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The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review
Sports Medicine, Volume: 53, Issue: 12, Pages: 2477 - 2504
Swansea University Authors: Alex Swain, Melitta McNarry , Kelly Mackintosh
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DOI (Published version): 10.1007/s40279-023-01905-1
Abstract
Background: Movement quality is typically assessed by drawing comparisons against predetermined movement standards. Movements are often discretely scored or labelled against pre-set criteria, though movement quality can also be evaluated using motion-related measurements (e.g., spatio-temporal param...
Published in: | Sports Medicine |
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ISSN: | 0112-1642 1179-2035 |
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Springer Science and Business Media LLC
2023
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URI: | https://cronfa.swan.ac.uk/Record/cronfa64059 |
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Movements are often discretely scored or labelled against pre-set criteria, though movement quality can also be evaluated using motion-related measurements (e.g., spatio-temporal parameters and kinematic variables). Wearable technology has the potential to measure and assess movement quality and offer valuable, practical feedback. Objectives: A systematic approach was taken to examine the benefits associated with multi-sensor and multiple wearable-device usage, compared with unimodal applications, when assessing movement quality. Consequently, this review considers the additional variables and features that could be obtained through multi-sensor devices for use in movement analyses. Processing methods and applications of the various configurations were also explored. Methods: Articles were included within this review if they were written in English, specifically studied the use of wearable sensors to assess movement quality, and were published between January 2010 and December 2022. Of the 62,635 articles initially identified, 27 papers were included in this review. The quality of included studies was determined using a modified Downs and Black checklist, with 24/27 high quality. Results: Fifteen of the 27 included studies used a classification approach, 11 used a measurement approach, and one used both methods. Accelerometers featured in all 27 studies, in isolation (n = 5), with a gyroscope (n = 9), or with both a gyroscope and a magnetometer (n = 13). Sampling frequencies across all studies ranged from 50 to 200 Hz. The most common classification methods were traditional feature-based classifiers (n = 5) and support vector machines (SVM; n = 5). Sensor fusion featured in six of the 16 classification studies and nine of the 12 measurement studies, with the Madgwick algorithm most prevalent (n = 7). Conclusions: This systematic review highlights the differences between the applications and processing methods associated with the use of unimodal and multi-sensor wearable devices when assessing movement quality. Further, the use of multiple devices appears to increase the feasibility of effectively assessing holistic movements, while multi-sensor devices offer the ability to obtain more output metrics.</abstract><type>Journal Article</type><journal>Sports Medicine</journal><volume>53</volume><journalNumber>12</journalNumber><paginationStart>2477</paginationStart><paginationEnd>2504</paginationEnd><publisher>Springer Science and Business Media LLC</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0112-1642</issnPrint><issnElectronic>1179-2035</issnElectronic><keywords/><publishedDay>1</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-12-01</publishedDate><doi>10.1007/s40279-023-01905-1</doi><url/><notes/><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><apcterm>SU Library paid the OA fee (TA Institutional Deal)</apcterm><funders>Swansea University. TAS has received financial support from a project sponsor, Polar Electro Oy. However, no specific sources of funding were used to assist in the preparation of this article.</funders><projectreference/><lastEdited>2024-08-20T15:51:29.6128090</lastEdited><Created>2023-08-09T08:56:08.5615014</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Sport and Exercise Sciences</level></path><authors><author><firstname>Alex</firstname><surname>Swain</surname><order>1</order></author><author><firstname>Melitta</firstname><surname>McNarry</surname><orcid>0000-0003-0813-7477</orcid><order>2</order></author><author><firstname>Adam W. H.</firstname><surname>Runacres</surname><orcid>0000-0002-8251-2805</orcid><order>3</order></author><author><firstname>Kelly</firstname><surname>Mackintosh</surname><orcid>0000-0003-0355-6357</orcid><order>4</order></author></authors><documents><document><filename>64059__28547__a58e22b5003546539f17fad7d3fad8a6.pdf</filename><originalFilename>64059.VOR.pdf</originalFilename><uploaded>2023-09-15T15:10:32.7525204</uploaded><type>Output</type><contentLength>1192373</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>© The Author(s) 2023. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0).</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807> |
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v2 64059 2023-08-09 The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review e58af411e7a9cdf4197ff81cad1eb321 Alex Swain Alex Swain true false 062f5697ff59f004bc8c713955988398 0000-0003-0813-7477 Melitta McNarry Melitta McNarry true false bdb20e3f31bcccf95c7bc116070c4214 0000-0003-0355-6357 Kelly Mackintosh Kelly Mackintosh true false 2023-08-09 Background: Movement quality is typically assessed by drawing comparisons against predetermined movement standards. Movements are often discretely scored or labelled against pre-set criteria, though movement quality can also be evaluated using motion-related measurements (e.g., spatio-temporal parameters and kinematic variables). Wearable technology has the potential to measure and assess movement quality and offer valuable, practical feedback. Objectives: A systematic approach was taken to examine the benefits associated with multi-sensor and multiple wearable-device usage, compared with unimodal applications, when assessing movement quality. Consequently, this review considers the additional variables and features that could be obtained through multi-sensor devices for use in movement analyses. Processing methods and applications of the various configurations were also explored. Methods: Articles were included within this review if they were written in English, specifically studied the use of wearable sensors to assess movement quality, and were published between January 2010 and December 2022. Of the 62,635 articles initially identified, 27 papers were included in this review. The quality of included studies was determined using a modified Downs and Black checklist, with 24/27 high quality. Results: Fifteen of the 27 included studies used a classification approach, 11 used a measurement approach, and one used both methods. Accelerometers featured in all 27 studies, in isolation (n = 5), with a gyroscope (n = 9), or with both a gyroscope and a magnetometer (n = 13). Sampling frequencies across all studies ranged from 50 to 200 Hz. The most common classification methods were traditional feature-based classifiers (n = 5) and support vector machines (SVM; n = 5). Sensor fusion featured in six of the 16 classification studies and nine of the 12 measurement studies, with the Madgwick algorithm most prevalent (n = 7). Conclusions: This systematic review highlights the differences between the applications and processing methods associated with the use of unimodal and multi-sensor wearable devices when assessing movement quality. Further, the use of multiple devices appears to increase the feasibility of effectively assessing holistic movements, while multi-sensor devices offer the ability to obtain more output metrics. Journal Article Sports Medicine 53 12 2477 2504 Springer Science and Business Media LLC 0112-1642 1179-2035 1 12 2023 2023-12-01 10.1007/s40279-023-01905-1 COLLEGE NANME COLLEGE CODE Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University. TAS has received financial support from a project sponsor, Polar Electro Oy. However, no specific sources of funding were used to assist in the preparation of this article. 2024-08-20T15:51:29.6128090 2023-08-09T08:56:08.5615014 Faculty of Science and Engineering School of Engineering and Applied Sciences - Sport and Exercise Sciences Alex Swain 1 Melitta McNarry 0000-0003-0813-7477 2 Adam W. H. Runacres 0000-0002-8251-2805 3 Kelly Mackintosh 0000-0003-0355-6357 4 64059__28547__a58e22b5003546539f17fad7d3fad8a6.pdf 64059.VOR.pdf 2023-09-15T15:10:32.7525204 Output 1192373 application/pdf Version of Record true © The Author(s) 2023. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/ |
title |
The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review |
spellingShingle |
The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review Alex Swain Melitta McNarry Kelly Mackintosh |
title_short |
The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review |
title_full |
The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review |
title_fullStr |
The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review |
title_full_unstemmed |
The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review |
title_sort |
The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review |
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e58af411e7a9cdf4197ff81cad1eb321_***_Alex Swain 062f5697ff59f004bc8c713955988398_***_Melitta McNarry bdb20e3f31bcccf95c7bc116070c4214_***_Kelly Mackintosh |
author |
Alex Swain Melitta McNarry Kelly Mackintosh |
author2 |
Alex Swain Melitta McNarry Adam W. H. Runacres Kelly Mackintosh |
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Sports Medicine |
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53 |
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10.1007/s40279-023-01905-1 |
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description |
Background: Movement quality is typically assessed by drawing comparisons against predetermined movement standards. Movements are often discretely scored or labelled against pre-set criteria, though movement quality can also be evaluated using motion-related measurements (e.g., spatio-temporal parameters and kinematic variables). Wearable technology has the potential to measure and assess movement quality and offer valuable, practical feedback. Objectives: A systematic approach was taken to examine the benefits associated with multi-sensor and multiple wearable-device usage, compared with unimodal applications, when assessing movement quality. Consequently, this review considers the additional variables and features that could be obtained through multi-sensor devices for use in movement analyses. Processing methods and applications of the various configurations were also explored. Methods: Articles were included within this review if they were written in English, specifically studied the use of wearable sensors to assess movement quality, and were published between January 2010 and December 2022. Of the 62,635 articles initially identified, 27 papers were included in this review. The quality of included studies was determined using a modified Downs and Black checklist, with 24/27 high quality. Results: Fifteen of the 27 included studies used a classification approach, 11 used a measurement approach, and one used both methods. Accelerometers featured in all 27 studies, in isolation (n = 5), with a gyroscope (n = 9), or with both a gyroscope and a magnetometer (n = 13). Sampling frequencies across all studies ranged from 50 to 200 Hz. The most common classification methods were traditional feature-based classifiers (n = 5) and support vector machines (SVM; n = 5). Sensor fusion featured in six of the 16 classification studies and nine of the 12 measurement studies, with the Madgwick algorithm most prevalent (n = 7). Conclusions: This systematic review highlights the differences between the applications and processing methods associated with the use of unimodal and multi-sensor wearable devices when assessing movement quality. Further, the use of multiple devices appears to increase the feasibility of effectively assessing holistic movements, while multi-sensor devices offer the ability to obtain more output metrics. |
published_date |
2023-12-01T15:51:28Z |
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11.035349 |