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开发和评估一种评估美式橄榄球头盔性能的测试方法。

Development and Evaluation of a Test Method for Assessing the Performance of American Football Helmets.

机构信息

Biomechanics Consulting and Research, LLC, Charlottesville, VA, USA.

Biokinetics and Associates, Ltd., Ottawa, ON, Canada.

出版信息

Ann Biomed Eng. 2020 Nov;48(11):2566-2579. doi: 10.1007/s10439-020-02626-6. Epub 2020 Oct 6.

Abstract

As more is learned about injury mechanisms of concussion and scenarios under which injuries are sustained in football games, methods used to evaluate protective equipment must adapt. A combination of video review, videogrammetry, and laboratory reconstructions was used to characterize concussive impacts from National Football League games during the 2015-2017 seasons. Test conditions were generated based upon impact locations and speeds from this data set, and a method for scoring overall helmet performance was created. Head kinematics generated using a linear impactor and sliding table fixture were comparable to those from laboratory reconstructions of concussive impacts at similar impact conditions. Impact tests were performed on 36 football helmet models at two laboratories to evaluate the reproducibility of results from the resulting test protocol. Head acceleration response metric, a head impact severity metric, varied 2.9-5.6% for helmet impacts in the same lab, and 3.8-6.0% for tests performed in a separate lab when averaged by location for the models tested. Overall inter-lab helmet performance varied by 1.1 ± 0.9%, while the standard deviation in helmet performance score was 7.0%. The worst helmet performance score was 33% greater than the score of the best-performing helmet evaluated by this study.

摘要

随着对脑震荡损伤机制和足球比赛中受伤情况的了解不断增加,评估防护设备的方法必须随之适应。本研究采用视频审查、运动图像测量和实验室重建相结合的方法,对 2015-2017 赛季美国国家橄榄球联盟比赛中的脑震荡冲击进行了特征描述。根据该数据集的冲击位置和速度,生成了测试条件,并创建了一种总体头盔性能评分方法。使用线性冲击器和滑动台夹具生成的头部运动学与在相似冲击条件下进行的脑震荡冲击实验室重建相似。在两个实验室对 36 个橄榄球头盔模型进行了冲击测试,以评估由此产生的测试方案的结果重现性。头部加速度响应指标,即头部冲击严重程度指标,在同一实验室的头盔冲击中变化了 2.9-5.6%,在不同实验室的测试中变化了 3.8-6.0%,在测试的模型中按位置平均。整体实验室间头盔性能差异为 1.1±0.9%,而头盔性能评分的标准差为 7.0%。在本研究评估的头盔中,性能最差的头盔比性能最好的头盔差 33%。

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