准确有效地预测ACL力:利用疲劳前后的生物机械着陆模式
Datao Xu1, Huiyu Zhou2, Wenjing Quan1
1Faculty of Sports Science, Ningbo University, Ningbo, 315211, China; Faculty of Engineering, University of Pannonia, Veszprém, 8201, Hungary; Savaria Institute of Technology, Eötvös Loránd University, Szombathely, 9700, Hungary.
Computer methods and programs in biomedicine
|August 14, 2023
概括
这项研究开发了一种深度学习模型,用于预测单腿着陆期间前十字带 (ACL) 的力量. 该模型通过分析脚运动来准确评估ACL受伤风险,有助于运动训练和伤害预防.
科学领域:
- 体育伤害的生物力学
- 人类运动的动力学和动力学.
- 机器学习在体育科学中的应用
背景情况:
- 前十字带 (ACL) 损伤在降落过程中很常见,特别是在疲劳后.
- 目前的方法难以准确检测ACL负载,阻碍有效的伤害预防和监测.
- 在疲劳后,脚运动模式可能会发生变化,这可能会增加在单腿着陆 (SL) 期间ACL受伤的风险.
研究的目的:
- 开发一个高度准确且易于实施的ACL力预测模型.
- 为了研究在疲劳后的SL期间脚运动模式和ACL力之间的关系.
- 将深度学习与生物力学数据相结合,以改善ACL损伤风险评估.
主要方法:
- 收集了56名受试者的疲劳前后SL数据.
- 探索了脚初始接触角度 (AIC),脚运动范围 (AROM) 和峰值ACL力 (PAF) 之间的关系.
- 开发了一种肌肉骨模型来计算ACL力,并使用子搜索算法 (SSA) 优化极端学习机器 (ELM) 和长短期记忆 (LSTM) 构建了一个预测模型.
主要成果:
- 在PAF和AIC (R = -0.70) 和AROM (R2 = -0.61) 之间发现了强烈的线性关系.
- 通过使用AIC和AROM,SSA-ELM模型显示出优异的预测性能 (R2 = 0.9992,MSE = 0.0023,RMSE = 0.0474).
- 结合的SSA-ELM和SSA-LSTM模型实现了优秀的整体波形ACL力预测 (R2 = 0.9947,MSE = 0.0076,RMSE = 0.0873).
结论:
- 在SL期间增加AIC和AROM可以增强下肢能量消散,减少ACL峰值力,从而降低受伤风险.
- 拟议的ACL动态负载力预测模型提供了高准确性,优秀的概括性,并且需要最小的输入变量 (松脚关节角度).
- 这个模型可以作为一个准确的ACL伤害风险评估工具,用于体育训练和监测.
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