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Open AccessArticle

Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses

1
Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695, USA
2
Department of Pediatrics, University of North Carolina School of Medicine, Chapel Hill, NC 27516, USA
*
Author to whom correspondence should be addressed.
Current address: Engineering Bldg II, 890 Oval Dr, Raleigh, NC 27695-7911, USA.
Sensors 2019, 19(3), 441; https://doi.org/10.3390/s19030441
Received: 31 December 2018 / Revised: 17 January 2019 / Accepted: 18 January 2019 / Published: 22 January 2019
(This article belongs to the Special Issue Computational Intelligence-Based Sensors)
Wearable health monitoring has emerged as a promising solution to the growing need for remote health assessment and growing demand for personalized preventative care and wellness management. Vital signs can be monitored and alerts can be made when anomalies are detected, potentially improving patient outcomes. One major challenge for the use of wearable health devices is their energy efficiency and battery-lifetime, which motivates the recent efforts towards the development of self-powered wearable devices. This article proposes a method for context aware dynamic sensor selection for power optimized physiological prediction using multi-modal wearable data streams. We first cluster the data by physical activity using the accelerometer data, and then fit a group lasso model to each activity cluster. We find the optimal reduced set of groups of sensor features, in turn reducing power usage by duty cycling these and optimizing prediction accuracy. We show that using activity state-based contextual information increases accuracy while decreasing power usage. We also show that the reduced feature set can be used in other regression models increasing accuracy and decreasing energy burden. We demonstrate the potential reduction in power usage using a custom-designed multi-modal wearable system prototype. View Full-Text
Keywords: wearable health; physiological prediction; activity clustering; multi-modal data; Body Sensor Networks; sensor selection; power efficient sensing wearable health; physiological prediction; activity clustering; multi-modal data; Body Sensor Networks; sensor selection; power efficient sensing
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Starliper, N.; Mohammadzadeh, F.; Songkakul, T.; Hernandez, M.; Bozkurt, A.; Lobaton, E. Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses. Sensors 2019, 19, 441.

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