Journal
PERVASIVE AND MOBILE COMPUTING
Volume 8, Issue 1, Pages 115-130Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.pmcj.2010.12.001
Keywords
Real-time activity recognition; Pattern mining; Wireless body sensor network
Funding
- Danish Council for Independent Research Natural Science [09-073281]
- National 973 program of China [2009CB320702]
- Natural Science Foundation of China [60736015, 60721002, 61073031]
- Jiangsu PanDeng Program [BK2008017]
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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%. (C) 2010 Elsevier B.V. All rights reserved.
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