Tools
Tools
Fergus, P, Hussain, A, Hearty, J, Fairclough, SJ, Boddy, LM, Mackintosh, KA, Stratton, G, Ridgers, ND and Radi, N (2015) A Machine Learning Approach to Measure and Monitor Physical Activity in Children to Help Fight Overweight and Obesity. Intelligent Computing Theories and Methodologies, 9226. pp. 676-688. ISSN 0302-9743
|
Text
PA#1.pdf - Accepted Version Download (564kB) | Preview |
Abstract
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.
| Item Type: | Article |
|---|---|
| Additional Information: | The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-22186-1_67 |
| Uncontrolled Keywords: | 08 Information And Computing Sciences |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RJ Pediatrics > RJ101 Child Health. Child health services |
| Divisions: | Computer Science & Mathematics Sport & Exercise Sciences |
| Publisher: | Springer Verlag |
| Date Deposited: | 25 Nov 2015 09:07 |
| Last Modified: | 04 Sep 2021 13:46 |
| DOI or ID number: | 10.1007/978-3-319-22186-1_67 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/2388 |
 |
View Item |