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Sensors
Volume 19
Issue 20
10.3390/s19204504
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Article

Towards a Portable Model to Discriminate Activity Clusters from Accelerometer Data

by
Petra Jones
1,2,*,
Evgeny M. Mirkes
3,
Tom Yates
2,4,
Charlotte L. Edwardson
2,4,
Mike Catt
5,
Melanie J. Davies
1,2,4,
Kamlesh Khunti
1,4 and
Alex V. Rowlands
2,4,6
1
Leicester Diabetes Centre, University Hospitals of Leicester, Leicester LE5 4PW, UK
2
Diabetes Research Centre, University of Leicester, Leicester General Hospital, Gwendolen Road, Leicester LE5 4PW, UK
3
Department of Mathematics, ATT 912, Attenborough Building, University of Leicester, University Road, Leicester LE5 4PW, UK
4
NIHR Leicester Biomedical Research Centre, Leicester General Hospital, Gwendolen Road, Leicester LE5 4PW, UK
5
Institute of Neuroscience, Henry Wellcome Building, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
6
Alliance for research in Exercise, Nutrition and Activity (ARENA), Sansom Institute for Health Research, Division of Health Sciences, University of South Australia, Adelaide SA 5001, Australia
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(20), 4504; https://doi.org/10.3390/s19204504
Received: 4 September 2019 / Revised: 4 October 2019 / Accepted: 15 October 2019 / Published: 17 October 2019
(This article belongs to the Section Physical Sensors)
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Abstract

Few methods for classifying physical activity from accelerometer data have been tested using an independent dataset for cross-validation, and even fewer using multiple independent datasets. The aim of this study was to evaluate whether unsupervised machine learning was a viable approach for the development of a reusable clustering model that was generalisable to independent datasets. We used two labelled adult laboratory datasets to generate a k-means clustering model. To assess its generalised application, we applied the stored clustering model to three independent labelled datasets: two laboratory and one free-living. Based on the development labelled data, the ten clusters were collapsed into four activity categories: sedentary, standing/mixed/slow ambulatory, brisk ambulatory, and running. The percentages of each activity type contained in these categories were 89%, 83%, 78%, and 96%, respectively. In the laboratory independent datasets, the consistency of activity types within the clusters dropped, but remained above 70% for the sedentary clusters, and 85% for the running and ambulatory clusters. Acceleration features were similar within each cluster across samples. The clusters created reflected activity types known to be associated with health and were reasonably robust when applied to diverse independent datasets. This suggests that an unsupervised approach is potentially useful for analysing free-living accelerometer data. View Full-Text
Keywords: unsupervised; machine learning; physical activity; clustering; wrist-worn; accelerometer; walking unsupervised; machine learning; physical activity; clustering; wrist-worn; accelerometer; walking
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MDPI and ACS Style

Jones, P.; Mirkes, E.M.; Yates, T.; Edwardson, C.L.; Catt, M.; Davies, M.J.; Khunti, K.; Rowlands, A.V. Towards a Portable Model to Discriminate Activity Clusters from Accelerometer Data. Sensors 2019, 19, 4504. https://doi.org/10.3390/s19204504

AMA Style

Jones P, Mirkes EM, Yates T, Edwardson CL, Catt M, Davies MJ, Khunti K, Rowlands AV. Towards a Portable Model to Discriminate Activity Clusters from Accelerometer Data. Sensors. 2019; 19(20):4504. https://doi.org/10.3390/s19204504

Chicago/Turabian Style

Jones, Petra, Evgeny M. Mirkes, Tom Yates, Charlotte L. Edwardson, Mike Catt, Melanie J. Davies, Kamlesh Khunti, and Alex V. Rowlands. 2019. "Towards a Portable Model to Discriminate Activity Clusters from Accelerometer Data" Sensors 19, no. 20: 4504. https://doi.org/10.3390/s19204504

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