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High accuracy context recovery using clustering mechanisms

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conference contribution
posted on 2009-01-01, 00:00 authored by Quoc-Dinh Phung, B Adams, K Tran, Svetha VenkateshSvetha Venkatesh, M Kumar
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.

History

Pagination

2 - 9

Location

Galveston, Tex.

Open access

  • Yes

Start date

2009-03-09

End date

2009-03-13

ISBN-13

9781424433049

Language

eng

Notes

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2009, IEEE

Title of proceedings

PerCom 2009 : Proceedings of the 7th Annual IEEE International Conference on Pervasive Computing and Communications

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