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带有CNN特征的EMG信号的分类和投票组合分类器的分类

M Emimal1, W Jino Hans1, T M Inbamalar2

  • 1Department of ECE, Sri Sivasubramaniya Nadar College of Engineering, Chennai, TamilNadu, India.

Computer methods in biomechanics and biomedical engineering
|February 6, 2024
PubMed
概括

这项研究通过使用电肌图 (EMG) 信号来增强假肢手的控制. 结合卷积神经网络 (CNN) 特性与k-最近邻居 (KNN) 整体分类器的新方法显著提高了手势分类的准确性.

关键词:
卷积神经网络是一种卷积神经网络.电动肌谱学 电动肌谱学手的手势分类手势分类.假肢是一种假体.

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