Correia Matheus A, McLachlin Stewart D, Cronin Duane S
Department of Mechanical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada.
Department of Mechanical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada.
J Biomech. 2020 May 7;104:109754. doi: 10.1016/j.jbiomech.2020.109754. Epub 2020 Mar 16.
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.
在汽车碰撞场景以及诸如自动紧急制动等主动安全系统导致的驾乘人员偏移中，颈部肌肉激活对于准确预测驾乘人员反应愈发重要。使用计算人体模型对正面碰撞场景中的肌肉激活和优化尚未在与这些事件相关的广泛加速度范围内进行研究。本研究针对一系列正面碰撞严重程度（2g至15g），对用于人体志愿者实验的当代人头颈部有限元模型的肌肉激活进行了优化。颈部肌肉分为屈肌和伸肌，并基于肌肉激活水平和激活时间对每组进行了优化。优化边界使用文献数据和初步参数研究来定义。采用线性多项式方法针对每种碰撞严重程度将模型头部运动学优化至志愿者实验数据。优化后的模型预测肌肉激活会随着碰撞严重程度的增加而增加，并且相对于原始模型中的最大肌肉激活（MMA）方案，平均互相关性提高了35%（从0.561提高到0.755）。重要的是，一种新提出的用于将头部保持在中立姿势的协同收缩肌肉激活（CMA）方案与MMA方案相比，平均相关性提高了23%。总之，本研究确定了一种新方案，可在计算人体模型中跨多种碰撞严重程度获得更准确的反应运动学，同时有助于理解正面碰撞场景中的肌肉影响。
