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肌电时间补丁:未来的假肢将有效地利用sEMG时间模式.

Rami Mobarak, Rami Khushaba, Alessandro Mengarelli

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

概括

此摘要是机器生成的。

一种新的肌电时补丁 (MTP) 方法有效地提取下肢假体的表面电肌图 (sEMG) 特性. 这种方法可以提高控制精度,而无需高计算成本,为先进的辅助设备铺平了道路.

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科学领域:

生物医学工程生物医学工程
康复技术康复技术康复技术
信号处理信号处理

背景情况:

强大的肌电控制对于下肢辅助设备和假肢至关重要.
目前用于表面电动图 (sEMG) 信号特征提取的深度学习方法具有高的计算需求,限制了它们在假肢中的使用.
从sEMG中有效提取特征是提高肌电控制器性能的关键.

研究的目的:

介绍一种新的,计算效率高的手工特征提取方法,称为肌电时补丁 (Myoelectric Temporal Patching,MTP).
在sEMG信号中捕获短期和长期时间动态,而无需显著的计算开销.
为了验证MTP在识别下肢运动意图的有效性,用于假肢应用.

主要方法:

开发了肌电时间补丁 (MTP) 方法,用于从sEMG信号段中提取多信号特征.
应用MTP提取时间特征,在窗口中传播信息以捕获动态.
使用两个模式识别实验评估MTP:步态相识别 (SIAT-LLMD数据集) 和运动模式识别 (MyPredict 1数据集).
使用线性差异分析 (LDA),K-近邻 (KNN) 和支向量机 (SVM) 模型,与传统和空间特征比较MTP性能.

主要成果:

在所有测试的机器学习模型中,MTP特征集显著超过了传统和空间特征 (p < 0.0001).
通过使用具有MTP功能的SVM实现了85.11%的步态识别和87.70%的机动模式识别的峰值精度.
通过利用sEMG时间动态,证明了MTP有效解码下肢运动意图的能力.

结论:

拟议的Myoelectric Temporal Patching (MTP) 方法为sEMG特征提取提供了一个强大的和计算上可行的解决方案.
在捕捉时间动态方面,MTP的有效性对于推进下肢假肢和辅助设备控制至关重要.
这项工作通过提供高效准确的肌电控制解决方案来支持商业水平下肢辅助设备的开发.