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相关实验视频

Updated: Jul 28, 2025

Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

Published on: September 3, 2015

用LSTM-AE来量化跨日上肢运动估计的域移动,使用表面电肌图.

Tianzhe Bao, Chao Wang, Pengfei Yang

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

|May 30, 2023

概括

对于肌电控制的深度学习模型与每日信号变化作斗争. 一种新的方法使用重建错误量化了这种域移动,提高了控制系统的稳定性.

科学领域:

生物医学工程生物医学工程
机器学习机器学习
信号处理信号处理

背景情况:

对于上肢肌电控制的深度学习 (DL) 模型,由于不稳定的表面肌电图 (sEMG) 信号,在日间强度方面面临挑战.
这种信号变化导致域转移,对现实应用中的DL模型性能产生负面影响.

研究的目的:

提出和验证一种基于重建的方法来量化肌电控制中的域转移.
评估领域转移对混合卷积神经网络-长期短期记忆 (CNN-LSTM) 模型性能的影响.
确定量化域位移和模型性能退化之间的相关性.

主要方法:

混合CNN-LSTM框架被用作肌电控制任务 (手势分类和手腕动力学回归) 的主要模型.
一个长期短期内存自动编码器 (LSTM-AE) 被开发出来,用于重建由CNN组件提取的特征.
通过测量LSTM-AE.产生的重建错误 (RErrors) 来量化域位移.

主要成果:

来自LSTM-AE的重建错误 (RErrors) 在日间测试中的性能下降时显著增加,这将它们与日内错误区分开来.
在LSTM-AE错误和CNN-LSTM模型的性能之间观察到强烈的负相关性.
平均皮尔森相关系数达到 -0.986 ± 0.014 的分类和 -0.992 ± 0.011 的回归.

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Corticospinal Excitability Modulation During Action Observation

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Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

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结论:

拟议的LSTM-AE方法有效量化了用于肌电控制的sEMG信号中的域移.
量化域位移是CNN-LSTM模型中性能退化的可靠指标.
这种方法为提高日常使用中的肌电控制系统的稳定性和可靠性提供了一条途径.