Abstract:Head impact measurement devices enable opportunities to collect impact data directly from humans to study topics like concussion biomechanics, head impact exposure and its effects, and concussion risk reduction techniques in sports when paired with other relevant data. With recent advances in head impact measurement devices and cost-effective price points, more and more investigators are using them to study brain health questions. However, as the field's literature grows, the variance in study quality is appar… Show more
“…Furthermore, these biases not only affect peak kinematics, but also any metric or analysis derived from the kinematic traces (e.g., Head Injury Criterion, impact location, injury risk curves). Our ndings strongly support the recommendation from the recent consensus on head acceleration measurement practices (CHAMP) to conduct a "waveform review to identify and remove erroneous data" [29]. Future research should establish easy-toapply methods and criteria to conduct data cleaning and ensure the reliability of head impact datasets.…”
Section: Association Between Signal Quality and Peak Kinematicssupporting
Purpose: When used in-vivo or in biofidelic environments, many head impact sensors have shown limitations related to the quality and validity of the kinematics measured. The objectives were to assess the quality of kinematic traces from three head impact sensors, determine the effects of signal quality on peak accelerations, and compare measurements across sensors.
Methods: Head impacts were collected with instrumented mouthguards, skin patches, and headgear patches during boxing sparring. The quality of the raw kinematic traces for 442 events was visually assessed for each sensor. The proportion of high-quality recordings was analyzed by participant, type of impact, and impact location. The associations between signal quality and peak kinematics, and peak kinematics between sensors were assessed.
Results: High-quality criteria were met in 53%, 20%, and 26% of events for the mouthguard, skin patch and headgear patch, respectively. High-quality recordings were less frequent for impacts occurring close to the sensor (e.g., 30% vs. 61% for the mouthguard) and showed lower peak kinematics than low-quality recordings (p < 0.001). Despite careful selection of high-quality simultaneous recordings, there was little to no association between the sensors’ measurements (Spearman’s p ≥ 0.043).
Conclusions: The kinematic data often reflected the motion of the sensor itself rather than the motion of the head, overestimating head impact exposure. Researchers should evaluate data quality prior to analyzing kinematics or injury severity metrics. Comparison of data across studies or in relation to injury risk functions needs to be done with caution when data were acquired from different sensors.
“…Furthermore, these biases not only affect peak kinematics, but also any metric or analysis derived from the kinematic traces (e.g., Head Injury Criterion, impact location, injury risk curves). Our ndings strongly support the recommendation from the recent consensus on head acceleration measurement practices (CHAMP) to conduct a "waveform review to identify and remove erroneous data" [29]. Future research should establish easy-toapply methods and criteria to conduct data cleaning and ensure the reliability of head impact datasets.…”
Section: Association Between Signal Quality and Peak Kinematicssupporting
Purpose: When used in-vivo or in biofidelic environments, many head impact sensors have shown limitations related to the quality and validity of the kinematics measured. The objectives were to assess the quality of kinematic traces from three head impact sensors, determine the effects of signal quality on peak accelerations, and compare measurements across sensors.
Methods: Head impacts were collected with instrumented mouthguards, skin patches, and headgear patches during boxing sparring. The quality of the raw kinematic traces for 442 events was visually assessed for each sensor. The proportion of high-quality recordings was analyzed by participant, type of impact, and impact location. The associations between signal quality and peak kinematics, and peak kinematics between sensors were assessed.
Results: High-quality criteria were met in 53%, 20%, and 26% of events for the mouthguard, skin patch and headgear patch, respectively. High-quality recordings were less frequent for impacts occurring close to the sensor (e.g., 30% vs. 61% for the mouthguard) and showed lower peak kinematics than low-quality recordings (p < 0.001). Despite careful selection of high-quality simultaneous recordings, there was little to no association between the sensors’ measurements (Spearman’s p ≥ 0.043).
Conclusions: The kinematic data often reflected the motion of the sensor itself rather than the motion of the head, overestimating head impact exposure. Researchers should evaluate data quality prior to analyzing kinematics or injury severity metrics. Comparison of data across studies or in relation to injury risk functions needs to be done with caution when data were acquired from different sensors.
“…Events were labelled as ‘unclear’ when the reviewer could not confirm the HAE mechanism because the event occurred off-camera or was obscured from camera view. As per CHAMP recommendations, all raw acceleration waveforms associated with verified and unclear events were inspected for signal quality prior to inclusion in the final dataset [ 26 ]. Fig.…”
Objectives
The aim of this study was to examine the cumulative head acceleration event (HAE) exposure in male rugby players from the Under-13 (U13) to senior club level over 4 weeks of matches and training during the 2021 community rugby season.
Methods
This prospective, observational cohort study involved 328 male rugby players. Players were representative of four playing grades: U13 (N = 60, age 12.5 ± 0.6 years), U15 (N = 100, age 14.8 ± 0.9 years), U19 (N = 78, age 16.9 ± 0.7 years) and Premier senior men (N = 97, age 22.5 ± 3.1 years). HAE exposure was tracked across 48 matches and 113 training sessions. HAEs were recorded using boil-and-bite instrumented mouthguards (iMGs). The study assessed the incidence and prevalence of HAEs by ages, playing positions, and session types (match or training).
Results
For all age grades, weekly match HAE incidence was highest at lower magnitudes (10–29 g). Proportionally, younger players experienced higher weekly incidence rates during training. The U19 players had 1.36 times the risk of high-magnitude (> 30 g) events during matches, while the U13 players had the lowest risk compared with all other grades. Tackles and rucks accounted for the largest HAE burden during matches, with forwards having 1.67 times the risk of > 30 g HAEs in rucks compared with backs.
Conclusions
This study provides novel data on head accelerations during rugby matches and training. The findings have important implications for identifying populations at greatest risk of high cumulative and acute head acceleration. Findings may guide training load management and teaching of skill execution in high-risk activities, particularly for younger players who may be exposed to proportionally more contact during training and for older players during matches.
“…The study was conducted within the framework outlined in the Consensus Head Acceleration Measurement Practices (CHAMP) 2022 project to ensure best practice. 3 , 29 Instrumented mouthguards were issued to 25 players participating in six matches in the women’s premier south rugby division. All sensors remained functional during the six competitive matches, with data recorded from players representing all positions.…”
Instrumented mouthguards have been used to detect head accelerations and record kinematic data in numerous sports. Each recording requires validation through time-consuming video verification. Classification algorithms have been posed to automatically categorise head acceleration events and spurious events. However, classification algorithms must be designed and/or validated for each combination of sport, sex and mouthguard system. This study provides the first algorithm to classify head acceleration data from exclusively female rugby union players. Mouthguards instrumented with kinematic sensors were given to 25 participants for six competitive rugby union matches in an inter-university league. Across all instrumented players, 214 impacts were recorded from 460 match-minutes. Matches were video recorded to enable retrospective labelling of genuine and spurious events. Four machine learning algorithms were trained on five matches to predict these labels, then tested on the sixth match. Of the four classifiers, the support vector machine achieved the best results, with area under the receiver operator curve (AUROC) and area under the precision recall curve (AUPRC) scores of 0.92 and 0.85 respectively, on the test data. These findings represent an important development for head impact telemetry in female sport, contributing to the safer participation and improving the reliability of head impact data collection within female contact sport.
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