Utvärdering av hjärnskadeindikatorer i fordonsrelaterade kollisioner

Traumatic brain injury (TBI) remains a significant concern in automotive safety, necessitating accurate injury metrics for predicting brain tissue deformation under impact conditions. Traditional kinematics-based injury criteria provide measurable indicators of head motion but do not directly quantify internal brain strain. In contrast, strain-based metrics derived from finite element (FE) simulations offer a more detailed assessment of brain tissue responses. However, the extent to which kinematics-based metrics accurately predict strain-based injury risk remains unclear. This study evaluates the correlation between kinematics-based and strain-based injury metrics using a dataset of 498 head impact cases from the National Highway Traffic Safety Administration (NHTSA) and previous research. Finite element simulations were conducted using the KTH head model to estimate brain strain. Statistical analyses, including linear regression, were applied to assess the predictive capabilities of various kinematics-based metrics. The results indicate that certain kinematics-based metrics, particularly DAMAGE and UBrIC, exhibit the strongest correlations with strain-based metrics, suggesting their potential suitability for estimating brain tissue deformation in automotive impacts. In contrast, traditional regulatory metrics such as HIC, HIP, and GAMBIT showed weaker correlations, highlighting their limitations in predicting diffuse brain injuries. The findings underscore the need to refine injury prediction models by incorporating rotational kinematics and strain-based assessments. This study contributes to the ongoing development of more accurate head injury criteria and highlights the potential for integrating strain-based modeling into regulatory crash testing frameworks. Future research should explore nonlinear predictive models, machine learning-based injury risk assessment, and expanded datasets covering a broader range of impact conditions. By improving the understanding of brain injury mechanisms, this research supports the development of safer vehicles and enhances injury prevention strategies in automotive safety.