On-field Head Acceleration Exposure Measurements Using Instrumented Mouthguards: Multi-stage Screening to Optimize Data Quality.

Instrumented mouthguards (iMGs) are widely applied to measure head acceleration event (HAE) exposure in sports. Despite laboratory validation, on-field factors including potential sensor skull-decoupling and spurious recordings limit data accuracy. Video analysis can provide complementary information to verify sensor data but lacks quantitative kinematics reference information and suffers from subjectivity. The purpose of this study was to develop a rigorous multi-stage screening procedure, combining iMG and video as independent measurements, aimed at improving the quality of on-field HAE exposure measurements. We deployed iMGs and gathered video recordings in a complete university men's ice hockey varsity season. We developed a four-stage process that involves independent video and sensor data collection (Stage I), general screening (Stage II), cross verification (Stage III), and coupling verification (Stage IV). Stage I yielded 24,596 iMG acceleration events (AEs) and 17,098 potential video HAEs from all games. Approximately 2.5% of iMG AEs were categorized as cross-verified and coupled iMG HAEs after Stage IV, and less than 1/5 of confirmed or probable video HAEs were cross-verified with iMG data during stage III. From Stage I to IV, we observed lower peak kinematics (median peak linear acceleration from 36.0 to 10.9 g; median peak angular acceleration from 3922 to 942 rad/s2) and reduced high-frequency signals, indicative of potential reduction in kinematic noise. Our study proposes a rigorous process for on-field data screening and provides quantitative evidence of data quality improvements using this process. Ensuring data quality is critical in further investigation of potential brain injury risk using HAE exposure data.

A computational pipeline towards large-scale and multiscale modeling of traumatic axonal injury.

Contemporary biomechanical modeling of traumatic brain injury (TBI) focuses on either the global brain as an organ or a representative tiny section of a single axon. In addition, while it is common for a global brain model to employ real-world impacts as input, axonal injury models have largely been limited to inputs of either tension or compression with assumed peak strain and strain rate. These major gaps between global and microscale modeling preclude a systematic and mechanistic investigation of how tissue strain from impact leads to downstream axonal damage throughout the white matter. In this study, a unique subject-specific multimodality dataset from a male ice-hockey player sustaining a diagnosed concussion is used to establish an efficient and scalable computational pipeline. It is then employed to derive voxelized brain deformation, maximum principal strains and white matter fiber strains, and finally, to produce diverse fiber strain profiles of various shapes in temporal history necessary for the development and application of a deep learning axonal injury model in the future. The pipeline employs a structured, voxelized representation of brain deformation with adjustable spatial resolution independent of model mesh resolution. The method can be easily extended to other head impacts or individuals. The framework established in this work is critical for enabling large-scale (i.e., across the entire white matter region, head impacts, and individuals) and multiscale (i.e., from organ to cell length scales) modeling for the investigation of traumatic axonal injury (TAI) triggering mechanisms. Ultimately, these efforts could enhance the assessment of concussion risks and design of protective headgear. Therefore, this work contributes to improved strategies for concussion detection, mitigation, and prevention.

On-field instrumented mouthguard coupling.

Instrumented mouthguard (iMG) sensors have been developed to measure sports head acceleration events (HAE) in brain injury research. Laboratory validation studies show that effective coupling of iMGs with the human skull is crucial for accurate head kinematics measurements. However, iMG-skull coupling has not been investigated in on-field sports settings. The objective of this study was to assess on-field iMG coupling using infrared proximity sensing and to investigate coupling effects on kinematics signal characteristics. Forty-two university-level men's ice hockey (n = 21) and women's rugby (n = 21) athletes participated in the study, wearing iMGs during 6-7 month in-season periods. Proximity data classified video-verified HAE recordings into four main iMG coupling categories: coupled (on-teeth), decoupling (on-teeth to off-teeth), recoupling (off-teeth to on-teeth) and decoupled (off-teeth). Poorly-coupled HAEs showed significantly higher peak angular acceleration amplitudes and greater signal power in medium-high frequency bands compared with well-coupled HAEs, indicating potential iMG movements independent of the skull. Further, even video-verified true positives included poorly-coupled HAEs, and iMG coupling patterns varied between the men's hockey and women's rugby teams. Our findings show the potential of using proximity sensing in iMGs to identify poorly-coupled HAEs. Utilizing this data screening process in conjunction with video review may mitigate a key source of sensor noise and enhance the overall quality of on-field sports HAE datasets.