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Philadelphia, PA
April 9, 2013 to April 11, 2013
ISBN: 978-1-4799-0186-9
pp: 163-172
Zhen Ren , Department of Computer Science, College of William and Mary, Williamsburg, VA, USA
In a Body Sensor Network (BSN) activity recognition system, sensor sampling and communication quickly deplete battery reserves. While reducing sampling and communication saves energy, this energy savings usually comes at the cost of reduced recognition accuracy. To address this challenge, we propose AdaSense, a framework that reduces the BSN sensors sampling rate while meeting a user-specified accuracy requirement. AdaSense utilizes a classifier set to do either multi-activity classification that requires a high sampling rate or single activity event detection that demands a very low sampling rate. AdaSense aims to utilize lower power single activity event detection most of the time. It only resorts to higher power multi-activity classification to find out the new activity when it is confident that the activity changes. Furthermore, AdaSense is able to determine the optimal sampling rates using a novel Genetic Programming algorithm. Through this Genetic Programming approach, AdaSense reduces sampling rates for both lower power single activity event detection and higher power multi-activity classification. With an existing BSN dataset and a smartphone dataset we collect from eight subjects, we demonstrate that AdaSense effectively reduces BSN sensors sampling rate and outperforms a state-of-the-art solution in terms of energy savings.
Body Sensor Network, Activity Recognition, Sampling Rate Reduction
Zhen Ren, "AdaSense: Adapting sampling rates for activity recognition in Body Sensor Networks", RTAS, 2013, 2013 IEEE 19th Real-Time and Embedded Technology and Applications Symposium (RTAS), 2013 IEEE 19th Real-Time and Embedded Technology and Applications Symposium (RTAS) 2013, pp. 163-172, doi:10.1109/RTAS.2013.6531089
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