Optimization of Neck Musculature Activation for Head Kinematics in Frontal, Lateral and Rear Impact Simulations
The activation of the neck musculature plays an important role in the response of the head and neck and can affect the risk of injury under impact conditions. Yet, the role of the level (i.e., magnitude) and timing of the neck muscle activation towards injury risk remains not well understood. Advanced finite element Human Body Models (HBMs) can predict the kinematic response of the head and neck upon impact, providing critical information to researchers and vehicle safety systems designers, but there is currently a lack of verified and validated schemes for neck muscle activation. This thesis focused on improving understanding of neck musculature activation by identifying optimized activation schemes for different impact scenarios using the 50th percentile male GHBMC contemporary finite element HBM assessed with experimental human volunteer impact test data. The HBM head-neck kinematics were evaluated for frontal, lateral and rear impacts over a wide range of accelerations, which represents novel information not found in the existing literature. The two main hypotheses were: (1) for different impact scenarios, the optimal muscle activation (OMA) schemes could be determined using the experimental volunteer kinematics, and (2) that a single muscle activation scheme could achieve a good correlation for all impact cases. The optimization results were assessed using volunteer data of 119 frontal impacts between 2g and 15g, 72 lateral impacts between 4g and 7g and 12 rear impacts between 3g and 4g. The frontal and lateral impacts data was collected from widely referenced studies with 16 volunteers, and rear impact data was collected from recent tests with 12 volunteers. No muscle activation data was recorded in the available experimental data; however, the studies presented kinematics of the head and first thoracic (T1) vertebra that were compared to the output of the computational HBM. The optimized muscle activation schemes improved the kinematic response for all impact cases (maximum average improvement of 35% for the frontal impacts) and could be used to elucidate the influence of muscle activation and onset time in head kinematics in other impact severities and directions. A novel Cocontraction Muscle Activation (CMA) scheme presented good correlation with frontal (23% average improvement), lateral (17% average improvement) and rear (6% average improvement) impacts, confirming the hypothesis that a unique activation scheme could be used to achieve an improved correlation of HBM global head kinematics with the experimental data. Furthermore, this work identified that the rear impact simulations demonstrated less sensitivity than the other impact directions for different muscle activation schemes. The lower sensitivity could be attributed to the reduced force associated with flexor muscles, which were antagonistic to the head movement. In conclusion, the optimized muscle activation scheme helped contextualize the neck muscle activation level and onset time through the identification of the sensitive parameters to impact, and the CMA scheme provided overall good correlation in all impact directions. The results from this work will enhance computational HBM that may better inform and develop preventions of injury to the head and neck during impact.
Cite this version of the work
Matheus Correia (2020). Optimization of Neck Musculature Activation for Head Kinematics in Frontal, Lateral and Rear Impact Simulations. UWSpace. http://hdl.handle.net/10012/16064