A hierarchical approach to real-time activity recognition in body sensor networks

Wang, L, Gu, T, Tao, X and Lu, J 2012, 'A hierarchical approach to real-time activity recognition in body sensor networks', Pervasive and Mobile Computing, vol. 8, no. 1, pp. 115-130.


Document type: Journal Article
Collection: Journal Articles

Title A hierarchical approach to real-time activity recognition in body sensor networks
Author(s) Wang, L
Gu, T
Tao, X
Lu, J
Year 2012
Journal name Pervasive and Mobile Computing
Volume number 8
Issue number 1
Start page 115
End page 130
Total pages 16
Publisher Elsevier
Abstract Real-time activity recognition in body sensor networks is an important and challenging task. In this paper, we propose a real-time, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this model, we first use a fast and lightweight algorithm to detect gestures at the sensor node level, and then propose a pattern based real-time algorithm to recognize complex, high-level activities at the portable device level. We evaluate our algorithms over a real-world dataset. The results show that the proposed system not only achieves good performance (an average utility of 0.81, an average accuracy of 82.87%, and an average real-time delay of 5.7 seconds), but also significantly reduces the network's communication cost by 60.2%.
Subject Ubiquitous Computing
Keyword(s) Real-time activity recognition
pattern mining
wireless body sensor network
DOI - identifier 10.1016/j.pmcj.2010.12.001
Copyright notice © 2010 Elsevier B.V. All rights reserved.
ISSN 1574-1192
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