High accuracy context recovery using clustering mechanisms
Phung, Dinh, Adams, Brett, Tran, Kha, Venkatesh, Svetha and Kumar, Mohan 2009, High accuracy context recovery using clustering mechanisms, in PerCom 2009 : Proceedings of the 7th Annual IEEE International Conference on Pervasive Computing and Communications, IEEE, [Washington, D. C.], pp. 2-9.
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 state-of-the-art probabilistic clustering technique, the Latent Dirichiet 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.
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ISBN
9781424433049
Language
eng
Field of Research
089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences
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