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 Magnus Borga (ed.) Proceedings of the 22nd International Conference on Pattern Recognition (ICPR 2014), Stockholm, Sweden, 24-28 August 2014, pp. 2017-2022.


Document type: Conference Paper
Collection: Conference Papers

Title A bayesian nonparametric framework for activity recognition using accelerometer data
Author(s) Nguyen, T
Gupta, S
Venkatesh, S
Phung, D
Year 2014
Conference name ICPR 2014
Conference location Stockholm, Sweden
Conference dates 24-28 August 2014
Proceedings title Proceedings of the 22nd International Conference on Pattern Recognition (ICPR 2014)
Editor(s) Magnus Borga
Publisher IEEE
Place of publication United States
Start page 2017
End page 2022
Total pages 6
Abstract 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.
Subjects Pattern Recognition and Data Mining
DOI - identifier 10.1109/ICPR.2014.352
Copyright notice © 2014 IEEE
ISBN 9781479952106
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