基于模板的协同推断分析用于预测肌肉刺激
Kaitai Li1, Daming Wang2, Zuobing Chen2
1The College of Biomedical Engineering and Instrument Science, Biosensor National Special Laboratory, Zhejiang University, Hangzhou, People's Republic of China.
Physiological measurement
|September 4, 2024
概括
这项研究引入了使用协同推断和非负矩阵因子化 (NMF) 预测肌肉刺激的新方法. 这些技术可以准确地估计未测量的肌肉活动,从而减少了对表面电肌图 (sEMG) 传感器的需求.
科学领域:
- 生物医学工程 生物医学工程
- 生物力学 生物力学
- 信号处理 信号处理
背景情况:
- 表面电肌图 (sEMG) 对于生理测量至关重要,但需要大量传感器.
- 协同推断可以重建肌肉刺激,但由于使用未测量的数据而面临限制.
- 减少sEMG传感器数量对于实际应用和系统可移植性至关重要.
研究的目的:
- 开发使用非负矩阵因子化 (NMF) 的协同推断的实用方法.
- 提高预测未测量的肌肉刺激的准确性和可行性.
- 为了证明降低sEMG传感器要求的潜力.
主要方法:
- 导出可调节的高斯-拉普拉斯混合物分布NMF (GLD-NMF) 用于协同激发估计.
- 提出了一个基于模板的抽象结构 (TBES),使用测量的sEMG数据进行抽象.
- 将GLD-NMF和TBES应用于姿势,步行和达到测试中的肌肉激发数据.
主要成果:
- GLD-NMF和TBES准确地推断出未测量的肌肉刺激.
- 协同权重矩阵模板有效地减少了所需的sEMG传感器的数量.
- 验证证实了用NMF方法进行协同推断的可行性.
结论:
- 拟议的 GLD-NMF 和 TBES 方法为协同推断提供了一个实用的方法.
- 协同推断显著减少了sEMG数据尺寸,提高了系统的可移植性.
- 这一进步促进了sEMG在人机界面中的应用.
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