在人类运动过程中使用ICA增强的sEMG信号和BP神经网络建模来估计关键肌单位的肌肉力量估计.
Hongyan Liu1, Jongchul Park1, Junghee Lee1
1Department of Marine Convergence Design Engineering, Pukyong National University, 45, Yongso-ro, Nam-gu, Busan 48513, Republic of Korea.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
这项研究引入了一种使用独立组件分析 (ICA) 和反向传播神经网络的新方法,用于准确预测人类运动中的肌肉力量. 它提高了效率,并减少了生物力学和康复应用的计算复杂性.
科学领域:
- 生物力学和运动控制
- 计算神经科学是一种神经科学.
- 康复工程 康复工程 康复工程
背景情况:
- 准确预测人体运动中的肌肉力量对于理解运动,优化训练和推进康复和假肢至关重要.
- 现有的肌肉强度预测方法通常在准确性和计算效率方面存在局限性.
研究的目的:
- 开发和评估一种新的方法,用于预测人体运动期间关键肌单位的肌肉力量.
- 为了提高肌肉力量预测的准确性和效率,同时降低计算复杂性.
主要方法:
- 独立组件分析 (ICA) 用于预测人体主要移动部位的肌肉力量.
- 逆向传播神经网络 (BPNN) 用于在关键肌单位预测肌肉强度.
- 基于不同样本大小的定位精度和时间来评估性能.
主要成果:
- 拟议的ICA方法实现了高定位精度 (98%的样本大小为20个).
- 在100个样本大小下,ICA方法证明了最短的局部化时间 (0.025秒).
- 基于BPNN的肌肉强度预测在100个样本大小下达到了99%的高准确性.
结论:
- 综合方法有效地优化了对人类运动的肌肉力量预测的准确性和效率.
- 这种方法显著降低了计算复杂性,为生物力学分析和临床应用提供了更实用的解决方案.
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