A hierarchical approach to real-time activity recognition in body sensor networks

Liang Wang, Tao Gu*, Xianping Tao, Jian Lu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Pages (from-to)115-130
Number of pages16
JournalPervasive and Mobile Computing
Volume8
Issue number1
DOIs
Publication statusPublished - Feb 2012
Externally publishedYes

Keywords

  • Real-time activity recognition
  • Pattern mining
  • Wireless body sensor network

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