Investigating the Relationship Between Kinematic-Based Brain Injury Metrics and Brain Tissue Strain for a Football Helmet Test Standard - Sanchez, Alexander (Université d'Ottawa / University of Ottawa, 2026)
American football athletes experience frequent head impacts that place them at risk of concussion. Although football helmet performance standards have been effective at reducing traumatic brain injuries, concussions remain prevalent, suggesting that current evaluation criteria may not adequately represent mechanisms associated with concussive injury. Maximum principal strain (MPS) is widely used as a strain-based predictor of brain injury; however, computational cost limits its application in helmet certification. As a practical alternative, helmet standards rely on kinematic-based brain injury metrics derived from head acceleration data, though their ability to represent brain tissue strain under standardized test conditions is unclear. The purpose of this study was to investigate the relationship between kinematic-based brain injury metrics and MPS using data obtained from NOCSAE standard impact tests. Youth and adult helmet models were tested using wire-guided drop and pneumatic ram impacts. Linear and rotational head kinematics were recorded and used to calculate six brain injury metrics (GSI, HIC, HIP, GAMBIT, BrIC, and UBrIC). Impact kinematics were applied to the University College Dublin Brain Trauma Model v2.0 to determine MPS. Linear, multiple, and random forest regression models were used to examine correlations of metrics with MPS and evaluate predictive performance. Kinematic brain injury metrics demonstrated poor correlations with MPS (R² < 0.4) and poor predictive capacity. Prediction accuracy improved substantially when multiple peak kinematic variables and their directional components were incorporated into multiple and random forest regression models. These findings suggest that current helmet evaluation criteria do not adequately represent brain tissue strain. Incorporating multiple kinematic variables to predict brain tissue strain may provide a more biologically meaningful approach for assessing concussive injury risk during NOCSAE football helmet testing.
Keywords: American football; Concussion; Equipment standards; Machine learning; Injury prediction