No Cover Image

Journal article 1447 views 47 downloads

Assessing real-world movements using consumer-grade wearable devices: Measuring segment orientations and movement quality

Alex Swain, Melitta McNarry Orcid Logo, Samuel Manzano-Carrasco, Kelly Mackintosh Orcid Logo

Wearable Technologies, Volume: 6

Swansea University Authors: Alex Swain, Melitta McNarry Orcid Logo, Kelly Mackintosh Orcid Logo

  • 70731.VoR.pdf

    PDF | Version of Record

    © The Author(s), 2025. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence.

    Download (943.95KB)

Check full text

DOI (Published version): 10.1017/wtc.2025.10034

Abstract

In recent years, there has been growing interest regarding the impact of human movement quality on health. However, assessing movement quality outside of laboratories or clinics remains challenging. This study aimed to evaluate the capabilities of consumer-grade wearables to assess movement quality...

Full description

Published in: Wearable Technologies
ISSN: 2631-7176
Published: Cambridge University Press (CUP) 2025
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa70731
first_indexed 2025-10-20T11:32:53Z
last_indexed 2025-11-22T05:31:54Z
id cronfa70731
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2025-11-21T15:30:38.2537230</datestamp><bib-version>v2</bib-version><id>70731</id><entry>2025-10-20</entry><title>Assessing real-world movements using consumer-grade wearable devices: Measuring segment orientations and movement quality</title><swanseaauthors><author><sid>e58af411e7a9cdf4197ff81cad1eb321</sid><firstname>Alex</firstname><surname>Swain</surname><name>Alex Swain</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>062f5697ff59f004bc8c713955988398</sid><ORCID>0000-0003-0813-7477</ORCID><firstname>Melitta</firstname><surname>McNarry</surname><name>Melitta McNarry</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>bdb20e3f31bcccf95c7bc116070c4214</sid><ORCID>0000-0003-0355-6357</ORCID><firstname>Kelly</firstname><surname>Mackintosh</surname><name>Kelly Mackintosh</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2025-10-20</date><deptcode>MEDS</deptcode><abstract>In recent years, there has been growing interest regarding the impact of human movement quality on health. However, assessing movement quality outside of laboratories or clinics remains challenging. This study aimed to evaluate the capabilities of consumer-grade wearables to assess movement quality and to consider optimal sensor locations. Twenty-two participants wore Polar Verity Sense magnetic, angular rate, and gravity (MARG) sensors on their chest and both wrists, thighs, and ankles, while performing repeated bodyweight movements. The Madgwick sensor-fusion algorithm was utilized to obtain three-dimensional orientations. Concurrent validity, quantified using the root-mean-square-error (RMSE), was established against a Vicon optical motion capture system following time-synchronization and coordinate-system alignment. The chest sensors demonstrated the highest accuracies overall, with mean RMSE ( RMSEmean ) less than 9.0&#xB0; across all movements. In contrast, the wrist sensors varied considerably ( 5.5&#x2218;&#x2264;RMSEmean&#x2264;139.1&#x2218; ). Ankle and thigh sensors yielded mixed results, with the RMSEmean ranging from 2.0&#xB0; to 40.0&#xB0;. Notably, yaw angles consistently demonstrated higher discrepancies overall, while pitch and roll were relatively more stable. This study highlights the potential of consumer-grade MARG sensors to increase the real-world applicability and accessibility of complex biomechanical models. It also accentuates the requirement for strategic sensor placement and refined calibration and postprocessing methods to ensure accuracy.</abstract><type>Journal Article</type><journal>Wearable Technologies</journal><volume>6</volume><journalNumber/><paginationStart/><paginationEnd/><publisher>Cambridge University Press (CUP)</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2631-7176</issnElectronic><keywords>wearable technology; IMU; motion capture; motor skills; exercise; physical activity</keywords><publishedDay>19</publishedDay><publishedMonth>11</publishedMonth><publishedYear>2025</publishedYear><publishedDate>2025-11-19</publishedDate><doi>10.1017/wtc.2025.10034</doi><url/><notes/><college>COLLEGE NANME</college><department>Medical School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MEDS</DepartmentCode><institution>Swansea University</institution><apcterm>SU Library paid the OA fee (TA Institutional Deal)</apcterm><funders>Polar Electro Oy</funders><projectreference/><lastEdited>2025-11-21T15:30:38.2537230</lastEdited><Created>2025-10-20T12:28:56.0568731</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>Samuel</firstname><surname>Manzano-Carrasco</surname><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>70731__35685__57306780aa2747649b53dcf0a221925e.pdf</filename><originalFilename>70731.VoR.pdf</originalFilename><uploaded>2025-11-21T15:27:26.4285871</uploaded><type>Output</type><contentLength>966609</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; The Author(s), 2025. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0</licence></document></documents><OutputDurs/></rfc1807>
spelling 2025-11-21T15:30:38.2537230 v2 70731 2025-10-20 Assessing real-world movements using consumer-grade wearable devices: Measuring segment orientations and movement quality 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 2025-10-20 MEDS In recent years, there has been growing interest regarding the impact of human movement quality on health. However, assessing movement quality outside of laboratories or clinics remains challenging. This study aimed to evaluate the capabilities of consumer-grade wearables to assess movement quality and to consider optimal sensor locations. Twenty-two participants wore Polar Verity Sense magnetic, angular rate, and gravity (MARG) sensors on their chest and both wrists, thighs, and ankles, while performing repeated bodyweight movements. The Madgwick sensor-fusion algorithm was utilized to obtain three-dimensional orientations. Concurrent validity, quantified using the root-mean-square-error (RMSE), was established against a Vicon optical motion capture system following time-synchronization and coordinate-system alignment. The chest sensors demonstrated the highest accuracies overall, with mean RMSE ( RMSEmean ) less than 9.0° across all movements. In contrast, the wrist sensors varied considerably ( 5.5∘≤RMSEmean≤139.1∘ ). Ankle and thigh sensors yielded mixed results, with the RMSEmean ranging from 2.0° to 40.0°. Notably, yaw angles consistently demonstrated higher discrepancies overall, while pitch and roll were relatively more stable. This study highlights the potential of consumer-grade MARG sensors to increase the real-world applicability and accessibility of complex biomechanical models. It also accentuates the requirement for strategic sensor placement and refined calibration and postprocessing methods to ensure accuracy. Journal Article Wearable Technologies 6 Cambridge University Press (CUP) 2631-7176 wearable technology; IMU; motion capture; motor skills; exercise; physical activity 19 11 2025 2025-11-19 10.1017/wtc.2025.10034 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University SU Library paid the OA fee (TA Institutional Deal) Polar Electro Oy 2025-11-21T15:30:38.2537230 2025-10-20T12:28:56.0568731 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 Samuel Manzano-Carrasco 3 Kelly Mackintosh 0000-0003-0355-6357 4 70731__35685__57306780aa2747649b53dcf0a221925e.pdf 70731.VoR.pdf 2025-11-21T15:27:26.4285871 Output 966609 application/pdf Version of Record true © The Author(s), 2025. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence. true eng http://creativecommons.org/licenses/by/4.0
title Assessing real-world movements using consumer-grade wearable devices: Measuring segment orientations and movement quality
spellingShingle Assessing real-world movements using consumer-grade wearable devices: Measuring segment orientations and movement quality
Alex Swain
Melitta McNarry
Kelly Mackintosh
title_short Assessing real-world movements using consumer-grade wearable devices: Measuring segment orientations and movement quality
title_full Assessing real-world movements using consumer-grade wearable devices: Measuring segment orientations and movement quality
title_fullStr Assessing real-world movements using consumer-grade wearable devices: Measuring segment orientations and movement quality
title_full_unstemmed Assessing real-world movements using consumer-grade wearable devices: Measuring segment orientations and movement quality
title_sort Assessing real-world movements using consumer-grade wearable devices: Measuring segment orientations and movement quality
author_id_str_mv e58af411e7a9cdf4197ff81cad1eb321
062f5697ff59f004bc8c713955988398
bdb20e3f31bcccf95c7bc116070c4214
author_id_fullname_str_mv e58af411e7a9cdf4197ff81cad1eb321_***_Alex Swain
062f5697ff59f004bc8c713955988398_***_Melitta McNarry
bdb20e3f31bcccf95c7bc116070c4214_***_Kelly Mackintosh
author Alex Swain
Melitta McNarry
Kelly Mackintosh
author2 Alex Swain
Melitta McNarry
Samuel Manzano-Carrasco
Kelly Mackintosh
format Journal article
container_title Wearable Technologies
container_volume 6
publishDate 2025
institution Swansea University
issn 2631-7176
doi_str_mv 10.1017/wtc.2025.10034
publisher Cambridge University Press (CUP)
college_str Faculty of Science and Engineering
hierarchytype
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 Engineering and Applied Sciences - Sport and Exercise Sciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Sport and Exercise Sciences
document_store_str 1
active_str 0
description In recent years, there has been growing interest regarding the impact of human movement quality on health. However, assessing movement quality outside of laboratories or clinics remains challenging. This study aimed to evaluate the capabilities of consumer-grade wearables to assess movement quality and to consider optimal sensor locations. Twenty-two participants wore Polar Verity Sense magnetic, angular rate, and gravity (MARG) sensors on their chest and both wrists, thighs, and ankles, while performing repeated bodyweight movements. The Madgwick sensor-fusion algorithm was utilized to obtain three-dimensional orientations. Concurrent validity, quantified using the root-mean-square-error (RMSE), was established against a Vicon optical motion capture system following time-synchronization and coordinate-system alignment. The chest sensors demonstrated the highest accuracies overall, with mean RMSE ( RMSEmean ) less than 9.0° across all movements. In contrast, the wrist sensors varied considerably ( 5.5∘≤RMSEmean≤139.1∘ ). Ankle and thigh sensors yielded mixed results, with the RMSEmean ranging from 2.0° to 40.0°. Notably, yaw angles consistently demonstrated higher discrepancies overall, while pitch and roll were relatively more stable. This study highlights the potential of consumer-grade MARG sensors to increase the real-world applicability and accessibility of complex biomechanical models. It also accentuates the requirement for strategic sensor placement and refined calibration and postprocessing methods to ensure accuracy.
published_date 2025-11-19T05:35:39Z
_version_ 1858164898195832832
score 11.098395

Similar Items