Development of a Machine-Learning-Based Classifier for the Identification of Head and Body Impacts in Elite Level Australian Rules Football Players

Background: Exposure to thousands of head and body impacts during a career in contact and collision sports may contribute to current or later life issues related to brain health. Wearable technology enables the measurement of impact exposure. The validation of impact detection is required for accura...

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Autores principales:	Peter Goodin, Andrew J. Gardner, Nasim Dokani, Ben Nizette, Saeed Ahmadizadeh, Suzi Edwards, Grant L. Iverson
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	Australian football brain concussion instrumented mouthguard kinematics impacts machine learning Sports GV557-1198.995
Acceso en línea:	https://doaj.org/article/deb24f8cbdcb4b3b85cf138b6881ebcd
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spelling	oai:doaj.org-article:deb24f8cbdcb4b3b85cf138b6881ebcd2021-11-19T05:43:20ZDevelopment of a Machine-Learning-Based Classifier for the Identification of Head and Body Impacts in Elite Level Australian Rules Football Players2624-936710.3389/fspor.2021.725245https://doaj.org/article/deb24f8cbdcb4b3b85cf138b6881ebcd2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fspor.2021.725245/fullhttps://doaj.org/toc/2624-9367Background: Exposure to thousands of head and body impacts during a career in contact and collision sports may contribute to current or later life issues related to brain health. Wearable technology enables the measurement of impact exposure. The validation of impact detection is required for accurate exposure monitoring. In this study, we present a method of automatic identification (classification) of head and body impacts using an instrumented mouthguard, video-verified impacts, and machine-learning algorithms.Methods: Time series data were collected via the Nexus A9 mouthguard from 60 elite level men (mean age = 26.33; SD = 3.79) and four women (mean age = 25.50; SD = 5.91) from the Australian Rules Football players from eight clubs, participating in 119 games during the 2020 season. Ground truth data labeling on the captures used in this machine learning study was performed through the analysis of game footage by two expert video reviewers using SportCode and Catapult Vision. The visual labeling process occurred independently of the mouthguard time series data. True positive captures (captures where the reviewer directly observed contact between the mouthguard wearer and another player, the ball, or the ground) were defined as hits. Spectral and convolutional kernel based features were extracted from time series data. Performances of untuned classification algorithms from scikit-learn in addition to XGBoost were assessed to select the best performing baseline method for tuning.Results: Based on performance, XGBoost was selected as the classifier algorithm for tuning. A total of 13,712 video verified captures were collected and used to train and validate the classifier. True positive detection ranged from 94.67% in the Test set to 100% in the hold out set. True negatives ranged from 95.65 to 96.83% in the test and rest sets, respectively.Discussion and conclusion: This study suggests the potential for high performing impact classification models to be used for Australian Rules Football and highlights the importance of frequencies <150 Hz for the identification of these impacts.Peter GoodinPeter GoodinAndrew J. GardnerAndrew J. GardnerAndrew J. GardnerNasim DokaniBen NizetteSaeed AhmadizadehSuzi EdwardsSuzi EdwardsSuzi EdwardsGrant L. IversonGrant L. IversonGrant L. IversonGrant L. IversonGrant L. IversonFrontiers Media S.A.articleAustralian footballbrain concussioninstrumented mouthguardkinematicsimpactsmachine learningSportsGV557-1198.995ENFrontiers in Sports and Active Living, Vol 3 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Australian football brain concussion instrumented mouthguard kinematics impacts machine learning Sports GV557-1198.995
spellingShingle	Australian football brain concussion instrumented mouthguard kinematics impacts machine learning Sports GV557-1198.995 Peter Goodin Peter Goodin Andrew J. Gardner Andrew J. Gardner Andrew J. Gardner Nasim Dokani Ben Nizette Saeed Ahmadizadeh Suzi Edwards Suzi Edwards Suzi Edwards Grant L. Iverson Grant L. Iverson Grant L. Iverson Grant L. Iverson Grant L. Iverson Development of a Machine-Learning-Based Classifier for the Identification of Head and Body Impacts in Elite Level Australian Rules Football Players
description	Background: Exposure to thousands of head and body impacts during a career in contact and collision sports may contribute to current or later life issues related to brain health. Wearable technology enables the measurement of impact exposure. The validation of impact detection is required for accurate exposure monitoring. In this study, we present a method of automatic identification (classification) of head and body impacts using an instrumented mouthguard, video-verified impacts, and machine-learning algorithms.Methods: Time series data were collected via the Nexus A9 mouthguard from 60 elite level men (mean age = 26.33; SD = 3.79) and four women (mean age = 25.50; SD = 5.91) from the Australian Rules Football players from eight clubs, participating in 119 games during the 2020 season. Ground truth data labeling on the captures used in this machine learning study was performed through the analysis of game footage by two expert video reviewers using SportCode and Catapult Vision. The visual labeling process occurred independently of the mouthguard time series data. True positive captures (captures where the reviewer directly observed contact between the mouthguard wearer and another player, the ball, or the ground) were defined as hits. Spectral and convolutional kernel based features were extracted from time series data. Performances of untuned classification algorithms from scikit-learn in addition to XGBoost were assessed to select the best performing baseline method for tuning.Results: Based on performance, XGBoost was selected as the classifier algorithm for tuning. A total of 13,712 video verified captures were collected and used to train and validate the classifier. True positive detection ranged from 94.67% in the Test set to 100% in the hold out set. True negatives ranged from 95.65 to 96.83% in the test and rest sets, respectively.Discussion and conclusion: This study suggests the potential for high performing impact classification models to be used for Australian Rules Football and highlights the importance of frequencies <150 Hz for the identification of these impacts.
format	article
author	Peter Goodin Peter Goodin Andrew J. Gardner Andrew J. Gardner Andrew J. Gardner Nasim Dokani Ben Nizette Saeed Ahmadizadeh Suzi Edwards Suzi Edwards Suzi Edwards Grant L. Iverson Grant L. Iverson Grant L. Iverson Grant L. Iverson Grant L. Iverson
author_facet	Peter Goodin Peter Goodin Andrew J. Gardner Andrew J. Gardner Andrew J. Gardner Nasim Dokani Ben Nizette Saeed Ahmadizadeh Suzi Edwards Suzi Edwards Suzi Edwards Grant L. Iverson Grant L. Iverson Grant L. Iverson Grant L. Iverson Grant L. Iverson
author_sort	Peter Goodin
title	Development of a Machine-Learning-Based Classifier for the Identification of Head and Body Impacts in Elite Level Australian Rules Football Players
title_short	Development of a Machine-Learning-Based Classifier for the Identification of Head and Body Impacts in Elite Level Australian Rules Football Players
title_full	Development of a Machine-Learning-Based Classifier for the Identification of Head and Body Impacts in Elite Level Australian Rules Football Players
title_fullStr	Development of a Machine-Learning-Based Classifier for the Identification of Head and Body Impacts in Elite Level Australian Rules Football Players
title_full_unstemmed	Development of a Machine-Learning-Based Classifier for the Identification of Head and Body Impacts in Elite Level Australian Rules Football Players
title_sort	development of a machine-learning-based classifier for the identification of head and body impacts in elite level australian rules football players
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/deb24f8cbdcb4b3b85cf138b6881ebcd
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Development of a Machine-Learning-Based Classifier for the Identification of Head and Body Impacts in Elite Level Australian Rules Football Players

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