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High accuracy context recovery using clustering mechanisms

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Open access
Authors
Phung, Dinh
Adams, Brett
Tran, Kha
Venkatesh, Svetha
Kumar, Mohan
Date
2009
Type
Conference Paper

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Citation
Phung, Dinh and Adams, Brett and Tran, Kha and Venkatesh, Svetha and Kumar, Mohan. 2009. High accuracy context recovery using clustering mechanisms, in Unknown (ed), PERCOM 2009, Mar 9 2009, pp. 1-9.Galveston, TX, USA: IEEE Computer Society.
Source Title
Proceeding of the 2009 IEEE international conference on Pervasive computing and communications
Source Conference
PERCOM 2009
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Faculty
School of Science and Computing
Department of Computing
Faculty of Science and Engineering
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Abstract

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.