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Classification of EMG signals with CNN features and voting ensemble classifier.

Computer methods in biomechanics and biomedical engineering·2024
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对于假肢手来说,多个尺度的EMG分类与时空注意力.

Emimal M1, W Jino Hans1, Inbamalar T M2

  • 1Department of Electronics and Communication Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, India.

Computer methods in biomechanics and biomedical engineering
|December 1, 2023
PubMed
概括

这项研究引入了一种新的框架,用于使用假肢手中的电肌学 (EMG) 信号来分类手势. 这种先进的模型实现了高精度,提高了假肢手的功能和用户的信心.

关键词:
卷积神经网络是一种卷积神经网络.电动肌谱学 电动肌谱学多头注意力多头注意力时间方面是时间方面.

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科学领域:

  • 生物医学工程 生物医学工程
  • 机器学习 机器学习
  • 康复技术 康复技术 康复技术

背景情况:

  • 电肌图 (EMG) 信号对于控制假肢至关重要.
  • 准确的手势分类对于直观的假肢控制至关重要.
  • 现有的方法可能无法完全捕捉EMG信号的复杂时空动态.

研究的目的:

  • 开发和评估使用EMG信号的手势的新型分类框架.
  • 为了利用多尺度特征和时空注意力,提高分类准确性.
  • 为了提高肌电假肢手的性能和可靠性.

主要方法:

  • 使用一个卷积神经网络 (CNN) 架构.
  • 包含一个多尺度粗粒度层,用于从1D-CNN提取增强的特征.
  • 实现了空间时间注意力机制,用于功能改进.
  • 根据处理的EMG信号特征进行分类的手势.

主要成果:

  • 在多个数据集中实现了高分类准确性:93.4% (Ninapro DB1),92.8% (DB2),91.3% (DB5) 和94.1% (DB7).
  • 证明了多尺度特征提取和注意力机制的有效性.
  • 拟议的框架显示了现实世界假肢应用的巨大潜力.

结论:

  • 开发的基于EMG的手势分类框架提供了高精度和稳定性.
  • 多尺度特征和时空注意力的集成显著提高了分类性能.
  • 这一进步可以带来更直观和可靠的假肢手的控制,提高用户的信心.