Title :
Real-Time Activity Recognition in Wireless Body Sensor Networks: From Simple Gestures to Complex Activities
Author :
Wang, Liang ; Gu, Tao ; Chen, Hanhua ; Tao, Xianping ; Lu, Jian
Author_Institution :
State Key Lab. for Novel Software Technol., Nanjing Univ., Nanjing, China
Abstract :
Real-time activity recognition using body sensor networks is an important and challenging task and it has many potential applications. 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, lightweight template matching algorithm to detect gestures at the sensor node level, and then use a discriminative pattern based real-time algorithm to recognize 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 precision of 94.9%, an average recall of 82.5%, and an average real-time delay of 5.7 seconds), but also significantly reduces the network communication cost by 60.2%.
Keywords :
body sensor networks; gesture recognition; image matching; real-time systems; lightweight template matching algorithm; real-time activity recognition; real-time delay; simple gesture recognition; wireless body sensor networks; Acceleration; Algorithm design and analysis; Body sensor networks; Gesture recognition; Real time systems; Wireless communication; Real-time activity recognition; gestures and highlevel activities; wireless body sensor networks;
Conference_Titel :
Embedded and Real-Time Computing Systems and Applications (RTCSA), 2010 IEEE 16th International Conference on
Conference_Location :
Macau SAR
Print_ISBN :
978-1-4244-8480-5
DOI :
10.1109/RTCSA.2010.19