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Proceedings Paper

Energy-aware activity classification using wearable sensor networks
Author(s): Bo Dong; Alexander Montoye; Rebecca Moore; Karin Pfeiffer; Subir Biswas
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Paper Abstract

This paper presents implementation details, system characterization, and the performance of a wearable sensor network that was designed for human activity analysis. Specific machine learning mechanisms are implemented for recognizing a target set of activities with both out-of-body and on-body processing arrangements. Impacts of energy consumption by the on-body sensors are analyzed in terms of activity detection accuracy for out-of-body processing. Impacts of limited processing abilities for the on-body scenario are also characterized in terms of detection accuracy, by varying the background processing load in the sensor units. Impacts of varying number of sensors in terms of activity classification accuracy are also evaluated. Through a rigorous systems study, it is shown that an efficient human activity analytics system can be designed and operated even under energy and processing constraints of tiny on-body wearable sensors.

Paper Details

Date Published: 29 May 2013
PDF: 7 pages
Proc. SPIE 8723, Sensing Technologies for Global Health, Military Medicine, and Environmental Monitoring III, 87230Y (29 May 2013); doi: 10.1117/12.2018134
Show Author Affiliations
Bo Dong, Michigan State Univ. (United States)
Alexander Montoye, Michigan State Univ. (United States)
Rebecca Moore, Michigan State Univ. (United States)
Karin Pfeiffer, Michigan State Univ. (United States)
Subir Biswas, Michigan State Univ. (United States)


Published in SPIE Proceedings Vol. 8723:
Sensing Technologies for Global Health, Military Medicine, and Environmental Monitoring III
Šárka O. Southern, Editor(s)

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