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dc.contributor.authorPhung, Dinh
dc.contributor.authorAdams, Brett
dc.contributor.authorVenkatesh, Svetha
dc.contributor.authorKumar, Mohan
dc.identifier.citationPhung, Dinh and Adams, Brett and Venkatesh, Svetha and Kumar, Mohan. 2009. Unsupervised context detection using wireless signals. Pervasive and Mobile Computing. 5 (6): pp. 714-733.

The sensing context plays an important role in many pervasive and mobile computingapplications. Continuing from previous work [D. Phung, B. Adams, S. Venkatesh,Computable social patterns from sparse sensor data, in: Proceedings of First InternationalWorkshop on Location Web, World Wide Web Conference (WWW), New York, NY,USA, 2008, ACM 6972.], we present an unsupervised framework for extracting usercontext in indoor environments with existing wireless infrastructures. Our novel approachcasts context detection into an incremental, unsupervised clustering setting. Using WiFiobservations consisting of access point identification and signal strengths freely availablein office or public spaces, we adapt a density-based clustering technique to recover basicforms of user contexts that include user motion state and significant places the user visitsfrom time to time. High-level user context, termed rhythms, comprising sequences ofsignificant places are derived from the above low-level context by employing probabilisticclustering techniques, latent Dirichlet allocation and its n-gram temporal extension. Theseuser contexts can enable a wide range of context-ware application services. Experimentalresults with real data in comparison with existing methods are presented to validate theproposed approach. Our motion classification algorithm operates in real-time, and achievesa 10% improvement over an existing method; significant locations are detected withover 90% accuracy and near perfect cluster purity. Richer indoor context and meaningfulrhythms, such as typical daily routines or meeting patterns, are also inferred automaticallyfrom collected raw WiFi signals.

dc.publisherElsevier Science publisher B.V.Amsterdam
dc.subjectContext modeling - Spatio-temporal rhythm extraction - Probabilistic topic models - Hidden markov models - Unsupervised learning - Wireless signals
dc.titleUnsupervised context detection using wireless signals
dc.typeJournal Article
dcterms.source.titlePervasive and Mobile Computing

The link to the journal’s home page is: Copyright © 2009 Elsevier B.V. All rights reserved

curtin.accessStatusOpen access
curtin.facultySchool of Science and Computing
curtin.facultyDepartment of Computing
curtin.facultyFaculty of Science and Engineering

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