Journal article 840 views
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...
| 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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<?xml version="1.0"?><rfc1807><datestamp>2018-11-26T10:35:19.1480716</datestamp><bib-version>v2</bib-version><id>46092</id><entry>2018-11-26</entry><title>7th International Society for Physical Activity and Health Congress</title><swanseaauthors><author><sid>6d62b2ed126961bed81a94a2beba8a01</sid><ORCID>0000-0001-5618-0803</ORCID><firstname>Gareth</firstname><surname>Stratton</surname><name>Gareth Stratton</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2018-11-26</date><deptcode>STSC</deptcode><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 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.</abstract><type>Journal Article</type><journal>Journal of Physical Activity and Health</journal><volume>15</volume><journalNumber>10 Suppl 1</journalNumber><paginationStart>S46</paginationStart><paginationEnd>S47</paginationEnd><publisher/><issnPrint>1543-3080</issnPrint><issnElectronic>1543-5474</issnElectronic><keywords/><publishedDay>31</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2018</publishedYear><publishedDate>2018-12-31</publishedDate><doi>10.1123/jpah.2018-0535</doi><url/><notes/><college>COLLEGE NANME</college><department>Sport and Exercise Sciences</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>STSC</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2018-11-26T10:35:19.1480716</lastEdited><Created>2018-11-26T09:44:49.5189049</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Sport and Exercise Sciences</level></path><authors><author><firstname>Gareth</firstname><surname>Stratton</surname><orcid>0000-0001-5618-0803</orcid><order>1</order></author></authors><documents/><OutputDurs/></rfc1807> |
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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 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 |
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7th International Society for Physical Activity and Health Congress |
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7th International Society for Physical Activity and Health Congress Gareth Stratton |
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7th International Society for Physical Activity and Health Congress |
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7th International Society for Physical Activity and Health Congress |
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7th International Society for Physical Activity and Health Congress |
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7th International Society for Physical Activity and Health Congress |
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Journal of Physical Activity and Health |
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10 Suppl 1 |
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S46 |
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2018 |
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1543-3080 1543-5474 |
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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. |
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2018-12-31T03:57:49Z |
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11.013148 |