Laboratory Validation of Instrumented Mouthguard for Use in Sport

Stitt, Danyon; Draper, Nick; Alexander, Keith; Kabaliuk, Natalia

doi:10.3390/s21186028

Cited by 10 publications

(21 citation statements)

References 44 publications

(60 reference statements)

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“…Furthermore, Bartsch et al, [17] reported an R 2 of 0.99 for PLA and 0.98 for PRA and when comparing their iMG using a head impact dosimeter up to impact velocities of 8. [32]. In contrast to just causal relationship Bland and Altman Analysis enable i The results follow similar suit of those found by Greybe et al, [20] who reported a systematic bias of 2.5 g and − 0.5 rad/s and Stitt et al, [32] who reported a systematic bias of -0.49% and -1% for PLA and PRA.…”

Section: Discussionsupporting

confidence: 82%

“…Bland and Altman results from this study with systemic bias values of 8.1 and 2.8 g for PLA, 0.00 and −0.01 rad/s for PRV and −76 and −38 for PRA rad/s 2 with 95% LOA of ± 17 and 9.2 g for PLA, ± 1.48 and 1.47 rad/s and ± 572 an 481 rad/s/s respectively. Only two previous studies have validated iMG systems using Bland and Altman Analysis with most using only linear correlations [32]. In contrast to just causal relationship Bland and Altman Analysis enable i The results follow similar suit of those found by Greybe et al, [20] who reported a systematic bias of 2.5 g and − 0.5 rad/s and Stitt et al, [32] who reported a systematic bias of −0.49% and −1% for PLA and PRA.…”

Section: Discussionmentioning

confidence: 99%

“…Only two previous studies have validated iMG systems using Bland and Altman Analysis with most using only linear correlations [32]. In contrast to just causal relationship Bland and Altman Analysis enable i The results follow similar suit of those found by Greybe et al, [20] who reported a systematic bias of 2.5 g and − 0.5 rad/s and Stitt et al, [32] who reported a systematic bias of −0.49% and −1% for PLA and PRA.…”

Section: Discussionmentioning

confidence: 99%

“…The measured iMG and ATD data were positively correlated, with R-squared values of ICC values of 0.84 and 0.99 and 0.94 for raw PLA, PRV and PRA, which met the minimum acceptability for reliability and validity measures of >0.80 [21]. For example, Stitt et al, [32] found R-squared values of 0.99 and 0.99 for PLA and PRA. Furthermore, Bartsch et al, [17] reported an R 2 of 0.99 for PLA and 0.98 for PRA and when comparing their iMG using a head impact dosimeter up to impact velocities of 8.5 m/s .…”

Section: Discussionmentioning

confidence: 99%

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Validation of an Instrumented Mouthguard

Jones¹,

Brown

2022

Preprint

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The purpose of this study was to determine the validity of an raw and filtered acceleration time-series data from an instrumented mouthguard system against an anthropometric testing device. Testing was conducted in a laboratory using a standard impact protocol utilising the head-form of reference system and the mouthguard system. Testing occurred at 5 impact locations: facemask, front, oblique, side and back, and at four velocities (3.6, 5.5, 7.4, and 9.3 m/s) to attain a total of 55 impacts. A 160 Hz Low-pass 4th order Butterworth filter was applied to the time-series data, and was reported as raw and filtered. Peak linear acceleration, peak rotational velocity and peak rotational acceleration was statistically compared to the reference measurement system. Total concordance correlation coefficient was 90.7% and 96.9%, for raw and filtered data set. Raw and filtered had intraclass correlation coefficients of 0.85 and 0.95 for peak linear acceleration, 0.99 and 0.99 for peak rotational velocity and 0.94 and 0.95 peak rotational acceleration respectively. The instrumented mouthguard displayed high accuracy when measuring head impact kinematics in a laboratory setting. The results presented in this study provide the basis on which the instrumented mouthguard can be further developed for deployment and application within sport quantify head impact collision dynamics in order to optimise performance and brain health.

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Section: Discussionsupporting

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Validation of an Instrumented Mouthguard

Jones¹,

Brown

2022

Preprint

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“…A three-accelerometer array located in the left, central, and right regions of the mouthguard was used to provide an estimate of the angular acceleration independent of the gyroscope and allowed for a crosscheck to remove spurious readings, such as those originating from actions like mouthguard deformation rather than head kinematics. The Nexus A9 mouthguard has been shown to have good concordance with reference sensors in drop tests [LCCC = 0.997 (Stitt et al, 2021)].…”

Section: Mouthguard Specificationsmentioning

confidence: 99%

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

Goodin¹,

Gardner²,

Dokani³

et al. 2021

Front. Sports Act. Living

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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.

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