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基于快球投球时个体骨盆和躯干(节段间)旋转预测肘部负荷。

Predicting elbow load based on individual pelvis and trunk (inter)segmental rotations in fastball pitching.

作者信息

Gomaz Larisa, van Trigt Bart, van der Meulen Frank, Veeger DirkJan

机构信息

Delft Institute of Applied Mathematics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands.

Department of BioMechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The Netherlands.

出版信息

Sports Biomech. 2024 Feb 14:1-16. doi: 10.1080/14763141.2024.2315230.

Abstract

The baseball pitch is a repetitive, full-body throwing motion that exposes the elbow to significant loads, leading to a high incidence of elbow injuries. Elbow injuries in pitching are often attributed to high external valgus torques as these are generally considered to be a good proxy for the load on the Ulnar Collateral Ligament. The aim of the study is to contribute to elbow load monitoring by developing a prediction model based on the pelvis and trunk peak angular velocities and their separation time. Eleven male youth elite baseball pitchers (age 17 ± 2.2 years) threw 25 fastballs at full effort off a mound. Two-level varying-intercept, varying-slope Bayesian models were used to predict external valgus torque based on (inter)segmental rotation in fastball pitching with pitcher's weight and height added to strengthen the individualisation of the prediction. The results revealed the high predictive performance of the models including a set of kinematic parameters trunk peak angular velocity and the separation time between the pelvis and trunk peak angular velocities. Such an approach allows individualised prediction of the external valgus torque for each pitcher, which has a great practical advantage compared to group-based predictions in terms of injury assessment and injury prevention.

摘要

棒球投球是一种重复性的全身投掷动作,会使肘部承受巨大负荷,导致肘部受伤的发生率很高。投球时的肘部损伤通常归因于高外部外翻扭矩,因为这些通常被认为是尺侧副韧带负荷的良好指标。本研究的目的是通过基于骨盆和躯干峰值角速度及其分离时间开发预测模型,为肘部负荷监测做出贡献。11名男性青年精英棒球投手(年龄17±2.2岁)在投手丘上全力投出25个快球。使用两级变截距、变斜率贝叶斯模型,根据快球投球中的(节段间)旋转来预测外部外翻扭矩,并加入投手的体重和身高以加强预测的个性化。结果显示模型具有很高的预测性能,包括一组运动学参数——躯干峰值角速度以及骨盆和躯干峰值角速度之间的分离时间。这种方法能够对每个投手的外部外翻扭矩进行个性化预测,与基于群体的预测相比,在损伤评估和预防方面具有很大的实际优势。

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