Rearward-Facing Infant Child Restraint Systems with Support Legs in Frontal and Frontal-Oblique Impacts

Previous studies of support legs in rearward-facing infant CRS models have focused on frontal impacts and have found that the presence of a support leg is associated with a reduction in head injury metrics. However, real-world crashes often involve an oblique principal direction of force. The current study used sled tests to evaluate the effectiveness of support legs in rearward-facing infant CRS models for frontal and frontal-oblique impacts with and without a simulated front row seatback. Frontal and frontal-oblique impact sled tests were conducted using the simulated Consumer Reports test method with and without the blocker plate, which was developed to represent a front row seatback. The Q1.5 anthropomorphic test device (ATD) was seated in rearward-facing infant CRS models, which were tested with and without support legs. The presence of a support leg was associated with significant reductions of head injury metrics below injury tolerance limits for all tests, which supports the findings of previous studies. The presence of a support leg was also associated with significant reductions of peak neck tensile force. The presence of the blocker plate resulted in greater head injury metrics compared to tests without the blocker plate, but the result was non-significant. However, the fidelity of the interaction between the CRS and blocker plate as an adequate representation of the interaction that would occur in a real vehicle is not well understood. The findings from the current study continue to support the benefit of support legs in managing the energy of impact for a child in a rearward-facing CRS.

A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion

Controlled cortical impact (CCI) on porcine brain is often utilized to investigate the pathophysiology and functional outcome of focal traumatic brain injury (TBI), such as cerebral contusion (CC). Using a finite element (FE) model of the porcine brain, the localized brain strain and strain rate resulting from CCI can be computed and compared to the experimentally assessed cortical lesion. This way, tissue-level injury metrics and corresponding thresholds specific for CC can be established. However, the variability and uncertainty associated with the CCI experimental parameters contribute to the uncertainty of the provoked cortical lesion and, in turn, of the predicted injury metrics. Uncertainty quantification via probabilistic methods (Monte Carlo simulation, MCS) requires a large number of FE simulations, which results in a time-consuming process. Following the recent success of machine learning (ML) in TBI biomechanical modeling, we developed an artificial neural network as surrogate of the FE porcine brain model to predict the brain strain and the strain rate in a computationally efficient way. We assessed the effect of several experimental and modeling parameters on four FE-derived CC injury metrics (maximum principal strain, maximum principal strain rate, product of maximum principal strain and strain rate, and maximum shear strain). Next, we compared the in silico brain mechanical response with cortical damage data from in vivo CCI experiments on pig brains to evaluate the predictive performance of the CC injury metrics. Our ML surrogate was capable of rapidly predicting the outcome of the FE porcine brain undergoing CCI. The now computationally efficient MCS showed that depth and velocity of indentation were the most influential parameters for the strain and the strain rate-based injury metrics, respectively. The sensitivity analysis and comparison with the cortical damage experimental data indicate a better performance of maximum principal strain and maximum shear strain as tissue-level injury metrics for CC. These results provide guidelines to optimize the design of CCI tests and bring new insights to the understanding of the mechanical response of brain tissue to focal traumatic brain injury. Our findings also highlight the potential of using ML for computationally efficient TBI biomechanics investigations.

Evaluation of brain injury criteria based on reliability analysis

Purpose: Among the proposed brain injury metrics, Brain Injury Criteria (BrIC) is a promising tool for performing safety assessment of vehicles in the future. In this paper, the available risk curves of BrIC were re-evaluated with the use of reliability analysis and new risk curves were constructed for different injury types based on literature data of tissue-level tolerances. Moreover, the comparison of different injury metrics and their corresponding risk curves were performed. Methods: Tissue-level uncertainties of the effect and resistance were considered by random variables. The variability of the tissue-level predictors was quantified by the finite element reconstruction of 100 frontal crash tests which were performed in Simulated Injury Monitor environment. The applied tests were scaled to given BrIC magnitudes and the injury probabilities were calculated by Monte Carlo simulations. New risk curves were fitted to the observed results using Weibull and Lognormal distribution functions. Results: The available risk curves of diffuse axonal injury (DAI) could be slightly improved, and combined AIS 4+ risk curves were obtained by considering subdural hematoma and contusion as well. The performance of several injury metrics and their risk curves were evaluated based on the observed correlations with the tissue-level predictors. Conclusions: The cumulative strain damage measure and the BrIC provide the highest correlation (R2 = 0.61) and the most reliable risk curve for the evaluation of DAI. Although the observed correlation is smaller for other injury types, the BrIC and the associated reliability analysis-based risk curves seem to provide the best available method for estimating the brain injury risk for frontal crash tests.

Developing Commotio Cordis Injury Metrics by Correlating Chest Force and Rib Deformation to Left Ventricle Strain and Pressure

Commotio cordis is a sudden death mechanism that occurs when the heart is impacted during the repolarization phase of the cardiac cycle. This study aimed to investigate commotio cordis injury metrics by correlating chest force and rib deformation to left ventricle (LV) strain and pressure. We simulated 128 chest impacts using a simulation matrix which included two initial velocities, 16 impact locations spread across the transverse and sagittal plane, and four baseball stiffness levels. Results showed that an initial velocity of 17.88 m/s and an impact location over the LV was the most damaging setting across all possible settings, causing the most considerable LV strain and pressure increases. The impact force metric did not correlate with LV strain and pressure, while rib deformations located over the LV were strongly correlated to LV strain. This leads us to the recommendation of exploring new injury metrics such as the rib deformations we have highlighted for future commotio cordis safety regulations.