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High accuracy context recovery using clustering mechanisms

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5 Author(s)
Phung, D. ; Dept. of Comput., Curtin Univ. of Technol., Perth, WA ; Adams, B. ; Tran, K. ; Venkatesh, S.
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This paper examines the recovery of user context in indoor environmnents with existing wireless infrastructures to enable assistive systems. We present a novel approach to the extraction of user context, casting the problem of context recovery as an unsupervised, clustering problem. A well known density-based clustering technique, DBSCAN, is adapted to recover user context that includes user motion state, and significant places the user visits from WiFi observations consisting of access point ID and signal strength. Furthermore, user rhythms or sequences of places the user visits periodically are derived from the above low level contexts by employing a state-of-the-art probabilistic clustering technique, the Latent Dirichlet Allocation (LDA), to enable a variety of application services. Experimental results with real data are presented to validate the proposed unsupervised learning approach and demonstrate its applicability.

Published in:

Pervasive Computing and Communications, 2009. PerCom 2009. IEEE International Conference on

Date of Conference:

9-13 March 2009