Stitt Danyon, Kabaliuk Natalia, Spriggs Nicole, Henley Stefan, Alexander Keith, Draper Nick
Department of Mechanical Engineering, University of Canterbury, Christchurch, Canterbury, New Zealand.
Sports Health and Rehabilitation Research Center (SHARRC), University of Canterbury, Christchurch, Canterbury, New Zealand.
PLoS One. 2025 Jan 6;20(1):e0305986. doi: 10.1371/journal.pone.0305986. eCollection 2025.
The incidence of head impacts in rugby has been a growing concern for player safety. While rugby headgear shows potential to mitigate head impact intensity during laboratory simulations, evaluating its on-field effectiveness is challenging. Current rugby-specific laboratory testing methods may not represent on-field conditions. This study aimed to create a machine-learning model capable of matching head impacts recorded via wearable sensors to the nearest match in a pre-existing library of laboratory-simulated head impacts for further investigation. Separate random forest models were trained, and optimised, on a training dataset of laboratory head impact data to predict the impact location, impact surface angle, neck inclusion, and drop height of a given laboratory head impact. The models achieved hold-out test set accuracies of 0.996, 1.0, 0.998, and 0.96 for the impact location, neck inclusion, impact surface angle, and drop height respectively. When applied to a male and female youth rugby head impact dataset, most impacts were classified as being to the side or rear of the head, with very few at the front of the head. Nearly 80% were more similar to laboratory impacts that included the neck with an impact surface angled at 30 or 45° with just under 20% being aligned with impacts onto a flat impact surface, and most were classified as low drop height impacts (7.5-30cm). Further analysis of the time series kinematics and spatial brain strain resulting from impact is required to align the laboratory head impact testing with the on-field conditions.
橄榄球运动中头部撞击的发生率一直是球员安全方面日益关注的问题。虽然橄榄球头盔在实验室模拟中显示出减轻头部撞击强度的潜力，但评估其在场上的有效性具有挑战性。目前特定于橄榄球的实验室测试方法可能无法代表场上情况。本研究旨在创建一个机器学习模型，该模型能够将通过可穿戴传感器记录的头部撞击与预先存在的实验室模拟头部撞击库中最接近的匹配撞击进行匹配，以供进一步研究。在实验室头部撞击数据的训练数据集上训练并优化了单独的随机森林模型，以预测给定实验室头部撞击的撞击位置、撞击表面角度、颈部包含情况和下落高度。这些模型在撞击位置、颈部包含情况、撞击表面角度和下落高度的留一法测试集准确率分别为0.996、1.0、0.998和0.96。当应用于男女青年橄榄球头部撞击数据集时，大多数撞击被分类为头部侧面或后部的撞击，很少有头部正面的撞击。近80%的撞击与包含颈部且撞击表面角度为30或45°的实验室撞击更相似，不到20%与平坦撞击表面上的撞击一致，并且大多数被分类为低下落高度撞击（7.5 - 30厘米）。需要对撞击产生的时间序列运动学和空间脑应变进行进一步分析，以使实验室头部撞击测试与场上情况相匹配。
