Development of a human head and neck muscle activation control model based on BPNN

Abstract

With the development of vehicle active safety technology and its combination with passive safety technologies, there is a need for an updated tool to assess the performance of vehicle safety systems in both the in-crash and pre-crash phases. Consequently, developing human body models with active muscles is critical to achieve improved bio-fidelity simulation results to evaluate the effects of safety systems. In the present study, an innovative human active muscle control model based on a back-propagation neural network (BPNN) controller was developed, and the active muscle response was modelled using a feedback control strategy with the BPNN algorithm implemented in a human head and neck Sim Mechanics model. The BPNN controller was used to dynamically control the muscle activation level, the most complicated and essential factor in active muscle force calculation based on the Hill equation. Then, the active muscle control model was evaluated by simulating the human response in volunteer sled tests. The results validated the proposed active muscle control method as successful and easily realized. Achieving muscle active control would further improve the bio-fidelity of the human body model.