Abstract
Brain strain indices calculated using human finite element models have recently been used to evaluate the brain injury risk in automobile accidents. However, the calculation using a finite element model of a human body is computationally expensive and time-consuming, making it impossible to evaluate brain strain indices immediately after a crash test. Therefore, this paper develops a deep learning model to predict the brain strain waveform from the angular velocity waveform of the head that can be measured by a crash dummy. The results of a comparison between the brain strain waveform obtained by finite element analysis and the waveform predicted by the developed deep learning model showed that the CORA evaluation exceeded 0.9, indicating that the model predicted the waveform with a high degree of accuracy.
