Gesture recognition based on surface electromyographic signal (sEMG) is one of the most used methods. The traditional manual feature extraction can only extract some low-level signal features, this causes poor classifier performance and low recognition accuracy when dealing with some complex signals. A recognition method, namely SEDCNN-SVM, is proposed to recognize sEMG of different gestures. SEDCNN-SVM consists of an improved deep convolutional neural network (DCNN) and a support vector machine (SVM). The DCNN can automatically extract and learn the feature information of sEMG through the convolution operation of the convolutional layer, so that it can capture the complex and high-level features in the data. The Squeeze and Excitation Networks (SE-Net) and the residual module were added to the model, so that the feature representation of each channel could be improved, the loss of feature information in convolutional operations was reduced, useful feature information was captured, and the problem of network gradient vanishing was eased. The SVM can improve the generalization ability and classification accuracy of the model by constructing an optimal hyperplane of the feature space. Hence, the SVM was used to replace the full connection layer and the Softmax function layer of the DCNN, the use of a suitable kernel function in SVM can improve the model's generalization ability and classification accuracy. To verify the effectiveness of the proposed classification algorithm, this method is analyzed and compared with other comparative classification methods. The recognition accuracy of SEDCNN-SVM can reach 0.955, it is significantly improved compared with other classification methods, the SEDCNN-SVM model is recognized online in real time.

翻译：基于表面肌电信号的手势识别是最常用的方法之一。传统的手工特征提取只能提取一些低层信号特征，这导致在处理某些复杂信号时分类器性能较差、识别准确率较低。本文提出了一种识别方法，即SEDCNN-SVM，用于识别不同手势的表面肌电信号。SEDCNN-SVM由一个改进的深度卷积神经网络和一个支持向量机构成。深度卷积神经网络可以通过卷积层的卷积操作自动提取和学习表面肌电信号的特征信息，从而捕获数据中复杂的高层特征。模型中加入了Squeeze and Excitation Networks和残差模块，从而改善了每个通道的特征表示，减少了卷积操作中特征信息的损失，捕获了有用的特征信息，并缓解了网络梯度消失的问题。支持向量机可以通过构建特征空间的最优超平面来提高模型的泛化能力和分类精度。因此，使用支持向量机替代了深度卷积神经网络的全连接层和Softmax函数层，在支持向量机中使用合适的核函数可以进一步提高模型的泛化能力和分类精度。为验证所提分类算法的有效性，本方法与其他对比分类方法进行了分析和比较。SEDCNN-SVM的识别准确率可达0.955，相较于其他分类方法有显著提升，且SEDCNN-SVM模型可实现实时在线识别。