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Nonparametric discovery of movement patterns from accelerometer signals

Nguyen, Thuong, Gupta, Sunil, Venkatesh, Svetha and Phung, Dinh 2016, Nonparametric discovery of movement patterns from accelerometer signals, Pattern recognition letters, vol. 70, pp. 52-58, doi: 10.1016/j.patrec.2015.11.003.

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Title Nonparametric discovery of movement patterns from accelerometer signals
Author(s) Nguyen, Thuong
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Journal name Pattern recognition letters
Volume number 70
Start page 52
End page 58
Total pages 7
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-01-15
ISSN 0167-8655
1872-7344
Keyword(s) movement intensity
activity recognition
accelerometer
Bayesian nonparametric
Dirichlet process
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
TRIAXIAL ACCELEROMETER
PHYSICAL-ACTIVITY
SENSORS
Summary Monitoring daily physical activity plays an important role in disease prevention and intervention. This paper proposes an approach to monitor the body movement intensity levels from accelerometer data. We collect the data using the accelerometer 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 their mobile phones. We compute a novel feature that has a strong correlation with the movement intensity. We use the hierarchical Dirichlet process (HDP) model to detect the activity levels from this feature. Consisting of Bayesian nonparametric priors over the parameters the model can infer the number of levels automatically. By demonstrating the approach on the publicly available USC-HAD dataset that includes ground-truth activity labels, we show a strong correlation between the discovered activity levels and the movement intensity of the activities. This correlation is further confirmed using our newly collected dataset. We further use the extracted patterns as features for clustering and classifying the activity sequences to improve performance.
Language eng
DOI 10.1016/j.patrec.2015.11.003
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082823

Document type: Journal Article
Collection: Centre for Pattern Recognition and Data Analytics
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Created: Wed, 13 Apr 2016, 16:29:22 EST

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