You are not logged in.

A machine learning approach to measure and monitor physical activity in children

Fergus, Paul, Hussain, Abir J., Hearty, John, Fairclough, Stuart, Boddy, Lynne, Mackintosh, Kelly, Stratton, Gareth, Ridgers, Nicky, Al-Jumeily, Dhiya, Aljaaf, Ahmed J. and Lunn, Janet 2017, A machine learning approach to measure and monitor physical activity in children, Neurocomputing, vol. 228, pp. 220-230, doi: 10.1016/j.neucom.2016.10.040.

Attached Files
Name Description MIMEType Size Downloads

Title A machine learning approach to measure and monitor physical activity in children
Author(s) Fergus, Paul
Hussain, Abir J.
Hearty, John
Fairclough, Stuart
Boddy, Lynne
Mackintosh, Kelly
Stratton, Gareth
Ridgers, NickyORCID iD for Ridgers, Nicky orcid.org/0000-0001-5713-3515
Al-Jumeily, Dhiya
Aljaaf, Ahmed J.
Lunn, Janet
Journal name Neurocomputing
Volume number 228
Start page 220
End page 230
Total pages 11
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2017-03-08
ISSN 0925-2312
1872-8286
Keyword(s) physical activity
overweight
obesity
machine learning
classification
neural networks
sensors
Summary The growing trend of obesity and overweight worldwide has reached epidemic proportions with one third of the global population now considered obese. This is having a significant medical impact on children and adults who are at risk of developing osteoarthritis, coronary heart disease and stroke, type 2 diabetes, cancers, respiratory problems, and non-alcoholic fatty liver disease. In an attempt to redress the issue, physical activity is being promoted as a fundamental component for maintaining a healthy lifestyle. Recommendations for physical activity levels are issued by most governments as part of their public health measures. However, current techniques and protocols, including those used in laboratory settings, have been criticised. The main concern is that it is not feasible to use multiple pieces of measurement hardware, such as VO2 masks and heart rate monitors, to monitor children in free-living environments due to weight and encumbrance constraints. This has prompted research in the use of wearable sensing and machine learning technology to produce classifications for specific physical activity events. This paper builds on this approach and presents a supervised machine learning method that utilises data obtained from accelerometer sensors worn by children in free-living environments. Our results show that when using an artificial neural network algorithm it is possible to obtain an overall accuracy of 96% using four features from the initial dataset, with sensitivity and specificity values equal to 95% and 99% respectively. Expanding the dataset with interpolated cases, it was possible to improve on these results with 98.8% for accuracy, and 99% for sensitivity and specificity when four features were used.
Language eng
DOI 10.1016/j.neucom.2016.10.040
Field of Research 110602 Exercise Physiology
Socio Economic Objective 0 Not Applicable
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:30091862

Document type: Journal Article
Collection: School of Exercise and Nutrition Sciences
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 31 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Fri, 10 Mar 2017, 13:41:19 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.