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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.

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Title High accuracy context recovery using clustering mechanisms
Author(s) Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Adams, Brett
Tran, Kha
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Kumar, Mohan
Conference name International Conference on Pervasive Computing and Communications (7th : 2009 : Galveston, Tex.)
Conference location Galveston, Tex.
Conference dates 9-13 Mar. 2009
Title of proceedings PerCom 2009 : Proceedings of the 7th Annual IEEE International Conference on Pervasive Computing and Communications
Editor(s) [Unknown]
Publication date 2009
Conference series International Conference on Pervasive Computing and Communications
Start page 2
End page 9
Total pages 9
Publisher IEEE
Place of publication [Washington, D. C.]
Keyword(s) bluetooth
context
data mining
delay
global positioning system
mobile computing
pervasive computing
rhythm
sensor phenomena and characterization
thermal sensors
Summary 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.
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.
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
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2009, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044569

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.