Journal article 656 views
Profiling movement behaviours in pre-school children: A self-organised map approach
,
Michael J. Duncan ,
Emma L. J. Eyre ,
Gareth Stratton ,
Xavier García-Massó ,
Isaac Estevan
Journal of Sports Sciences, Volume: 38, Issue: 2, Pages: 150 - 158
Swansea University Author:
Gareth Stratton
Full text not available from this repository: check for access using links below.
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...
| Published in: | Journal of Sports Sciences |
|---|---|
| ISSN: | 0264-0414 1466-447X |
| Published: |
Informa UK Limited
2020
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa52422 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| 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 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”. |
|---|---|
| Keywords: |
Motor competence, machine learning, unsupervised, cluster analysis, physical activity |
| College: |
Faculty of Science and Engineering |
| Issue: |
2 |
| Start Page: |
150 |
| End Page: |
158 |