Sanchez Erin J, Gabler Lee F, Good Ann B, Funk James R, Crandall Jeff R, Panzer Matthew B
Department of Mechanical and Aerospace Engineering at the University of Virginia, Center for Applied Biomechanics, 4040 Lewis and Clark Drive, Charlottesville, VA 22911, USA.
Biocore, LLC, 1621 Quail Run, Charlottesville, VA 22911, USA.
Clin Biomech (Bristol). 2019 Apr;64:82-89. doi: 10.1016/j.clinbiomech.2018.02.019. Epub 2018 Mar 14.
Head kinematics generated by laboratory reconstructions of professional football helmet impacts have been applied to computational models to study the biomechanics of concussion. Since the original publication of this data, techniques for evaluating accelerometer consistency and error correction have been developed. This study applies these techniques to the original reconstruction data and reanalyzes the results given the current state of concussion biomechanics.
Consistency checks were applied to the sensor data collected in the head of each test dummy. Inconsistent data were corrected using analytical techniques, and head kinematics were recalculated from the corrected data. Reconstruction videos were reviewed to identify artefactual impacts during the reconstruction to establish the region of applicability for simulations. Corrected head kinematics were input into finite element brain models to investigate strain response to the corrected dataset.
Multiple reconstruction cases had inconsistent sensor arrays caused by a problematic sensor; corrections to the arrays caused changes in calculated rotational head motion. These corrections increased median peak angular velocity for the concussion cases from 35.6 to 41.5 rad/s. Using the original kinematics resulted in an average error of 20% in maximum principal strain results for each case. Simulations of the reconstructions also demonstrated that simulation lengths less than 40 ms did not capture the entire brain strain response and under-predicted strain.
This study corrects data that were used to determine concussion risk, and indicates altered head angular motion and brain strain response for many reconstructions. Conclusions based on the original data should be re-examined based on this new study.
通过对职业橄榄球头盔撞击进行实验室重建所生成的头部运动学数据已应用于计算模型，以研究脑震荡的生物力学。自该数据首次发表以来，已开发出评估加速度计一致性和误差校正的技术。本研究将这些技术应用于原始重建数据，并根据当前脑震荡生物力学的现状重新分析结果。
对每个测试假人头部收集的传感器数据进行一致性检查。使用分析技术校正不一致的数据，并根据校正后的数据重新计算头部运动学。查看重建视频以识别重建过程中的人为撞击，从而确定模拟的适用范围。将校正后的头部运动学数据输入有限元脑模型，以研究对校正数据集的应变响应。
多个重建案例中存在由有问题的传感器导致的传感器阵列不一致；对阵列的校正导致计算出的头部旋转运动发生变化。这些校正使脑震荡案例的中位峰值角速度从35.6弧度/秒增加到41.5弧度/秒。使用原始运动学数据导致每个案例的最大主应变结果平均误差为20%。重建的模拟还表明，模拟时长小于40毫秒无法捕捉整个脑应变响应且应变预测不足。
本研究校正了用于确定脑震荡风险的数据，并表明许多重建案例中的头部角运动和脑应变响应发生了改变。基于原始数据得出的结论应根据这项新研究重新审视。
