A machine learning approach to measure and monitor physical activity in children to help fight overweight and obesity

Fergus, P., Hussain, A., Hearty, J., Fairclough, S., Boddy, L., Mackintosh, K.A., Stratton, G., Ridgers, N.D. and Radi, Naeem 2015, A machine learning approach to measure and monitor physical activity in children to help fight overweight and obesity, in ICIC 2015 : Intelligent Computing Theories, Proceedings Part II, Springer, Cham, Switzerland, pp. 676-688, doi: 10.1007/978-3-319-22186-1_67.

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Title A machine learning approach to measure and monitor physical activity in children to help fight overweight and obesity
Author(s) Fergus, P.
Hussain, A.
Hearty, J.
Fairclough, S.
Boddy, L.
Mackintosh, K.A.
Stratton, G.
Ridgers, N.D.ORCID iD for Ridgers, N.D. orcid.org/0000-0001-5713-3515
Radi, Naeem
Conference name Intelligent Compuation. International Conference (11th : 2015 : Fuzhou, China)
Conference location Fuzhou, China
Conference dates 20-23 Aug. 2015
Title of proceedings ICIC 2015 : Intelligent Computing Theories, Proceedings Part II
Editor(s) Huang, De-Shuang
Jo, Kang-Hyun
Hussain, Abir
Publication date 2015
Start page 676
End page 688
Total pages 13
Publisher Springer
Place of publication Cham, Switzerland
Keyword(s) physical activity
machine learning
neural networks
Summary Physical Activity is important for maintaining healthy lifestyles. Recommendations for physical activity levels are issued by most governments as part of public health measures. As such, reliable measurement of physical activity for regulatory purposes is vital. This has lead research to explore standards for achieving this using wearable technology and artificial neural networks that produce classifications for specific physical activity events. Applied from a very early age, the ubiquitous capture of physical activity data using mobile and wearable technology may help us to understand how we can combat childhood obesity and the impact that this has in later life. A supervised machine learning approach is adopted in this paper that utilizes data obtained from accelerometer sensors worn by children in free-living environments. The paper presents a set of activities and features suitable for measuring physical activity and evaluates the use of a Multilayer Perceptron neural network to classify physical activities by activity type. A rigorous reproducible data science methodology is presented for subsequent use in physical activity research. Our results show that it was possible to obtain an overall accuracy of 96 % with 95 % for sensitivity, 99 % for specificity and a kappa value of 94 % when three and four feature combinations were used.
ISBN 9783319221854
ISSN 0302-9743
Language eng
DOI 10.1007/978-3-319-22186-1_67
Field of Research 111199 Nutrition and Dietetics not elsewhere classified
Socio Economic Objective 920501 Child Health
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30080231

Document type: Conference Paper
Collections: Faculty of Health
School of Exercise and Nutrition Sciences
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