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Physical activity classification using a single triaxial accelerometer based on HMM

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4 Author(s)
Aiguang Li ; Inst. of Autom., Sensor Networks & Applic. Joint Res. Center (SNARC), Grad. Univ. of the Chinese Acad. of Sci., Beijing, China ; Lianying Ji ; Shaofeng Wang ; Jiankang Wu

This study focuses on physical activity classification method using a single triaxial accelerometer attached on chest. With acceleration data acquired by a wearable wireless device, features are extracted using sliding window to describe different activity types. Hidden Markov Model (HMM) is used to recognize physical activity sequence. A modified Viterbi algorithm is used to find the optimal state sequence. The experimental results on 6 subjects have achieved an overall accuracy of 99.59% using our method, which is the best result so far.

Published in:

Wireless Sensor Network, 2010. IET-WSN. IET International Conference on

Date of Conference:

15-17 Nov. 2010