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The objective of this study was to investigate potential for traumatic brain injuries (TBI) using a newly developed, geometrically detailed, finite element head model (FEHM) within the concept of a simulated injury monitor (SIMon). The new FEHM is comprised of several parts: cerebrum, cerebellum, falx, tentorium, combined pia-arachnoid complex (PAC) with cerebro-spinal fluid (CSF), ventricles, brainstem, and parasagittal blood vessels. The model's topology was derived from human computer tomography (CT) scans and then uniformly scaled such that the mass of the brain represents the mass of a 50th percentile male's brain (1.5 kg) with the total head mass of 4.5 kg. The topology of the model was then compared to the preliminary data on the average topology derived from Procrustes shape analysis of 59 individuals. Material properties of the various parts were assigned based on the latest experimental data.

The objective of this study is to develop a machine learning based predictive model from the available crash test data and use it for predicting injury metrics. In this study, a model was developed for predicting the head injury criterion, HIC15, using pre-test features (vehicle, test, occupant and restraint related). This problem was solved as a classification task, in which HIC15 with a threshold of 700 was divided into three classes i.e. low, medium and high. Crash test data was collected from the NHTSA database and was split into training and test datasets. Predictive models were developed from the training dataset using cross-validation while the test dataset was only used at the final step to evaluate the chosen predictive model. A logistic regression based predictive model was chosen as it demonstrated minimal overfitting and gave the highest F1 score (0.81) on the validation dataset. This chosen model gave a F1 score of 0.82 on the test (new/unseen) dataset.