一种基于长期短期记忆建模的补偿方法,用于肌肉协同作用
Zhengye Pan1, Lushuai Liu1, Xingman Li1
1College of Physical Education and Sports, Beijing Normal University, Beijing, China.
Medical engineering & physics
|October 14, 2023
概括
这项研究引入了一种新的肌肉协同补偿方法,以改善跑步期间的动力学预测. 半补偿提高了预测准确度,对原始肌肉协同模式的破坏最小.
科学领域:
- 生物力学 生物力学
- 神经科学是一个神经科学.
- 机器人技术 机器人技术 机器人技术
背景情况:
- 肌肉协同作用,包括时间和空间肌肉激活模式,对于预测运动动力学至关重要.
- 现有的方法经常显示预测和实际运动性能之间的差异,需要提高预测准确性.
研究的目的:
- 提出和验证一种补偿肌肉协同作用的新方法,以提高动力学特征预测的准确性.
- 为了研究不同的补偿策略 (全补偿与半补偿) 在预测运动方向变化期间的骨盆中心质量运动的有效性.
主要方法:
- 使用非负矩阵因子化 (NMF) 来提取肌肉协同作用,在不同角度的方向变化.
- 长期和短期记忆 (LSTM) 神经网络建立了一个非线性协同作用和骨盆中心质量高度之间的关联.
- 逆向传播神经网络评估了补偿协同效应在预测骨盆中心群体移动的准确性.
主要成果:
- 补偿肌肉协同作用显著提高了预测骨盆中心质量位移的准确性 (R2,p < 0.05).
- 所有补偿导致了与最终摆动的实际表现的显著差异,可能是由于个体的变化.
- 半补偿与实际性能没有显著差异,对原始协同效应的破坏较小,与角度相比,错误增加较小.
结论:
- 拟议的肌肉协同补偿方法,特别是半补偿,提高了动力学特征的预测精度.
- 半补偿提供了一种可行的方法,通过提高预测准确度,同时最大限度地减少对原始肌肉协同作用的改变,使其在不同的运动角度上强大.
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