Prediction of activity type in preschool children using machine learning techniques

Hagenbuchner, Markus, Cliff, Dylan P., Trost, Stewart G., Van Tuc, Nguyen, & Peoples, Gregory E. (2014) Prediction of activity type in preschool children using machine learning techniques. Journal of Science and Medicine in Sport, 18(4), pp. 426-431.

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Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children.


Participants completed 12 standardised activity trials (TV, reading, tablet game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits.


Eleven children aged 3–6 years (mean age = 4.8 ± 0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised into five activity classes: sedentary activities, light activities, moderate to vigorous activities, walking, and running. A standard feed-forward Artificial Neural Network and a Deep Learning Ensemble Network were trained on features in the accelerometer data used in previous investigations (10th, 25th, 50th, 75th and 90th percentiles and the lag-one autocorrelation).


Overall recognition accuracy for the standard feed forward Artificial Neural Network was 69.7%. Recognition accuracy for sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running was 82%, 79%, 64%, 36% and 46%, respectively. In comparison, overall recognition accuracy for the Deep Learning Ensemble Network was 82.6%. For sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running recognition accuracy was 84%, 91%, 79%, 73% and 73%, respectively.


Ensemble machine learning approaches such as Deep Learning Ensemble Network can accurately predict activity type from accelerometer data in preschool children.

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2 citations in Scopus
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ID Code: 78790
Item Type: Journal Article
Refereed: Yes
DOI: 10.1016/j.jsams.2014.06.003
ISSN: 1878-1861
Subjects: Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > HUMAN MOVEMENT AND SPORTS SCIENCE (110600)
Divisions: Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Exercise & Nutrition Sciences
Copyright Owner: Copyright 2014 Elsevier
Copyright Statement: This is the author’s version of a work that was accepted for publication in Journal of Science and Medicine in Sport. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Science and Medicine in Sport, Vol 18, Issue 4 DOI: 10.1016/j.jsams.2014.06.003
Deposited On: 19 Nov 2014 22:35
Last Modified: 15 Jun 2015 04:56

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