High accuracy context recovery using clustering mechanisms

Dinh Phung, Brett Adams, Kha Tran, Svetha Venkatesh, Mohan Kumar

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

21 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication7th Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2009
DOIs
Publication statusPublished - 28 Sep 2009
Externally publishedYes
EventIEEE International Conference on Pervasive Computing and Communications 2009 - Galveston, United States of America
Duration: 9 Mar 200913 Mar 2009
Conference number: 7th
https://ieeexplore.ieee.org/xpl/conhome/4906860/proceeding

Publication series

Name7th Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2009

Conference

ConferenceIEEE International Conference on Pervasive Computing and Communications 2009
Abbreviated titlePerCom 2009
CountryUnited States of America
CityGalveston
Period9/03/0913/03/09
Internet address

Cite this