Minimization of analytical injury metrics for head impact injuries.

OBJECTIVE: To compare the predictions of the head injury criterion (HIC), currently used to predict the risk of traumatic brain injury in frontal vehicle impact and pedestrian impact tests, with the predictions of other empirical and analytical injury metrics. METHODS: The appropriateness of different criteria relative to injury metrics derived from a head finite element (FE) model is investigated for different deceleration pulses in this research. Empirical injury metrics are computed by direct calculation for different analyzed pulses. In addition, for each pulse full FE model simulations of a complete human head were performed by means of the SIMon model. The computations are used to calculate the analytical injury metrics. RESULTS: This article shows that an optimal head deceleration curve based on HIC does not minimize other analytical injury metrics. The results obtained in this study suggest that the HIC criterion does not necessarily provide the same severity ranking for different external loadings to the head as the injury metrics derived from the FE models. CONCLUSION: Countermeasures designed based only on HIC could differ significantly from those based on analytical injury measures computed by FE models. The use of multiple injury metrics is recommended given that no scalar measure seems to be positively and strongly correlated with relevant injury metrics.

Human surrogates for injury biomechanics research.

This article reviews the attributes of the human surrogates most commonly used in injury biomechanics research. In particular, the merits of human cadavers, human volunteers, animals, dummies, and computational models are assessed relative to their ability to characterize the living human response and injury in an impact environment. Although data obtained from these surrogates have enabled biomechanical engineers and designers to develop effective injury countermeasures for occupants and pedestrians involved in crashes, the magnitude of the traffic safety problem necessitates expanded efforts in research and development. This article makes the case that while there are limitations and challenges associated with any particular surrogate, each provides a critical and necessary component in the continued quest to reduce crash-related injuries and fatalities.

Influence of pre-collision occupant parameters on injury outcome in a frontal collision.

Optimal performance of adaptive restraint systems in the vehicle requires an accurate assessment of occupant characteristics including physical properties and pre-collision response of the occupant. To provide a feasible framework for incorporating occupant characteristics into adaptive restraint schemes, this study evaluates the sensitivity of injury risk in frontal collisions to four occupant parameters: mass, stature, posture and bracing level. The numerical approach includes using commercial multi-body software to develop occupant models that span a range of occupant parameters representative of the real-world driver population. Coupled with a multi-body model of the vehicle interior and standard restraint system, risk of occupant injuries within specific body regions are predicted through numerical simulations in conjunction with established injury risk functions. The results show occupant posture to be the most significant parameter affecting the overall risk of injury in frontal collisions. The causal relationship as predicted using the numerical model has been compared to the traffic injury epidemiology findings, and the feasibility of an analytical methodology to provide real-time estimates of injury severity has been discussed. Preliminary estimates from the study indicate that the proposed methodology will provide a framework to optimize restraint performance and potentially reduce the risk of injuries up to 35% (based on parameter-specific optimization), using accurate information regarding the pre-collision occupant characteristics.