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Profiling movement behaviours in pre-school children: A self-organised map approach

Cain C. T. Clark Orcid Logo, Michael J. Duncan Orcid Logo, Emma L. J. Eyre Orcid Logo, Gareth Stratton Orcid Logo, Xavier García-Massó Orcid Logo, Isaac Estevan Orcid Logo

Journal of Sports Sciences, Volume: 38, Issue: 2, Pages: 150 - 158

Swansea University Author: Gareth Stratton Orcid Logo

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DOI (Published version): 10.1080/02640414.2019.1686942

Abstract

Application of machine learning techniques has the potential to yield unseen insights into movement and permits visualisation of complex behaviours and tangible profiles. The aim of this study was to identify profiles of relative motor competence (MC) and movement behaviours in pre-school children u...

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Published in: Journal of Sports Sciences
ISSN: 0264-0414 1466-447X
Published: Informa UK Limited 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa52422
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first_indexed 2019-10-15T03:09:15Z
last_indexed 2023-03-15T04:06:14Z
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spelling 2023-03-14T11:17:11.1717294 v2 52422 2019-10-14 Profiling movement behaviours in pre-school children: A self-organised map approach 6d62b2ed126961bed81a94a2beba8a01 0000-0001-5618-0803 Gareth Stratton Gareth Stratton true false 2019-10-14 STSC Application of machine learning techniques has the potential to yield unseen insights into movement and permits visualisation of complex behaviours and tangible profiles. The aim of this study was to identify profiles of relative motor competence (MC) and movement behaviours in pre-school children using novel analytics. One-hundred and twenty-five children (4.3 ± 0.5y, 1.04 ± 0.05 m, 17.8 ± 3.2 kg, BMI: 16.2 ± 1.9 kg.m2) took part in this study. Measures included accelerometer-derived 24-h activity, MC (Movement Assessment Battery for Children second edition), height, weight and waist circumference, from which zBMI were derived. Self-Organised Map (SOM) analysis was used to classify participants’ profiles and a k-means cluster analysis was used to classify the neurons into larger groups according to the input variables. These clusters were used to describe the individuals’ characteristics according to their MC and PA compositions. The SOM analysis indicated five profiles according to MC and PA. One cluster was identified as having both the lowest MC and MVPA (profile 2), whilst profiles 4 and 5 show moderate-high values of PA and MC. We present a novel pathway to profiling complex tenets of human movement and behaviour, which has never previously been implemented in pre-school children, highlighting that the focus should change from obesity monitoring, to “moving well”. Journal Article Journal of Sports Sciences 38 2 150 158 Informa UK Limited 0264-0414 1466-447X Motor competence, machine learning, unsupervised, cluster analysis, physical activity 17 1 2020 2020-01-17 10.1080/02640414.2019.1686942 http://dx.doi.org/10.1080/02640414.2019.1686942 COLLEGE NANME Sport and Exercise Sciences COLLEGE CODE STSC Swansea University 2023-03-14T11:17:11.1717294 2019-10-14T10:47:41.8623593 Faculty of Science and Engineering School of Engineering and Applied Sciences - Sport and Exercise Sciences Cain C. T. Clark 0000-0002-6610-4617 1 Michael J. Duncan 0000-0002-2016-6580 2 Emma L. J. Eyre 0000-0002-4040-5921 3 Gareth Stratton 0000-0001-5618-0803 4 Xavier García-Massó 0000-0002-5925-4537 5 Isaac Estevan 0000-0003-3748-2288 6
title Profiling movement behaviours in pre-school children: A self-organised map approach
spellingShingle Profiling movement behaviours in pre-school children: A self-organised map approach
Gareth Stratton
title_short Profiling movement behaviours in pre-school children: A self-organised map approach
title_full Profiling movement behaviours in pre-school children: A self-organised map approach
title_fullStr Profiling movement behaviours in pre-school children: A self-organised map approach
title_full_unstemmed Profiling movement behaviours in pre-school children: A self-organised map approach
title_sort Profiling movement behaviours in pre-school children: A self-organised map approach
author_id_str_mv 6d62b2ed126961bed81a94a2beba8a01
author_id_fullname_str_mv 6d62b2ed126961bed81a94a2beba8a01_***_Gareth Stratton
author Gareth Stratton
author2 Cain C. T. Clark
Michael J. Duncan
Emma L. J. Eyre
Gareth Stratton
Xavier García-Massó
Isaac Estevan
format Journal article
container_title Journal of Sports Sciences
container_volume 38
container_issue 2
container_start_page 150
publishDate 2020
institution Swansea University
issn 0264-0414
1466-447X
doi_str_mv 10.1080/02640414.2019.1686942
publisher Informa UK Limited
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Sport and Exercise Sciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Sport and Exercise Sciences
url http://dx.doi.org/10.1080/02640414.2019.1686942
document_store_str 0
active_str 0
description Application of machine learning techniques has the potential to yield unseen insights into movement and permits visualisation of complex behaviours and tangible profiles. The aim of this study was to identify profiles of relative motor competence (MC) and movement behaviours in pre-school children using novel analytics. One-hundred and twenty-five children (4.3 ± 0.5y, 1.04 ± 0.05 m, 17.8 ± 3.2 kg, BMI: 16.2 ± 1.9 kg.m2) took part in this study. Measures included accelerometer-derived 24-h activity, MC (Movement Assessment Battery for Children second edition), height, weight and waist circumference, from which zBMI were derived. Self-Organised Map (SOM) analysis was used to classify participants’ profiles and a k-means cluster analysis was used to classify the neurons into larger groups according to the input variables. These clusters were used to describe the individuals’ characteristics according to their MC and PA compositions. The SOM analysis indicated five profiles according to MC and PA. One cluster was identified as having both the lowest MC and MVPA (profile 2), whilst profiles 4 and 5 show moderate-high values of PA and MC. We present a novel pathway to profiling complex tenets of human movement and behaviour, which has never previously been implemented in pre-school children, highlighting that the focus should change from obesity monitoring, to “moving well”.
published_date 2020-01-17T04:04:46Z
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score 11.037603

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