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. Attached Files Name Description MIMEType Size Downloads phung-highaccuracy-2009.pdf Published version application/pdf 275.86KB 264 Title High accuracy context recovery using clustering mechanisms Author(s) Phung, Dinh orcid.org/0000-0002-9977-8247 Adams, Brett Tran, Kha 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 Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30044569 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Fri, 20 Apr 2012, 11:32:51 EST Thu, 26 Apr 2012, 15:38:33 EST Thu, 26 Apr 2012, 15:40:40 EST Tue, 01 May 2012, 15:45:26 EST Tue, 06 Sep 2016, 18:43:21 EST Tue, 05 Dec 2017, 12:31:21 EST Fri, 07 Sep 2018, 15:51:52 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 21 times in Scopus Search Google Scholar Access Statistics: 636 Abstract Views, 264 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:32:51 EST

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