Development of a Head Acceleration Event Classification Algorithm for Female Rugby Union

L., David R.; Petrie, Freja; Docherty, Paul D.; Arora, Hari; Williams, Elisabeth; Powell, David

doi:10.1007/s10439-023-03138-9

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Development of a Head Acceleration Event Classification Algorithm for Female Rugby Union

David R. L. Powell

, Freja Petrie, Paul D. Docherty, Hari Arora

, Elisabeth Williams

, David Powell

Annals of Biomedical Engineering, Volume: 51, Issue: 6, Pages: 1322 - 1330

Swansea University Authors: Freja Petrie, Hari Arora , Elisabeth Williams , David Powell

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DOI (Published version): 10.1007/s10439-023-03138-9

Abstract

Instrumented mouthguards have been used to detect head accelerations and record kinematic data in numerous sports. Each recording requires validation through time-consuming video verification. Classification algorithms have been posed to automatically categorise head acceleration events and spurious...

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Published in:	Annals of Biomedical Engineering
ISSN:	0090-6964 1573-9686
Published:	Springer Science and Business Media LLC 2023
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa62386
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first_indexed	2023-01-20T12:50:25Z
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Each recording requires validation through time-consuming video verification. Classification algorithms have been posed to automatically categorise head acceleration events and spurious events. However, classification algorithms must be designed and/or validated for each combination of sport, sex and mouthguard system. This study provides the first algorithm to classify head acceleration data from exclusively female rugby union players. Mouthguards instrumented with kinematic sensors were given to 25 participants for six competitive rugby union matches in an inter-university league. Across all instrumented players, 214 impacts were recorded from 460 match-minutes. Matches were video recorded to enable retrospective labelling of genuine and spurious events. Four machine learning algorithms were trained on five matches to predict these labels, then tested on the sixth match. Of the four classifiers, the support vector machine achieved the best results, with area under the receiver operator curve (AUROC) and area under the precision recall curve (AUPRC) scores of 0.92 and 0.85 respectively, on the test data. 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spelling	v2 62386 2023-01-20 Development of a Head Acceleration Event Classification Algorithm for Female Rugby Union f784f2faa2ff9ae4991c3dc8a159bd0b Freja Petrie Freja Petrie true false ed7371c768e9746008a6807f9f7a1555 0000-0002-9790-0907 Hari Arora Hari Arora true false 2c5b3af00392058866bfd4af84bef390 0000-0002-8422-5842 Elisabeth Williams Elisabeth Williams true false fc993a4376907915ea6fe43b0d963bbc David Powell David Powell true false 2023-01-20 FGSEN Instrumented mouthguards have been used to detect head accelerations and record kinematic data in numerous sports. Each recording requires validation through time-consuming video verification. Classification algorithms have been posed to automatically categorise head acceleration events and spurious events. However, classification algorithms must be designed and/or validated for each combination of sport, sex and mouthguard system. This study provides the first algorithm to classify head acceleration data from exclusively female rugby union players. Mouthguards instrumented with kinematic sensors were given to 25 participants for six competitive rugby union matches in an inter-university league. Across all instrumented players, 214 impacts were recorded from 460 match-minutes. Matches were video recorded to enable retrospective labelling of genuine and spurious events. Four machine learning algorithms were trained on five matches to predict these labels, then tested on the sixth match. Of the four classifiers, the support vector machine achieved the best results, with area under the receiver operator curve (AUROC) and area under the precision recall curve (AUPRC) scores of 0.92 and 0.85 respectively, on the test data. These findings represent an important development for head impact telemetry in female sport, contributing to the safer participation and improving the reliability of head impact data collection within female contact sport. Journal Article Annals of Biomedical Engineering 51 6 1322 1330 Springer Science and Business Media LLC 0090-6964 1573-9686 Machine learning, head impact telemetry, wearable sensors, concussion, mTBI 1 6 2023 2023-06-01 10.1007/s10439-023-03138-9 http://dx.doi.org/10.1007/s10439-023-03138-9 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University SU Library paid the OA fee (TA Institutional Deal) The authors would like to thank participants and coaches for their engagement in the study. We would specifically like to thank the Zienkiewicz Centre for Computational Engineering for the scholarship of DP. Additionally, the authors would like to thank Prevent Biometrics for the technical support throughout this project. Funding Funding was provided by Economic and Social Research Council Wales Doctoral Training Partnership and Zienkiewicz Centre for Computational Engineering (ZCCE) Doctoral Scholarship, Faculty of Science and Engineering, Swansea University, Swansea, UK. 2023-06-20T16:30:25.6822902 2023-01-20T12:48:44.5164826 Faculty of Science and Engineering School of Engineering and Applied Sciences - Sport and Exercise Sciences David R. L. Powell 0000-0002-2996-7499 1 Freja Petrie 2 Paul D. Docherty 3 Hari Arora 0000-0002-9790-0907 4 Elisabeth Williams 0000-0002-8422-5842 5 David Powell 6 62386__27899__fe67e0c60e6243e2bc86074ca40b8d02.pdf 62386.VOR.pdf 2023-06-20T16:29:02.1940747 Output 690661 application/pdf Version of Record true Copyright: 2023 The Author(s). 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	Development of a Head Acceleration Event Classification Algorithm for Female Rugby Union
spellingShingle	Development of a Head Acceleration Event Classification Algorithm for Female Rugby Union Freja Petrie Hari Arora Elisabeth Williams David Powell
title_short	Development of a Head Acceleration Event Classification Algorithm for Female Rugby Union
title_full	Development of a Head Acceleration Event Classification Algorithm for Female Rugby Union
title_fullStr	Development of a Head Acceleration Event Classification Algorithm for Female Rugby Union
title_full_unstemmed	Development of a Head Acceleration Event Classification Algorithm for Female Rugby Union
title_sort	Development of a Head Acceleration Event Classification Algorithm for Female Rugby Union
author_id_str_mv	f784f2faa2ff9ae4991c3dc8a159bd0b ed7371c768e9746008a6807f9f7a1555 2c5b3af00392058866bfd4af84bef390 fc993a4376907915ea6fe43b0d963bbc
author_id_fullname_str_mv	f784f2faa2ff9ae4991c3dc8a159bd0b_*_Freja Petrie ed7371c768e9746008a6807f9f7a1555__Hari Arora 2c5b3af00392058866bfd4af84bef390__Elisabeth Williams fc993a4376907915ea6fe43b0d963bbc_*_David Powell
author	Freja Petrie Hari Arora Elisabeth Williams David Powell
author2	David R. L. Powell Freja Petrie Paul D. Docherty Hari Arora Elisabeth Williams David Powell
format	Journal article
container_title	Annals of Biomedical Engineering
container_volume	51
container_issue	6
container_start_page	1322
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institution	Swansea University
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doi_str_mv	10.1007/s10439-023-03138-9
publisher	Springer Science and Business Media LLC
college_str	Faculty of Science and Engineering
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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
url	http://dx.doi.org/10.1007/s10439-023-03138-9
document_store_str	1
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description	Instrumented mouthguards have been used to detect head accelerations and record kinematic data in numerous sports. Each recording requires validation through time-consuming video verification. Classification algorithms have been posed to automatically categorise head acceleration events and spurious events. However, classification algorithms must be designed and/or validated for each combination of sport, sex and mouthguard system. This study provides the first algorithm to classify head acceleration data from exclusively female rugby union players. Mouthguards instrumented with kinematic sensors were given to 25 participants for six competitive rugby union matches in an inter-university league. Across all instrumented players, 214 impacts were recorded from 460 match-minutes. Matches were video recorded to enable retrospective labelling of genuine and spurious events. Four machine learning algorithms were trained on five matches to predict these labels, then tested on the sixth match. Of the four classifiers, the support vector machine achieved the best results, with area under the receiver operator curve (AUROC) and area under the precision recall curve (AUPRC) scores of 0.92 and 0.85 respectively, on the test data. These findings represent an important development for head impact telemetry in female sport, contributing to the safer participation and improving the reliability of head impact data collection within female contact sport.
published_date	2023-06-01T16:30:23Z
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Development of a Head Acceleration Event Classification Algorithm for Female Rugby Union

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