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Instrumented Mouthguards: Advancing the Functionality and Reliability / DAVID POWELL

Swansea University Author: DAVID POWELL

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DOI (Published version): 10.23889/SUThesis.68812

Abstract

Instrumented mouthguards (IMGs) are wearable devices designed to record kinematic data describing the head’s motion during potentially injurious impacts in sports. Already a popular device, IMGs are set for a surge in popularity following World Rugby’s mandate making them a requirement for all profe...

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Published: Swansea University, Wales, UK 2024
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Williams, E. M. P., and Arora, H.
URI: https://cronfa.swan.ac.uk/Record/cronfa68812
first_indexed 2025-02-06T12:20:39Z
last_indexed 2025-02-07T05:56:36Z
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recordtype RisThesis
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spelling 2025-02-06T12:37:18.9646558 v2 68812 2025-02-06 Instrumented Mouthguards: Advancing the Functionality and Reliability 1bb0856c7afda5d2ef179de3940e5cc1 DAVID POWELL DAVID POWELL true false 2025-02-06 Instrumented mouthguards (IMGs) are wearable devices designed to record kinematic data describing the head’s motion during potentially injurious impacts in sports. Already a popular device, IMGs are set for a surge in popularity following World Rugby’s mandate making them a requirement for all professional rugby players. Despite this widespread implementation, numerous aspects of the design may be improved. Specific improvements include stricter measures to ensure the validity of recorded data and the extraction of further information about the recorded data while avoiding time-consuming video review. Machine learning algorithms have been created to address the validation issue, but not for female sports specifically or rugby union. To address this, a dataset was collected from six women’s rugby union matches, resulting in 214 impacts from 480 minutes of play. After training, a machine learning algorithm yielded scores of 0.92 and 0.85 for the area under the receiver operator and precision-recall curves (AUROC/AUPRC) respectively, on test data. This advancement signifies a crucial step in female sports' head impact telemetry, enhancing safety and data reliability in contact sports for women. Secondly, a study used kinematic recordings to create algorithms predicting impact action (ball carrier vs. tackler) and impact type (direct head contact vs. secondary acceleration). Machine learning algorithms achieved 69.4%/0.721 and 65.4%/0.744 macro recall/AUROC scores. With further refinement, this may potentially automate impact analysis, aiding athlete protection. Lastly, methods to reliably report linear acceleration are not fullyunderstood. This was investigated by estimating the linear acceleration at the head’s centre of gravity with measurements from a cohort of 25 (11F) individuals. Substantial differences between maximum and minimum impact values were found. Given the variation in head shape and size between youth, adult males and adult females, this indicates a one-size-fits-all approach will not be appropriate and individualised measurements are required to estimate linear acceleration accurately. E-Thesis Swansea University, Wales, UK Concussion, Instrumented Mouthguards, Machine Learning, Female Sport 6 12 2024 2024-12-06 10.23889/SUThesis.68812 A selection of content is redacted or is partially redacted from this thesis to protect sensitive and personal information. COLLEGE NANME COLLEGE CODE Swansea University Williams, E. M. P., and Arora, H. Doctoral Ph.D Zienkiewicz Centre for Computational Engineering grant Zienkiewicz Centre for Computational Engineering grant 2025-02-06T12:37:18.9646558 2025-02-06T12:13:28.5892303 Faculty of Science and Engineering School of Engineering and Applied Sciences - Sport and Exercise Sciences DAVID POWELL 1 68812__33519__d51dde42a0004caba484fee04cb646e7.pdf 2024_Powell_D.final.68812.pdf 2025-02-06T12:20:11.2333540 Output 4453543 application/pdf E-Thesis – open access true Copyright: The Author, David Rhys Lloyd Powell, 2024 true eng
title Instrumented Mouthguards: Advancing the Functionality and Reliability
spellingShingle Instrumented Mouthguards: Advancing the Functionality and Reliability
DAVID POWELL
title_short Instrumented Mouthguards: Advancing the Functionality and Reliability
title_full Instrumented Mouthguards: Advancing the Functionality and Reliability
title_fullStr Instrumented Mouthguards: Advancing the Functionality and Reliability
title_full_unstemmed Instrumented Mouthguards: Advancing the Functionality and Reliability
title_sort Instrumented Mouthguards: Advancing the Functionality and Reliability
author_id_str_mv 1bb0856c7afda5d2ef179de3940e5cc1
author_id_fullname_str_mv 1bb0856c7afda5d2ef179de3940e5cc1_***_DAVID POWELL
author DAVID POWELL
author2 DAVID POWELL
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publishDate 2024
institution Swansea University
doi_str_mv 10.23889/SUThesis.68812
college_str Faculty of Science and Engineering
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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
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description Instrumented mouthguards (IMGs) are wearable devices designed to record kinematic data describing the head’s motion during potentially injurious impacts in sports. Already a popular device, IMGs are set for a surge in popularity following World Rugby’s mandate making them a requirement for all professional rugby players. Despite this widespread implementation, numerous aspects of the design may be improved. Specific improvements include stricter measures to ensure the validity of recorded data and the extraction of further information about the recorded data while avoiding time-consuming video review. Machine learning algorithms have been created to address the validation issue, but not for female sports specifically or rugby union. To address this, a dataset was collected from six women’s rugby union matches, resulting in 214 impacts from 480 minutes of play. After training, a machine learning algorithm yielded scores of 0.92 and 0.85 for the area under the receiver operator and precision-recall curves (AUROC/AUPRC) respectively, on test data. This advancement signifies a crucial step in female sports' head impact telemetry, enhancing safety and data reliability in contact sports for women. Secondly, a study used kinematic recordings to create algorithms predicting impact action (ball carrier vs. tackler) and impact type (direct head contact vs. secondary acceleration). Machine learning algorithms achieved 69.4%/0.721 and 65.4%/0.744 macro recall/AUROC scores. With further refinement, this may potentially automate impact analysis, aiding athlete protection. Lastly, methods to reliably report linear acceleration are not fullyunderstood. This was investigated by estimating the linear acceleration at the head’s centre of gravity with measurements from a cohort of 25 (11F) individuals. Substantial differences between maximum and minimum impact values were found. Given the variation in head shape and size between youth, adult males and adult females, this indicates a one-size-fits-all approach will not be appropriate and individualised measurements are required to estimate linear acceleration accurately.
published_date 2024-12-06T05:37:43Z
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