Effect of muscle pre-tension and pre-impact neck posture on the kinematic response of the cervical spine in simulated low-speed rear impacts.

Computational human body models (HBMs) can identify potential injury pathways not easily accessible through experimental studies, such as whiplash induced injuries. However, previous computational studies investigating neck response to simulated impact conditions have neglected the effect of pre-impact neck posture and muscle pre-tension on the intervertebral kinematics and tissue-level response. The purpose of the present study was addressing this knowledge gap using a detailed neck model subjected to simulated low-acceleration rear impact conditions, towards improved intervertebral kinematics and soft tissue response for injury assessment. An improved muscle path implementation in the model enabled the modeling of muscle pre-tension using experimental muscle pre-stretch data determined from previous cadaver studies. Cadaveric neck impact tests and human volunteer tests with the corresponding cervical spine posture were simulated using a detailed neck model with the reported boundary conditions and no muscle activation. Computed intervertebral kinematics of the model with pre-tension achieved, for the first time, the S-shape behavior of the neck observed in low severity rear impacts of both cadaver and volunteer studies. The maximum first principal strain in the muscles for the model with pre-tension was 27% higher than that without pre-tension. Although, the pre-impact neck posture was updated to match the average posture reported in the experimental tests, the change in posture was generally small with only small changes in vertebral kinematics and muscle strain. This study provides a method to incorporate muscle pre-tension in HBM and quantifies the importance of pre-tension in calculating tissue-level distractions.

Vestibulocollic and Cervicocollic Muscle Reflexes in a Finite Element Neck Model During Multidirectional Impacts.

Active neck musculature plays an important role in the response of the head and neck during impact and can affect the risk of injury. Finite element Human Body Models (HBM) have been proposed with open and closed-loop controllers for activation of muscle forces; however, controllers are often calibrated to specific experimental loading cases, without considering the intrinsic role of physiologic muscle reflex mechanisms under different loading conditions. This study aimed to develop a single closed-loop controller for neck muscle activation in a contemporary male HBM based on known reflex mechanisms and assess how this approach compared to current open-loop controllers across a range of impact directions and severities. Controller parameters were optimized using volunteer data and independently assessed across twelve impact conditions. The kinematics from the closed-loop controller simulations showed good average CORA rating to the experimental data (0.699) for the impacts following the ISO/TR9790 standard. Compared to previously optimized open-loop activation strategy, the average difference was less than 9%. The incorporation of the reflex mechanisms using a closed-loop controller can provide robust performance for a range of impact directions and severities, which is critical to improving HBM response under a larger spectrum of automotive impact simulations.

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

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.