Electromyography is a science that studies or detects bioelectrical activity of muscles to analyze skills and morphological changes of the neuromuscular system and contributes to studies on the neuromuscular system. Surface electromyography (SEMG) signal is a bioelectrical signal emitted when nervous and muscular activities are recorded from the surface of human skeletons by means of poles, which can reflect the functional state of nerves and muscles under non-invasive conditions on a real-time basis. SEMG signals found a wide application in different fields including prosthesis control, sports medicine, rehabilitation medicine, and clinical diagnosis. However, how to efficiently exact features from SEMG signals to realize accurate recognition of action modes is a key issue for the practice of electromyography-controlled prostheses and to achieve precision of rehabilitation treatment. Deep learning reveals drastic changes in many fields of machine learning, including machine vision and voice recognition, over the past few years. We use convolutional neural networks (CNNs) to extract deep features from SEMG signals and classify actions. CNNs exhibit good translation invariance due to its characteristics of local connection and weight sharing. If SEMG signals were applied in the modeling of electromyography signal recognition, then the diversity of electromyography signal itself can be overcome using invariance in convolutions. Therefore, in this study, the spectrogram obtained by analyzing electromyography signals is proposed to be used as an image. Intensively used deep convolutional networks in the image were also adopted to conduct the gesture motion recognition of SEMG signals.

肌电图学是一门研究或检测肌肉的生物电活动以分析神经肌肉系统的技能和形态变化并为神经肌肉系统的研究做出贡献的科学。表面肌电图（SEMG）信号是通过极点从人的骨骼表面记录神经和肌肉活动时发出的生物电信号，它可以实时反映非侵入性条件下神经和肌肉的功能状态。SEMG信号在假体控制，运动医学，康复医学和临床诊断等不同领域得到了广泛的应用。然而，如何有效地从SEMG信号中准确识别特征，以实现对动作模式的准确识别，是肌电图控制的假体的实践以及实现康复治疗精度的关键问题。深度学习揭示了机器学习和语音识别等机器学习许多领域在过去几年中的巨大变化。我们使用卷积神经网络（CNN）从SEMG信号中提取深层特征并对动作进行分类。CNN由于其本地连接和权重共享的特性而表现出良好的平移不变性。如果将SEMG信号应用于肌电信号识别的建模，则可以使用卷积不变性来克服肌电信号本身的多样性。因此，在这项研究中 通过分析肌电信号得到的频谱图被建议用作图像。图像中密集使用的深度卷积网络也被用来进行SEMG信号的手势运动识别。