Recently, deep learning has become a promising technique for constructing gesture recognition classifiers from surface electromyography (sEMG) signals in human–computer interaction. In this paper, we propose a gesture recognition method with sEMG signals based on a deep multi-parallel convolutional neural network (CNN), which solves the problem that traditional machine learning methods may lose too much useful information during feature extraction. CNNs provide an efficient way to constrain the complexity of feedforward neural networks by weight sharing and restriction to local connections. Sophisticated feature extraction is to be avoided and hand gestures are to be classified directly. A multi-parallel and multi-convolution layer convolution structure is proposed to classify hand gestures. Experiment results show that in comparison with five traditional machine learning methods, the proposed method could achieve higher accuracy.

中文翻译：

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基于sEMG信号和卷积神经网络的手势识别

最近，深度学习已成为一种很有前途的技术，可用于在人机交互中从表面肌电图（sEMG）信号构建手势识别分类器。在本文中，我们提出了一种基于深度多并行卷积神经网络（CNN）的带有sEMG信号的手势识别方法，解决了传统机器学习方法在特征提取过程中可能丢失过多有用信息的问题。CNN 提供了一种通过权重共享和对局部连接的限制来限制前馈神经网络复杂性的有效方法。避免复杂的特征提取，直接对手势进行分类。提出了一种多并行多卷积层卷积结构来对手势进行分类。