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Move well, move often: Understanding motor competence in children and young people: New horizons in movement skill assessment: sensors

Gareth Stratton Orcid Logo

Journal of Physical Activity and Health, Volume: 15, Issue: S1

Swansea University Author: Gareth Stratton Orcid Logo

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
Published: 2016
URI: https://cronfa.swan.ac.uk/Record/cronfa49767
first_indexed 2019-03-27T11:22:14Z
last_indexed 2019-03-27T11:22:14Z
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spelling 2019-03-27T08:57:03.4341798 v2 49767 2019-03-27 Move well, move often: Understanding motor competence in children and young people: New horizons in movement skill assessment: sensors 6d62b2ed126961bed81a94a2beba8a01 0000-0001-5618-0803 Gareth Stratton Gareth Stratton true false 2019-03-27 EAAS 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 Other Journal of Physical Activity and Health 15 S1 31 10 2016 2016-10-31 COLLEGE NANME Engineering and Applied Sciences School COLLEGE CODE EAAS Swansea University 2019-03-27T08:57:03.4341798 2019-03-27T08:53:51.9984543 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Sport and Exercise Sciences Gareth Stratton 0000-0001-5618-0803 1
title Move well, move often: Understanding motor competence in children and young people: New horizons in movement skill assessment: sensors
spellingShingle Move well, move often: Understanding motor competence in children and young people: New horizons in movement skill assessment: sensors
Gareth Stratton
title_short Move well, move often: Understanding motor competence in children and young people: New horizons in movement skill assessment: sensors
title_full Move well, move often: Understanding motor competence in children and young people: New horizons in movement skill assessment: sensors
title_fullStr Move well, move often: Understanding motor competence in children and young people: New horizons in movement skill assessment: sensors
title_full_unstemmed Move well, move often: Understanding motor competence in children and young people: New horizons in movement skill assessment: sensors
title_sort Move well, move often: Understanding motor competence in children and young people: New horizons in movement skill assessment: sensors
author_id_str_mv 6d62b2ed126961bed81a94a2beba8a01
author_id_fullname_str_mv 6d62b2ed126961bed81a94a2beba8a01_***_Gareth Stratton
author Gareth Stratton
author2 Gareth Stratton
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container_title Journal of Physical Activity and Health
container_volume 15
container_issue S1
publishDate 2016
institution Swansea University
college_str Faculty of Science and Engineering
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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 Aerospace, Civil, Electrical, General and Mechanical Engineering - Sport and Exercise Sciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Sport and Exercise Sciences
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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 2016-10-31T01:56:37Z
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score 11.04748

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