Real-time brain strain calculation and visualization using deep learning

Stanford researchers at the Camarillo Lab have developed a neural-network based model that can provide real-time calculation of brain strain based on instrumented mouthguard kinematics signals. This fast and accurate calculation can help better protect athletes wearing the instrumented mouthguard disclosed in Stanford docket S15-432.

Current approaches for head impact measurement such as finite element analysis (FEA) are much slower and do not use real-time models based on the complex dynamics of the head-brain interface to predict and visualize brain strain. This new neural-network model is dramatically faster, easier to interpret, and can calculate detailed spatial resolution of brain strain, showing metrics (maximum principal strain) in each of the elements of the brain.

Stage of Development