Human activity recognition with trajectory data in multi-floor indoor environment

  • Authors:

  • Xu Zhang;Goung-Bae Kim;Ying Xia;Hae-Young Bae

  • Affiliations:

  • Department of Computer Science, Inha University, South Korea;Department of Computer Education, Seowon University, South Korea;College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, China;Department of Computer Science, Inha University, South Korea

  • Venue:
  • RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
  • Year:
  • 2012

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Abstract

In pervasive and context-awareness computing, transferring user movement to activity knowledge in indoor is an important yet challenging task, especially in multi-floor environments. In this paper, we propose a new semantic model describing trajectories in multi-floor environment, and then N-gram model is implemented for transferring trajectory to human activity knowledge. Our method successfully alleviates the common problem of indoor movement representation and activity recognition accuracy affected by wireless signal calibration. Experimental implementation and analysis on both real and synthetic dataset exhibit that our proposed method can effectively process with indoor movement, and it renders good performance in accuracy and robustness for activity recognition with less calibration effort.