Optimization of muscle activation schemes in a finite element neck model simulating volunteer frontal impact scenarios

Abstract

Neck muscle activation is increasingly important for accurate prediction of occupant response in automotive impact scenarios and occupant excursion resulting from active safety systems such as autonomous emergency braking. Muscle activation and optimization in frontal impact scenarios using computational Human Body Models have not been investigated over the broad range of accelerations relevant to these events. This study optimized the muscle activation of a contemporary finite element model of the human head and neck for human volunteer experiments over a range of frontal impact severities (2 g to 15 g). The neck muscles were grouped as flexors and extensors, and optimization was undertaken for each group based on muscle activation level and activation time. The boundaries for optimization were defined using data from the literature and a preliminary parametric study. A linear polynomial method was used to optimize the model head kinematics to the volunteer experiments for each impact severity. The optimized models predicted muscle activation to increase with higher impact severities, and improved the average cross-correlation by 35% (0.561–0.755) relative to the Maximum Muscle Activation (MMA) scheme in the original model. Importantly, a newly proposed Cocontraction Muscle Activation (CMA) scheme for maintaining the head in a neutral posture provided a 23% on average improvement in correlation compared to the MMA scheme. In conclusion, this study identified a new scheme to obtain more accurate response kinematics across multiple impact severities in computational Human Body Models as well as contributing to the understanding of muscle influence during frontal impact scenarios.