A Bayesian nonparametric framework for activity recognition using accelerometer data

Thuong Nguyen, Sunil Gupta, Svetha Venkatesh, Dinh Phung

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

11 Citations (Scopus)


Monitoring daily physical activity of human plays an important role in preventing diseases as well as improving health. In this paper, we demonstrate a framework for monitoring the physical activity levels in daily life. We collect the data using accelerometer sensors in a realistic setting without any supervision. The ground truth of activities is provided by the participants themselves using an experience sampling application running on mobile phones. The original data is discretized by the hierarchical Dirichlet process (HDP) into different activity levels and the number of levels is inferred automatically. We validate the accuracy of the extracted patterns by using them for the multi-label classification of activities and demonstrate the high performances in various standard evaluation metrics. We further show that the extracted patterns are highly correlated to the daily routine of users.

Original languageEnglish
Title of host publicationProceedings - 22nd International Conference on Pattern Recognition, ICPR 2014
Subtitle of host publication24–28 August 2014 Stockholm, Sweden Los Alamitos
EditorsAnders Heyden, Denis Laurendeau, Michael Felsberg
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781479952083, 9781479952090
Publication statusPublished - 2014
Externally publishedYes
EventInternational Conference on Pattern Recognition 2014 - Stockholm, Sweden
Duration: 24 Aug 201428 Aug 2014
Conference number: 22nd
https://ieeexplore.ieee.org/xpl/conhome/6966883/proceeding (Proceedings)


ConferenceInternational Conference on Pattern Recognition 2014
Abbreviated titleICPR 2014
Internet address

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