smartphone sensing and persuasive feedback design is enabling a new generation of wellbeing applications capable of automatically monitoring multiple aspects of physical and mental health. In this paper, we present BeWell+ the next generation of the BeWell smartphone health app, which continuously monitors user behavior along three distinct health dimensions, namely sleep, physical activity, and social interaction. BeWell promotes improved behavioral patterns via feedback rendered as an ambient display on the smartphone's wallpaper. With BeWell+, we introduce new wellbeing mechanisms to address challenges identified during the initial deployment of the BeWell app; specifically, (i) community adaptive wellbeing feedback, which automatically generalize to diverse user communities (e.g., elderly, young adults, children) by balancing the need to promote better behavior yet remains realistic to the user's goals; and, (ii) wellbeing adaptive energy allocation, which prioritizes monitoring fidelity and feedback responsiveness on specific health dimensions of wellbeing (e.g., social interaction) where the user needs most help. We evaluate the performance of these mechanisms as part of an initial deployment and user study that includes 27 people using BeWell+ over a 19 day field trial. Our findings show that not only can BeWell+ operate successfully on consumer-grade smartphones, but users understand feedback and respond by taking positive steps towards leading healthier lifestyles.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"28 1","pages":"10:1-10:8"},"PeriodicalIF":0.0,"publicationDate":"2012-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79271070","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}
Thomas R. Kirchner, Jennifer Cantrell, Andrew Anesetti-Rothermel, Jennifer L. Pearson, Sarah Cha, Jennifer M Kreslake, Ollie Ganz, Michael Tacelosky, D. Abrams, D. Vallone
Health-related behaviors occur as part of a broad socio-ecological context that unfolds dynamically over time. Yet systematic quantification of the way individuals come into contact with health-related features in their local environment remains a difficult challenge. Doing so requires a multi-tiered approach that integrates both individual geo-location data and comprehensive community-level information about health-related features in the local built environment. This report describes the implementation of a system for quantification of real-time exposure to point-of-sale tobacco marketing via mobile phone geo-location tracking. Individual mobility patterns from a longitudinal cohort of DC residents (N=486) were overlaid on an existing community-level point-of-sale surveillance geodatabase (N=1, 080 stores). Participants were DC residents who carried a geo-location tracking device over the first 8-weeks of a smoking cessation attempt. Tracking data were then used to produce a mobility "signature," physically linking each person to their surrounding point-of-sale marketing environment in real-time. Results demonstrate the dynamic nature of an individuals' experience of the point-of-sale environment. We identify substantial between-person differences in tobacco product pricing exposure, and find that these correspond to clusters of individuals whose price exposures vary systematically over time of day. These data suggest that perceptions of the point-of-sale environment as relatively static fail to account for the mobility and preferences of individuals as they actively engage with their neighborhoods over time.
{"title":"Individual mobility patterns and real-time geo-spatial exposure to point-of-sale tobacco marketing","authors":"Thomas R. Kirchner, Jennifer Cantrell, Andrew Anesetti-Rothermel, Jennifer L. Pearson, Sarah Cha, Jennifer M Kreslake, Ollie Ganz, Michael Tacelosky, D. Abrams, D. Vallone","doi":"10.1145/2448096.2448104","DOIUrl":"https://doi.org/10.1145/2448096.2448104","url":null,"abstract":"Health-related behaviors occur as part of a broad socio-ecological context that unfolds dynamically over time. Yet systematic quantification of the way individuals come into contact with health-related features in their local environment remains a difficult challenge. Doing so requires a multi-tiered approach that integrates both individual geo-location data and comprehensive community-level information about health-related features in the local built environment.\u0000 This report describes the implementation of a system for quantification of real-time exposure to point-of-sale tobacco marketing via mobile phone geo-location tracking. Individual mobility patterns from a longitudinal cohort of DC residents (N=486) were overlaid on an existing community-level point-of-sale surveillance geodatabase (N=1, 080 stores). Participants were DC residents who carried a geo-location tracking device over the first 8-weeks of a smoking cessation attempt. Tracking data were then used to produce a mobility \"signature,\" physically linking each person to their surrounding point-of-sale marketing environment in real-time.\u0000 Results demonstrate the dynamic nature of an individuals' experience of the point-of-sale environment. We identify substantial between-person differences in tobacco product pricing exposure, and find that these correspond to clusters of individuals whose price exposures vary systematically over time of day. These data suggest that perceptions of the point-of-sale environment as relatively static fail to account for the mobility and preferences of individuals as they actively engage with their neighborhoods over time.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"63 1","pages":"8:1-8:8"},"PeriodicalIF":0.0,"publicationDate":"2012-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86029150","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}
Robert F. Dickerson, Enamul Hoque, J. Stankovic, David Gerdt, Joel G. Anderson, Ann G. Taylor
One of the most commonly identified precipitant of seizures for people with epilepsy is stress. We have developed a system for tracking objective measures in the subjects homes. An integrated system is used for data collection, uploading, and viewing the data. Our system is currently deployed and actively collecting data from people suffering from epilepsy and enrolled in a meditative relaxation course.
{"title":"Tracking influence of reflective exercise for persons with epilepsy","authors":"Robert F. Dickerson, Enamul Hoque, J. Stankovic, David Gerdt, Joel G. Anderson, Ann G. Taylor","doi":"10.1145/2448096.2448110","DOIUrl":"https://doi.org/10.1145/2448096.2448110","url":null,"abstract":"One of the most commonly identified precipitant of seizures for people with epilepsy is stress. We have developed a system for tracking objective measures in the subjects homes. An integrated system is used for data collection, uploading, and viewing the data. Our system is currently deployed and actively collecting data from people suffering from epilepsy and enrolled in a meditative relaxation course.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"28 1","pages":"14:1-14:2"},"PeriodicalIF":0.0,"publicationDate":"2012-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73449402","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}
This paper describes a technique for non-contact measurement of cardiac signals (ECG) from the driver of a car, with a wireless interface, thus allowing continuous monitoring of driver health. The system employs an ultra high impedance solid state electric field sensor - Electric Potential Integrated Circuit (EPIC) - developed by Plessey Semiconductors Ltd and the University of Sussex [1]. Recent developments in both technology and understanding have resulted in a reliable technique for measuring ECG through clothing by means of sensors embedded in the driver's seat. A Bluetooth interface enables transmission of the data to a monitoring system, either in-car or on a mobile device such as a smart phone or tablet. The data can be monitored in "real time", or subsequently sent over mobile or cloud computing networks to a remote server for analysis.
{"title":"Wireless, non-contact driver's ECG monitoring system","authors":"S. Mukherjee, Robert Breakspear, Sean D. Connor","doi":"10.1145/2448096.2448112","DOIUrl":"https://doi.org/10.1145/2448096.2448112","url":null,"abstract":"This paper describes a technique for non-contact measurement of cardiac signals (ECG) from the driver of a car, with a wireless interface, thus allowing continuous monitoring of driver health. The system employs an ultra high impedance solid state electric field sensor - Electric Potential Integrated Circuit (EPIC) - developed by Plessey Semiconductors Ltd and the University of Sussex [1].\u0000 Recent developments in both technology and understanding have resulted in a reliable technique for measuring ECG through clothing by means of sensors embedded in the driver's seat. A Bluetooth interface enables transmission of the data to a monitoring system, either in-car or on a mobile device such as a smart phone or tablet. The data can be monitored in \"real time\", or subsequently sent over mobile or cloud computing networks to a remote server for analysis.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"C-20 1","pages":"16:1-16:2"},"PeriodicalIF":0.0,"publicationDate":"2012-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85064014","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}
Rahav Dor, Gregory Hackmann, Zhicheng Yang, Chenyang Lu, Yixin Chen, M. Kollef, T. Bailey
Wireless sensor networks can play an important role in improving patient care by collecting continuous vital signs for clinical decision support. This paper presents the architecture of, and our experiences with, a large-scale wireless clinical monitoring system. Our system encompasses portable wireless pulse oximeters, a wireless relay network spanning multiple hospital floors, and integration into the hospital Electronic Medical Record (EMR) databases. We report our experience and lessons learned from a 14-month clinical trial of the system in six hospital wards of Barnes-Jewish Hospital in St. Louis, Missouri. Our experiences show the feasibility of achieving reliable vital sign collection, using a wireless sensor network integrated with hospital IT infrastructure and procedures. We highlight technical and non-technical elements that pose challenges in a real-world hospital environment and provide guidelines for successful and efficient deployment of similar systems.
{"title":"Experiences with an end-to-end wireless clinical monitoring system","authors":"Rahav Dor, Gregory Hackmann, Zhicheng Yang, Chenyang Lu, Yixin Chen, M. Kollef, T. Bailey","doi":"10.1145/2448096.2448100","DOIUrl":"https://doi.org/10.1145/2448096.2448100","url":null,"abstract":"Wireless sensor networks can play an important role in improving patient care by collecting continuous vital signs for clinical decision support. This paper presents the architecture of, and our experiences with, a large-scale wireless clinical monitoring system. Our system encompasses portable wireless pulse oximeters, a wireless relay network spanning multiple hospital floors, and integration into the hospital Electronic Medical Record (EMR) databases. We report our experience and lessons learned from a 14-month clinical trial of the system in six hospital wards of Barnes-Jewish Hospital in St. Louis, Missouri. Our experiences show the feasibility of achieving reliable vital sign collection, using a wireless sensor network integrated with hospital IT infrastructure and procedures. We highlight technical and non-technical elements that pose challenges in a real-world hospital environment and provide guidelines for successful and efficient deployment of similar systems.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"6 1","pages":"4:1-4:8"},"PeriodicalIF":0.0,"publicationDate":"2012-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90158107","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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