根据头部运动学确定头部撞击位置、速度和力度

Xianghao Zhan, Yuzhe Liu, Nicholas J. Cecchi, Jessica Towns, Ashlyn A. Callan, Olivier Gevaert, Michael M. Zeineh, David B. Camarillo
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引用次数: 0

摘要

目的:包括撞击方向、速度和力量在内的头部撞击信息对于研究创伤性脑损伤、设计和评估防护装备非常重要。本研究介绍了一种深度学习模型,该模型可根据头盔撞击时头部的运动学特性准确预测头部撞击信息,包括位置、速度、方向和力:利用 16,000 个使用 Riddell 头盔有限元模型模拟的头盔头部撞击数据集,我们实施了一个长短期记忆(LSTM)网络来处理头部运动学:三轴线性加速度和角速度。结果这些模型准确预测了描述撞击位置、方向、速度和撞击力曲线的撞击参数,所有任务的 R2 均超过 70%。进一步验证使用了由仪器护齿和视频记录的现场数据集,该数据集包括 79 次头部撞击,其中撞击位置可以清晰识别。深度学习模型的表现明显优于现有方法,在识别撞击位置方面达到了 79.7% 的准确率,而传统方法的准确率较低(现有方法的最高准确率为 49.4%)。结论精确度强调了该模型通过提供更准确的撞击数据来提高头盔设计和运动安全的潜力。未来的研究应在大型活体数据集上测试各种头盔和运动的模型,以验证模型的准确性,并采用迁移学习等技术扩大其有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of head impact locations, speeds, and force based on head kinematics
Objective: Head impact information including impact directions, speeds and force are important to study traumatic brain injury, design and evaluate protective gears. This study presents a deep learning model developed to accurately predict head impact information, including location, speed, orientation, and force, based on head kinematics during helmeted impacts. Methods: Leveraging a dataset of 16,000 simulated helmeted head impacts using the Riddell helmet finite element model, we implemented a Long Short-Term Memory (LSTM) network to process the head kinematics: tri-axial linear accelerations and angular velocities. Results: The models accurately predict the impact parameters describing impact location, direction, speed, and the impact force profile with R2 exceeding 70% for all tasks. Further validation was conducted using an on-field dataset recorded by instrumented mouthguards and videos, consisting of 79 head impacts in which the impact location can be clearly identified. The deep learning model significantly outperformed existing methods, achieving a 79.7% accuracy in identifying impact locations, compared to lower accuracies with traditional methods (the highest accuracy of existing methods is 49.4%). Conclusion: The precision underscores the model's potential in enhancing helmet design and safety in sports by providing more accurate impact data. Future studies should test the models across various helmets and sports on large in vivo datasets to validate the accuracy of the models, employing techniques like transfer learning to broaden its effectiveness.
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