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%.
Original language | English |
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Pages (from-to) | 115-130 |
Number of pages | 16 |
Journal | Pervasive and Mobile Computing |
Volume | 8 |
Issue number | 1 |
DOIs | |
Publication status | Published - Feb 2012 |
Externally published | Yes |
Keywords
- Real-time activity recognition
- Pattern mining
- Wireless body sensor network