基于卷积神经网络的肌电信号人体运动模式识别技术

Along with the rapid development of surface electromyography (sEMG)-controlled devices, such as exoskeleton robots, the application of non-stationary and aperiodic signals in advanced high-performance motion recognition system has become a notable focus in relevant fields. To achieve the cross-domain feature fusion of sEMG, a dual convolutional chains neural network based on sEMG signals is proposed. Raw sEMG signals of seven key differentiated muscles are collected and processed by feature extraction methods, and converted into energy kernel phase diagram and discrete wavelet transform coefficient feature map, which are respectively input into the CNN branch and the MobileNetV2 branch of dual convolutional chains neural networks. The extracted high-level hidden features are processed by the fusion module for full interaction. Three datasets, including the above two feature images and conventional electromyography spectrum, are prepared. Three sets of cross experiments show that the average recognition accuracy of the proposed method for six self-tested lower limb movements is 94, 19%, which is significantly better than other feature combinations and network architectures. Meanwhile, seven lower limb movements are identified with 98郾 32% steady鄄state recognition accuracy in ENABL3S open source dataset, further proving that the proposed method has excellent sEMG feature capture ability and pattern recognition accuracy.