EAGER- Events of Interest (EoI) Capture Using Novel Body-worn Fully-passive Wireless Sensors for S&CC
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A smart and connected community (S&CC) will utilize distributed sensors and embedded computing to seamlessly generate meaningful interpretations that would be of great benefit to individuals, the community, and society, in general. Although rapidly emerging mobile health technology is already tapping into widely used smartphone infrastructure, data collection using smartphone mobile devices is currently limited by the availability of few integrated sensors (e.g., Inertial measurement unit (IMU), camera, optical sensors, temperature sensor, and GPS). In this project, we investigate opportunities to advance the S&CC by incorporating capabilities from external battery-less sensors into this framework. This research will: (1) deliver a platform for fully-passive wireless electronic patch sensors, called WRAP (Wireless Resistive Analog Passive) sensors, to collect physiological data and to incorporate multimodal sensor data for real-time classification, (2) develop an open-source framework for meaningful and reliable Events-of-Interest (EoI) detection using a custom smartphone app for patient self-monitoring and communal sharing of de-identified EoIs, and (3) deploy the sensors in a “Living Lab” within a pilot study where community members will self-monitor and apply the framework to generate EoIs for various health conditions, such as arrhythmia, asthma, fever, and sleep disorder. The interdisciplinary research team is collaborating with the non-profit Memphis District of the United Methodist Church located in the greater Memphis community to complete this work. Some functional prototypes of WRAP sensors have been developed on paper substrate. The first phase design of a portable scanner (6"Å~3") device has been completed. The effect of antenna orientation due to coil separation and various coil-plane angles has been studied using COMSOL Finite Element Analysis (FEA) method. An algorithm for automated arrhythmia classification has been developed based on ECG database from PhysioBank. The resulting sensors and smartphone app will empower users, permit the S&CC to assess population health status related information, reduce the need for frequent hospital visits, and help identify potential individual and community actions to achieve improvement in health status.
Submitted by Bashir Morshed
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A smart and connected community (S&CC) will utilize distributed sensors and embedded computing to seamlessly generate meaningful interpretations that would be of great benefit to individuals, the community, and society, in general. Although rapidly emerging mobile health technology is already tapping into widely used smartphone infrastructure, data collection using smartphone mobile devices is currently limited by the availability of few integrated sensors (e.g., Inertial measurement unit (IMU), camera, optical sensors, temperature sensor, and GPS). In this project, we investigate opportunities to advance the S&CC by incorporating capabilities from external battery-less sensors into this framework. This research will: (1) deliver a platform for fully-passive wireless electronic patch sensors, called WRAP (Wireless Resistive Analog Passive) sensors, to collect physiological data and to incorporate multimodal sensor data for real-time classification, (2) develop an open-source framework for meaningful and reliable Events-of-Interest (EoI) detection using a custom smartphone app for patient self-monitoring and communal sharing of de-identified EoIs, and (3) deploy the sensors in a “Living Lab” within a pilot study where community members will self-monitor and apply the framework to generate EoIs for various health conditions, such as arrhythmia, asthma, fever, and sleep disorder. The interdisciplinary research team is collaborating with the non-profit Memphis District of the United Methodist Church located in the greater Memphis community to complete this work. Some functional prototypes of WRAP sensors have been developed on paper substrate. The first phase design of a portable scanner (6"Å~3") device has been completed. The effect of antenna orientation due to coil separation and various coil-plane angles has been studied using COMSOL Finite Element Analysis (FEA) method. An algorithm for automated arrhythmia classification has been developed based on ECG database from PhysioBank. The resulting sensors and smartphone app will empower users, permit the S&CC to assess population health status related information, reduce the need for frequent hospital visits, and help identify potential individual and community actions to achieve improvement in health status.