基于群优化的拉盖尔-沃尔特拉网络模型,用于评估肌肉疲劳状态中的EMG-力关系
Min Ma1, Xi Luo1, Shiji Xiahou1
1University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave., West Hi-Tech Zone, Chengdu, Sichuan 611731, China.
The Review of scientific instruments
|June 14, 2024
概括
这项研究引入了一种新的双输入拉盖尔-沃尔特拉网络 (LVN) 模型,以准确地估计来自电肌图 (EMG) 信号的肌肉力量,即使在疲劳时也是如此. 该模型有效地将EMG数据转化为可穿戴设备的可靠力读数.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 人体生理学 人体生理学
背景情况:
- 电肌图 (EMG) 越来越多地用于使用可穿戴设备进行非侵入性肌肉力量评估.
- 电磁信号和肌肉力量之间的关系是复杂的,非线性,随着运动和疲劳而变化.
- 由于这些非线性,在肌肉疲劳期间准确估计力量是具有挑战性的.
研究的目的:
- 开发一个可靠的模型来准确估计来自EMG信号的肌肉力量,特别是在肌肉疲劳的情况下.
- 为了解决EMG-力关系的非线性和时间变化的性质.
主要方法:
- 提出了一种双输入的拉盖尔-沃尔特拉网络 (LVN) 模型,结合了19个EMG特征,包括非高斯特征,如曲和负.
- 11个关键的EMG疲劳特征是根据疲劳测试选择的.
- 用群优化来训练LVN模型参数,并为自适应特征加权定义了惩罚术语.
主要成果:
- 该LVN模型证明了在五个疲劳阶段快速准确地适应力信号:非疲劳,轻微,轻微,严重和极端疲劳.
- 该模型有效地将处理的EMG和疲劳特征转化为实时的力信号.
- 该模型在单次训练后表现出良好的稳定性和长期有效性.
结论:
- 拟议的LVN模型提供了一种稳定高效的实时肌肉力估计方法,适用于可穿戴应用.
- 这种方法有助于监测肌肉力量和疲劳状态,增强用户使用可穿戴设备的体验.
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