Stark Nicole E-P, Henley Ethan S, Reilly Brianna A, Kuehl Damon R, Rowson Steve
Department of Biomedical Engineering, Virginia Tech, Blacksburg, VA, USA.
Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA; School of Neuroscience, College of Science, Virginia Tech, Blacksburg, VA, USA.
J Am Med Dir Assoc. 2025 May;26(5):105545. doi: 10.1016/j.jamda.2025.105545. Epub 2025 Mar 26.
To quantify real-world impact conditions of falls, which cause 50% to 90% of older adult traumatic brain injuries, and reconstruct them using dummy headforms to analyze kinematics and injury outcomes.
Mixed-methods: Observational and experimental.
An open-access dataset of 118 videos of head impacts at long-term care facilities was used.
Videos were analyzed to determine head impact occurrence, and for each video with a head impact, fall characteristics were recorded. Perpendicular view fall videos were analyzed using validated model-based image-matching software to track head impact velocities. From the tracked videos, falls were reconstructed with a Hybrid III headform mounted on an inverted pendulum to capture impact kinematics.
Of the 118 fall videos with head impacts, we tracked 29 videos, finding a normal velocity of 1.76 ± 1.02 m/s and a tangential velocity of 1.27 ± 0.95 m/s. Twenty-three of these impacts were reconstructed, producing peak linear acceleration (PLA) 50.2 ± 36.4 g and peak rotational acceleration (PRA) 2.91 ± 2.16 krad/s. Impacts that occurred against the floor had a 38% higher PLA and a 25% higher PRA compared with wall impacts. Compared with backward and forward falls, lateral falls resulted in 46 and 52 g higher PLA and 3.12 and 4.66 krad/s higher PRA, respectively.
Fall direction and impact surface influenced head impact accelerations, with certain fall configurations, such as lateral falls against tile, posing a greater risk for traumatic brain injuries. These findings provide critical insights into the biomechanics of older adult head impact falls and highlight the need for targeted fall prevention strategies, such as interventions that reduce the occurrence of lateral falls. In addition, this work offers foundational data for designing protective equipment, including headgear and energy-absorbing flooring, optimized for these specific kinematics.
量化导致50%至90%老年创伤性脑损伤的跌倒的实际影响情况，并使用假人头模型对其进行重建，以分析运动学和损伤结果。
混合方法：观察性和实验性。
使用了一个包含长期护理机构118个头撞击视频的开放获取数据集。
对视频进行分析以确定头部撞击的发生情况，并对每个有头部撞击的视频记录跌倒特征。使用经过验证的基于模型的图像匹配软件分析垂直视角的跌倒视频，以跟踪头部撞击速度。从跟踪的视频中，使用安装在倒立摆上的Hybrid III头模型重建跌倒，以捕捉撞击运动学。
在118个有头部撞击的跌倒视频中，我们跟踪了29个视频，发现法向速度为1.76±1.02米/秒，切向速度为1.27±0.95米/秒。其中23次撞击进行了重建，产生的峰值线性加速度（PLA）为50.2±36.4g，峰值旋转加速度（PRA）为2.91±2.16千弧度/秒。与墙壁撞击相比，与地面撞击产生的PLA高38%，PRA高25%。与向后和向前跌倒相比，侧向跌倒导致的PLA分别高46和52g，PRA分别高3.12和4.66千弧度/秒。
跌倒方向和撞击表面影响头部撞击加速度，某些跌倒形态，如侧向跌倒在瓷砖上，会造成更大的创伤性脑损伤风险。这些发现为老年头部撞击跌倒的生物力学提供了关键见解，并强调了针对性跌倒预防策略的必要性，如减少侧向跌倒发生的干预措施。此外，这项工作为设计针对这些特定运动学优化的防护设备（包括头盔和吸能地板）提供了基础数据。
