Autonomous rehabilitation training for assisted patients with injured upper-limbs promotes the regenerative communication between muscle signals and brain consciousness. Surface electromyographic (sEMG) is a type of electrical signals of neuromuscular activity recorded by electrodes on the surface of the human body, which is widely applied for detecting gestures and stimuli reactions. Experimental results proved the importance of the sEMG signals for extracting such reactions, in which, the segmentation and classification of the sEMG are vital tasks. The objective of the present work is to segment and classify the sEMG signals of patients to assist the design of clinical rehabilitation devices based on the classification of sEMG signals. In the pre-processing stage, a dual-tone multi-frequency signaling is designed for signal coding; subsequently, the pre-processed sEMG signal is transformed by the Fast Fourier Transfer. Afterward, a time-series frequency analysis is performed by applying Hidden Markov Models. A basic traditional long short-term memory (LSTM) model is addressed for waveform-based classification to be compared to the proposed improved deep BP (back-propagation)–LSTM for sEMG signal classification. Seventeen performance features are selected for evaluating the proposed multi-classification, deep learning model for classifying six actions, namely moving gesture of grip, slowly moving, flexor, straight lift, stretch, and up-high lift; which were proposed by rehabilitation physician. The experiment results indicated the superiority of the proposed method compared to other well-known classifiers, such as the neural network, support vector machine, decision trees, Bayes inference, and recurrent neural network. The proposed deep BP–LSTM network achieved 92% accuracy, 89% specificity, 91% precision, and 96% F1-score, in the multi-classification of the sEMG signals.

对上肢受伤的患者进行的自主康复训练可以促进肌肉信号与大脑意识之间的再生沟通。表面肌电图（sEMG）是一种由人体表面上的电极记录的神经肌肉活动的电信号，已广泛应用于检测手势和刺激反应。实验结果证明了sEMG信号对于提取此类反应的重要性，其中sEMG的分割和分类是至关重要的任务。本工作的目的是对患者的sEMG信号进行细分和分类，以帮助基于sEMG信号的分类设计临床康复设备。在预处理阶段，为信号编码设计了双音多频信令。后来，预处理的sEMG信号通过快速傅里叶传递进行转换。之后，通过应用隐马尔可夫模型进行时间序列频率分析。针对基于波形的分类，解决了基本的传统长期短期记忆（LSTM）模型，将其与针对sEMG信号分类提出的改进的深度BP（反向传播）–LSTM进行了比较。选择了十七种性能特征来评估所提出的多分类深度学习模型，以对六个动作进行分类，这些动作包括抓握的移动手势，缓慢移动，屈肌，直举，伸展和高举；由康复医师提出。实验结果表明，与其他知名分类器（如神经网络，支持向量机，决策树，贝叶斯推理，和递归神经网络。在sEMG信号的多分类中，提出的深层BP–LSTM网络实现了92％的准确性，89％的特异性，91％的准确性和96％的F1评分。