Hodges Thea, Jones Adam, Del Olmo Lucía Pérez, Mishra Ashwin, Caulfield Brian, Kechadi Tahar, MacManus David, Gilchrist Michael D
School of Mechanical and Materials Engineering, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland.
School of Public Health, Physiotherapy & Sports Science, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland.
Bioengineering (Basel). 2025 Mar 31;12(4):361. doi: 10.3390/bioengineering12040361.
This study involved the simulation of five real-world head impact events in rugby, to assess the level of protection provided by a novel foam headguard, the N-Pro. The University College Dublin Brain Trauma Model (UCDBTM) was used to estimate the peak resultant head accelerations and brain tissue responses in different head impact scenarios. The input kinematics were obtained from two sources: video analysis of impact events, and real-time data obtained through instrumented mouthguards. The impact events were simulated under both unprotected and protected conditions. All simulations were performed against a rigid, non-compliant surface model. The results obtained in this study demonstrate the significant potential of the N-Pro in reducing peak head accelerations and brain tissue stress/strain responses by up to c. 70% compared to unprotected head impacts. This study highlights the headguard's promising potential to reduce the severity of impact-related injuries by effectively attenuating stresses and strains, as well as linear and rotational kinematics. Additionally, the study supports the recommendation in the literature that kinematic data collected from wearable sensors should be supplemented by video analysis to improve accident reconstructions.
本研究对橄榄球运动中五个真实世界的头部撞击事件进行了模拟，以评估一种新型泡沫头盔N-Pro提供的保护水平。都柏林大学学院脑创伤模型（UCDBTM）用于估计不同头部撞击场景下的头部合成加速度峰值和脑组织反应。输入运动学数据来自两个来源：撞击事件的视频分析，以及通过仪器化护齿获得的实时数据。撞击事件在未受保护和受保护条件下均进行了模拟。所有模拟均针对刚性、不顺应表面模型进行。本研究获得的结果表明，与未受保护的头部撞击相比，N-Pro在将头部加速度峰值和脑组织应力/应变反应降低多达约70%方面具有巨大潜力。本研究突出了该头盔通过有效衰减应力、应变以及线性和旋转运动学，在降低撞击相关损伤严重程度方面的可观潜力。此外，该研究支持文献中的建议，即从可穿戴传感器收集的运动学数据应由视频分析补充，以改进事故重建。
