For the problem of surface electromyography (sEMG) gesture recognition, considering the fact that the traditional machine learning model is susceptible to the sEMG feature extraction method, it is difficult to distinguish the subtle differences between similar gestures. The NinaPro DB1 dataset is used as the research object, and the sEMG feature image and the Convolutional Neural Network (CNN) are combined to recognize 52 gesture movements. The CNN model effectively solves the limitations of traditional machine learning in sEMG gesture recognition, and combines 1‐dim convolution kernel to extract deep abstract features to improve the recognition effect. Finally, the simulation experiment shows that compared with the accuracy of the raw‐sEMG images based on the CNN and the sEMG‐feature‐images based on the CNN and sEMG based on the traditional machine learning, the multi‐sEMG‐features image based on the CNN is the highest, which coming up to 82.54%.

中文翻译：

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基于表面肌电特征图像的手势识别

对于表面肌电图（sEMG）手势识别问题，考虑到传统的机器学习模型易受sEMG特征提取方法的影响，很难区分相似手势之间的细微差别。NinaPro DB1数据集被用作研究对象，并且sEMG特征图像和卷积神经网络（CNN）被组合以识别52个手势运动。CNN模型有效地解决了传统机器学习在sEMG手势识别中的局限性，并结合一维卷积核提取深度抽象特征以提高识别效果。最后，