The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review
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 parame...
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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. |
doi_str_mv | 10.1007/s40279-023-01905-1 |
format | Article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10687099</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2899164249</sourcerecordid><originalsourceid>FETCH-LOGICAL-c452t-6d1a466f90e18c2e1da8ea55ffd8dfca1874e5a18d0f4e6648ee1eaef9370fe23</originalsourceid><addsrcrecordid>eNp9kU9v1DAQxS0EokvhC3CKxIVLYMaxHZsLWlXlj9QK0ZazZZJx6yqJi50s2m-Pt6lAcOA00szvPc3MY-wlwhsEaN9mAbw1NfCmBjQga3zENoilxaGRj9kGEHmNSvAj9iznWwCQWvCn7KhpldGtUhtmr26ouogDVdFX58swh_qSphxTdU4uL4lGmuYqTNVcuG3OlPN950DH3Tr9urghzPt31ba63OeZRjeHrrqgXaCfz9kT74ZMLx7qMfv24fTq5FN99uXj55PtWd0Jyeda9eiEUt4Aoe44Ye80OSm973XvO4e6FSRL6cELUkpoIiRH3jQteOLNMXu_-t4t30fqu7JXcoO9S2F0aW-jC_bvyRRu7HXcWQSlWzCmOLx-cEjxx0J5tmPIHQ2Dmygu2XKtBEpluCzoq3_Q27ikqdxXKGMOHxcHQ75SXYo5J_K_t0GwhwDtGqAtAdr7AC0WUbOKcoGna0p_rP-j-gU2C53q</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2899164249</pqid></control><display><type>article</type><title>The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review</title><source>Springer Nature - Complete Springer Journals</source><creator>Swain, T. Alexander ; McNarry, Melitta A. ; Runacres, Adam W. H. ; Mackintosh, Kelly A.</creator><creatorcontrib>Swain, T. Alexander ; McNarry, Melitta A. ; Runacres, Adam W. H. ; Mackintosh, Kelly A.</creatorcontrib><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.</description><identifier>ISSN: 0112-1642</identifier><identifier>EISSN: 1179-2035</identifier><identifier>DOI: 10.1007/s40279-023-01905-1</identifier><identifier>PMID: 37698766</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Classification ; Exercise ; Feedback ; Kinematics ; Medicine ; Medicine & Public Health ; Physical fitness ; Reviews ; Sensors ; Sports Medicine ; Systematic Review ; Wearable computers</subject><ispartof>Sports medicine (Auckland), 2023-12, Vol.53 (12), p.2477-2504</ispartof><rights>The Author(s) 2023</rights><rights>Copyright Springer Nature B.V. Dec 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c452t-6d1a466f90e18c2e1da8ea55ffd8dfca1874e5a18d0f4e6648ee1eaef9370fe23</citedby><cites>FETCH-LOGICAL-c452t-6d1a466f90e18c2e1da8ea55ffd8dfca1874e5a18d0f4e6648ee1eaef9370fe23</cites><orcidid>0000-0002-8251-2805 ; 0000-0003-3142-2399 ; 0000-0003-0813-7477 ; 0000-0003-0355-6357</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s40279-023-01905-1$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s40279-023-01905-1$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,27903,27904,41467,42536,51297</link.rule.ids></links><search><creatorcontrib>Swain, T. Alexander</creatorcontrib><creatorcontrib>McNarry, Melitta A.</creatorcontrib><creatorcontrib>Runacres, Adam W. H.</creatorcontrib><creatorcontrib>Mackintosh, Kelly A.</creatorcontrib><title>The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review</title><title>Sports medicine (Auckland)</title><addtitle>Sports Med</addtitle><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.</description><subject>Classification</subject><subject>Exercise</subject><subject>Feedback</subject><subject>Kinematics</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Physical fitness</subject><subject>Reviews</subject><subject>Sensors</subject><subject>Sports Medicine</subject><subject>Systematic Review</subject><subject>Wearable computers</subject><issn>0112-1642</issn><issn>1179-2035</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><recordid>eNp9kU9v1DAQxS0EokvhC3CKxIVLYMaxHZsLWlXlj9QK0ZazZZJx6yqJi50s2m-Pt6lAcOA00szvPc3MY-wlwhsEaN9mAbw1NfCmBjQga3zENoilxaGRj9kGEHmNSvAj9iznWwCQWvCn7KhpldGtUhtmr26ouogDVdFX58swh_qSphxTdU4uL4lGmuYqTNVcuG3OlPN950DH3Tr9urghzPt31ba63OeZRjeHrrqgXaCfz9kT74ZMLx7qMfv24fTq5FN99uXj55PtWd0Jyeda9eiEUt4Aoe44Ye80OSm973XvO4e6FSRL6cELUkpoIiRH3jQteOLNMXu_-t4t30fqu7JXcoO9S2F0aW-jC_bvyRRu7HXcWQSlWzCmOLx-cEjxx0J5tmPIHQ2Dmygu2XKtBEpluCzoq3_Q27ikqdxXKGMOHxcHQ75SXYo5J_K_t0GwhwDtGqAtAdr7AC0WUbOKcoGna0p_rP-j-gU2C53q</recordid><startdate>20231201</startdate><enddate>20231201</enddate><creator>Swain, T. Alexander</creator><creator>McNarry, Melitta A.</creator><creator>Runacres, Adam W. H.</creator><creator>Mackintosh, Kelly A.</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>4T-</scope><scope>7QP</scope><scope>7RV</scope><scope>7TS</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>H94</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-8251-2805</orcidid><orcidid>https://orcid.org/0000-0003-3142-2399</orcidid><orcidid>https://orcid.org/0000-0003-0813-7477</orcidid><orcidid>https://orcid.org/0000-0003-0355-6357</orcidid></search><sort><creationdate>20231201</creationdate><title>The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review</title><author>Swain, T. Alexander ; McNarry, Melitta A. ; Runacres, Adam W. H. ; Mackintosh, Kelly A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c452t-6d1a466f90e18c2e1da8ea55ffd8dfca1874e5a18d0f4e6648ee1eaef9370fe23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Classification</topic><topic>Exercise</topic><topic>Feedback</topic><topic>Kinematics</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Physical fitness</topic><topic>Reviews</topic><topic>Sensors</topic><topic>Sports Medicine</topic><topic>Systematic Review</topic><topic>Wearable computers</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Swain, T. Alexander</creatorcontrib><creatorcontrib>McNarry, Melitta A.</creatorcontrib><creatorcontrib>Runacres, Adam W. H.</creatorcontrib><creatorcontrib>Mackintosh, Kelly A.</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Docstoc</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Physical Education Index</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Sports medicine (Auckland)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Swain, T. Alexander</au><au>McNarry, Melitta A.</au><au>Runacres, Adam W. H.</au><au>Mackintosh, Kelly A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review</atitle><jtitle>Sports medicine (Auckland)</jtitle><stitle>Sports Med</stitle><date>2023-12-01</date><risdate>2023</risdate><volume>53</volume><issue>12</issue><spage>2477</spage><epage>2504</epage><pages>2477-2504</pages><issn>0112-1642</issn><eissn>1179-2035</eissn><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 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><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>37698766</pmid><doi>10.1007/s40279-023-01905-1</doi><tpages>28</tpages><orcidid>https://orcid.org/0000-0002-8251-2805</orcidid><orcidid>https://orcid.org/0000-0003-3142-2399</orcidid><orcidid>https://orcid.org/0000-0003-0813-7477</orcidid><orcidid>https://orcid.org/0000-0003-0355-6357</orcidid><oa>free_for_read</oa></addata></record> |
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source | Springer Nature - Complete Springer Journals |
subjects | Classification Exercise Feedback Kinematics Medicine Medicine & Public Health Physical fitness Reviews Sensors Sports Medicine Systematic Review Wearable computers |
title | The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review |
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