Unsupervised context detection using wireless signals

  • Authors:

  • Dinh Phung;Brett Adams;Svetha Venkatesh;Mohan Kumar

  • Affiliations:

  • Department of Computing, Curtin University of Technology, GPO Box U1987, Perth, WA 6845, Australia;Department of Computing, Curtin University of Technology, GPO Box U1987, Perth, WA 6845, Australia;Department of Computing, Curtin University of Technology, GPO Box U1987, Perth, WA 6845, Australia;Department of Computer Science and Engineering, The University of Texas, Box 19015, Arlington, TX 76019, USA

  • Venue:
  • Pervasive and Mobile Computing
  • Year:
  • 2009

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Abstract

The sensing context plays an important role in many pervasive and mobile computing applications. Continuing from previous work [D. Phung, B. Adams, S. Venkatesh, Computable social patterns from sparse sensor data, in: Proceedings of First International Workshop on Location Web, World Wide Web Conference (WWW), New York, NY, USA, 2008, ACM 69-72.], we present an unsupervised framework for extracting user context in indoor environments with existing wireless infrastructures. Our novel approach casts context detection into an incremental, unsupervised clustering setting. Using WiFi observations consisting of access point identification and signal strengths freely available in office or public spaces, we adapt a density-based clustering technique to recover basic forms of user contexts that include user motion state and significant places the user visits from time to time. High-level user context, termed rhythms, comprising sequences of significant places are derived from the above low-level context by employing probabilistic clustering techniques, latent Dirichlet allocation and its n-gram temporal extension. These user contexts can enable a wide range of context-ware application services. Experimental results with real data in comparison with existing methods are presented to validate the proposed approach. Our motion classification algorithm operates in real-time, and achieves a 10% improvement over an existing method; significant locations are detected with over 90% accuracy and near perfect cluster purity. Richer indoor context and meaningful rhythms, such as typical daily routines or meeting patterns, are also inferred automatically from collected raw WiFi signals.