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Sensor-Based Human Activity Recognition in a Multi-user Scenario

  • Liang Wang
  • Tao Gu
  • Xianping Tao
  • Jian Lu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5859)

Abstract

Existing work on sensor-based activity recognition focuses mainly on single-user activities. However, in real life, activities are often performed by multiple users involving interactions between them. In this paper, we propose Coupled Hidden Markov Models (CHMMs) to recognize multi-user activities from sensor readings in a smart home environment. We develop a multimodal sensing platform and present a theoretical framework to recognize both single-user and multi-user activities. We conduct our trace collection done in a smart home, and evaluate our framework through experimental studies. Our experimental result shows that we achieve an average accuracy of 85.46% with CHMMs.

Keywords

Multi-user activity recognition probabilistic model 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Liang Wang
    • 1
  • Tao Gu
    • 2
  • Xianping Tao
    • 1
  • Jian Lu
    • 1
  1. 1.State Key Laboratory for Novel Software TechnologyNanjing University 
  2. 2.Department of Mathematics and Computer ScienceUniversity of Southern Denmark 

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