一个训练有素的神经网络模型准确地预测了基于剪切波传播的走路和跑步期间阿基里斯肌应力
Jack A Martin1, Darryl G Thelen2
1Department of Mechanical Engineering, Department of Orthopedics and Rehabilitation, University of Wisconsin-Madison, 3046 Mechanical Engineering Building, 1513 University Ave, Madison, WI 53703, United States.
Journal of biomechanics
|July 10, 2023
概括
机器学习使用剪波张力计准确预测阿基里斯肌应力,减少校准的需要. 这一进步提高了这种非侵入性技术的可用性,用于测量体力活动期间肌负荷.
科学领域:
- 生物力学 生物力学
- 生物医学工程 生物医学工程
- 肌肉骨研究 研究
背景情况:
- 剪波张力计通过剪波速度测量肌负荷.
- 目前的方法需要校准,以确定绝对肌负荷.
- 其他波浪特性 (振幅,频率) 也可能表明肌负荷.
研究的目的:
- 调查机器学习是否可以从剪切波张力计数据中预测阿基里斯肌应力.
- 为了确定使用额外的波形特征是否可以消除校准的需要.
- 评估综合神经网络方法用于肌负载估计的可行性.
主要方法:
- 一个合体神经网络被训练来预测阿基里斯肌的压力.
- 利用了先前一项关于活动期间剪切波张力测量的研究的数据.
- 该模型使用剪切波速度,振幅和频率内容来估计应力.
主要成果:
- 神经网络的预测与步行 (R2 = 0.89) 和跑步 (R2 = 0.87) 的逆动态估计有很强的相关性.
- 预测错误是合理的:走路 (11 ± 2%的峰值负载) 和跑步 (25 ± 14%).
- 该模型成功地预测了使用全面的波特征集来预测肌应力.
结论:
- 机器学习,特别是合体神经网络,对校准剪切波张力计具有前景.
- 这种方法有可能减少对传统校准方法的依赖.
- 这些发现表明,在各种环境中,剪切波张力计的可用性扩大了.
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