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7th International Society for Physical Activity and Health Congress / Gareth Stratton

Journal of Physical Activity and Health, Volume: 15, Issue: 10 Suppl 1, Pages: S46 - S47

Swansea University Author: Gareth Stratton

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DOI (Published version): 10.1123/jpah.2018-0535

Abstract

Introduction: Accelerometers are widely used to study physical activity and have been shown to be informative of motion mechanics. Whilst Process-oriented assessment is an important tool in the development of children’s fundamental movement skills, current methods of assessment are cumbersome and su...

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Published in: Journal of Physical Activity and Health
ISSN: 1543-3080 1543-5474
Published: 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa46092
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first_indexed 2018-11-26T14:23:16Z
last_indexed 2018-11-26T14:23:16Z
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spelling 2018-11-26T10:35:19.1480716 v2 46092 2018-11-26 7th International Society for Physical Activity and Health Congress 6d62b2ed126961bed81a94a2beba8a01 0000-0001-5618-0803 Gareth Stratton Gareth Stratton true false 2018-11-26 STSC Introduction: Accelerometers are widely used to study physical activity and have been shown to be informative of motion mechanics. Whilst Process-oriented assessment is an important tool in the development of children’s fundamental movement skills, current methods of assessment are cumbersome and subjective. We present a novel analysis framework for activity assessment that uses accelerometry to create sophisticated motion maps and demonstrate their utility in profiling and categorising movement mechanics, objectively, in a diverse range of fundamental movements. Methods: Acceleration data were collected from ankle and wrist mounted sensors. Children aged 9 - 12 years were assessed in a multi-stage fitness test and a fundamental movement skill (FMS) challenge. Acceleration and magnetometer data were used to construct spectrograms, phase maps of motion and a performance sphere. Specific activity components were analysed through pattern recognition using machine learning, and dynamic time-warping. Results: Novel analyses of FMS displayed patterns clearly linked to specific activities such as throwing, jumping and body roll. These were sufficient to classify performance into activity categories using pattern recognition and a training set from expert observer scores. Discussion: Novel analyses of children’s mechanical motion patterns can be achieved for FMS using lightweight, low cost wearable sensors. These motion maps can be predictive of performance based on limited sampling allowing population profiling of FMS. Further, the use of computerised pattern recognition and classification gives an objective scoring of complex motion, normally requiring subjective assessment by expert human observers. Journal Article Journal of Physical Activity and Health 15 10 Suppl 1 S46 S47 1543-3080 1543-5474 31 12 2018 2018-12-31 10.1123/jpah.2018-0535 COLLEGE NANME Sport and Exercise Sciences COLLEGE CODE STSC Swansea University 2018-11-26T10:35:19.1480716 2018-11-26T09:44:49.5189049 College of Engineering Sports Science Gareth Stratton 0000-0001-5618-0803 1
title 7th International Society for Physical Activity and Health Congress
spellingShingle 7th International Society for Physical Activity and Health Congress
Gareth, Stratton
title_short 7th International Society for Physical Activity and Health Congress
title_full 7th International Society for Physical Activity and Health Congress
title_fullStr 7th International Society for Physical Activity and Health Congress
title_full_unstemmed 7th International Society for Physical Activity and Health Congress
title_sort 7th International Society for Physical Activity and Health Congress
author_id_str_mv 6d62b2ed126961bed81a94a2beba8a01
author_id_fullname_str_mv 6d62b2ed126961bed81a94a2beba8a01_***_Gareth, Stratton
author Gareth, Stratton
author2 Gareth Stratton
format Journal article
container_title Journal of Physical Activity and Health
container_volume 15
container_issue 10 Suppl 1
container_start_page S46
publishDate 2018
institution Swansea University
issn 1543-3080
1543-5474
doi_str_mv 10.1123/jpah.2018-0535
college_str College of Engineering
hierarchytype
hierarchy_top_id collegeofengineering
hierarchy_top_title College of Engineering
hierarchy_parent_id collegeofengineering
hierarchy_parent_title College of Engineering
department_str Sports Science{{{_:::_}}}College of Engineering{{{_:::_}}}Sports Science
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description Introduction: Accelerometers are widely used to study physical activity and have been shown to be informative of motion mechanics. Whilst Process-oriented assessment is an important tool in the development of children’s fundamental movement skills, current methods of assessment are cumbersome and subjective. We present a novel analysis framework for activity assessment that uses accelerometry to create sophisticated motion maps and demonstrate their utility in profiling and categorising movement mechanics, objectively, in a diverse range of fundamental movements. Methods: Acceleration data were collected from ankle and wrist mounted sensors. Children aged 9 - 12 years were assessed in a multi-stage fitness test and a fundamental movement skill (FMS) challenge. Acceleration and magnetometer data were used to construct spectrograms, phase maps of motion and a performance sphere. Specific activity components were analysed through pattern recognition using machine learning, and dynamic time-warping. Results: Novel analyses of FMS displayed patterns clearly linked to specific activities such as throwing, jumping and body roll. These were sufficient to classify performance into activity categories using pattern recognition and a training set from expert observer scores. Discussion: Novel analyses of children’s mechanical motion patterns can be achieved for FMS using lightweight, low cost wearable sensors. These motion maps can be predictive of performance based on limited sampling allowing population profiling of FMS. Further, the use of computerised pattern recognition and classification gives an objective scoring of complex motion, normally requiring subjective assessment by expert human observers.
published_date 2018-12-31T04:07:51Z
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