- Title
- Development of a Machine-Learning-Based Classifier for the Identification of Head and Body Impacts in Elite Level Australian Rules Football Players
- Creator
- Goodin, Peter; Gardner, Andrew J.; Dokani, Nasim; Nizette, Ben; Ahmadizadeh, Saeed; Edwards, Suzi; Iverson, Grant L.
- Relation
- Frontiers in Sports and Active Living Vol. 3, Issue 19 November 2021, no. 725245
- Publisher Link
- http://dx.doi.org/10.3389/fspor.2021.725245
- Publisher
- Frontiers Research Foundation
- Resource Type
- journal article
- Date
- 2021
- 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.
- Subject
- Australian football; brain concussion; instrumented mouthguard; kinematics; impacts; machine learning
- Identifier
- http://hdl.handle.net/1959.13/1471012
- Identifier
- uon:48598
- Identifier
- ISSN:2624-9367
- Language
- eng
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