A hierarchical approach to real-time activity recognition in body sensor networks |
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Authors: | Liang Wang Tao Gu Xianping Tao Jian Lu |
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Affiliation: | 1. State Key Laboratory for Novel Software Technology, Nanjing University, China;2. Department of Mathematics and Computer Science, University of Southern Denmark, Denmark |
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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%. |
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