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A bayesian nonparametric framework for activity recognition using accelerometer data
conference contribution
posted on 2014-12-04, 00:00 authored by Cong Thuong Nguyen, Sunil GuptaSunil Gupta, Svetha VenkateshSvetha Venkatesh, Quoc-Dinh PhungMonitoring 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.
History
Event
Pattern Recognition. Conference (22nd : 2014 : Stockholm, Sweden)Pagination
2017 - 2022Publisher
IEEELocation
Stockholm, SwedenPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2014-08-24End date
2014-08-28ISSN
1051-4651ISBN-13
9781479952083Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2014, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
ICPR 2014 : Proceedings of the 22nd International Conference on Pattern RecognitionUsage metrics
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