Abstract
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.
Highlights
Concussion is a common injury in contact and collision sports (Donaldson et al, 2013; Gardner et al, 2014a,b; Makdissi and Davis, 2016; Dai et al, 2018; Ramkumar et al, 2019)
Researchers have reported that sub concussive head impacts are associated modest elevations of blood biomarkers over a single practice session of American football (Rubin et al, 2019), and college football players might sustain 1,000 or more sub concussive impacts to the head over the course of a season (Gysland et al, 2011; Bazarian et al, 2014)
Cumulative exposure to repetitive head impacts, over time during a single season, might be a risk factor for sustaining a concussion during that season in elite American college football players (Stemper et al, 2018), but cumulative exposure to head impacts was not associated with concussion risk in high school football players (Eckner et al, 2011)
Summary
Concussion is a common injury in contact and collision sports (Donaldson et al, 2013; Gardner et al, 2014a,b; Makdissi and Davis, 2016; Dai et al, 2018; Ramkumar et al, 2019). A number of professional sporting leagues, for example, the Australian Football League (AFL) (Davis et al, 2019a), National Football League (Ellenbogen et al, 2018; Davis et al, 2019a), National Hockey League (Davis et al, 2019a), professional rugby union (Gardner et al, 2018), and the National Rugby League (Davis et al, 2019a), have implemented sideline video surveillance as a strategy for improving the identification of concussion (Davis et al, 2019a,b). We present a method of automatic identification (classification) of head and body impacts using an instrumented mouthguard, video-verified impacts, and machine-learning algorithms
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