用深度学习从头盔撞击数据精准反推撞击位置、速度和力度。
Identification of head impact locations, speeds, and force based on head kinematics
- 基于LSTM网络分析头部加速度与角速度，预测撞击参数。
- 在79次真实撞击中定位准确率达79.7%，远超传统方法的49.4%。
- 适合体育安全研究与头盔设计优化，可推广至多类运动场景。
目的：撞击方向、速度与力值等头部撞击信息对创伤性脑损伤研究及防护装备设计至关重要。本研究提出一种深度学习模型，基于戴头盔撞击时的头部运动学数据，准确预测撞击位置、方向、速度及冲击力曲线。方法：利用16,000次基于Riddell头盔有限元模型的模拟撞击数据，采用长短期记忆（LSTM）网络处理三轴线性加速度与角速度。结果：模型在所有任务中均实现超过70%的决定系数（R²），表现优异。进一步在真实赛场数据集上验证，该数据集由佩戴传感器的口套与视频记录组成，包含79次可明确识别撞击位置的头部撞击。模型显著优于现有方法，撞击位置识别准确率达79.7%，而现有方法最高仅49.4%。结论：该模型精度表明其在提升头盔设计与运动安全方面具有潜力。未来研究应在多种头盔与大型在体数据集上测试，结合迁移学习以增强泛化能力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。