A bayesian nonparametric framework for activity recognition using accelerometer data

Nguyen,T, Gupta,S, Venkatesh,S and Phung,D 2014, A bayesian nonparametric framework for activity recognition using accelerometer data, in ICPR 2014 : Proceedings of the 22nd International Conference on Pattern Recognition, IEEE, Piscataway, N.J., pp. 2017-2022, doi: 10.1109/ICPR.2014.352.

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Title A bayesian nonparametric framework for activity recognition using accelerometer data
Author(s) Nguyen,T
Gupta,SORCID iD for Gupta,S orcid.org/0000-0002-3308-1930
Venkatesh,SORCID iD for Venkatesh,S orcid.org/0000-0001-8675-6631
Phung,DORCID iD for Phung,D orcid.org/0000-0002-9977-8247
Conference name Pattern Recognition. Conference (22nd : 2014 : Stockholm, Sweden)
Conference location Stockholm, Sweden
Conference dates 24-28 Aug. 2014
Title of proceedings ICPR 2014 : Proceedings of the 22nd International Conference on Pattern Recognition
Editor(s) [Unknown]
Publication date 2014
Conference series Pattern Recognition Conference
Start page 2017
End page 2022
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Summary 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.
ISBN 9781479952083
ISSN 1051-4651
Language eng
DOI 10.1109/ICPR.2014.352
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070022

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Citation counts: TR Web of Science Citation Count  Cited 8 times in TR Web of Science
Scopus Citation Count Cited 9 times in Scopus
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