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%. © 2010 Elsevier B.V. All rights reserved.
CITATION STYLE
Wang, L., Gu, T., Tao, X., & Lu, J. (2012). A hierarchical approach to real-time activity recognition in body sensor networks. Pervasive and Mobile Computing, 8(1), 115–130. https://doi.org/10.1016/j.pmcj.2010.12.001
Mendeley helps you to discover research relevant for your work.