SEMG gesture recognition based on improved ResNet50
To enhance the recognition accuracy of gesture actions in scenarios with numerous and highly similar categories,a gesture recognition method for surface electromyographic signals based on continuous wavelet transform and residual neural network ResNet50 is proposed.The raw surface EMG signals collected are first preprocessed and continuously wavelet transformed to obtain the Multi-sEMG Wavelet Map dataset,and then fed into the improved ResNet50 model for recognition and classification.The experimental results show that the improved ResNet50 network model achieves an average accuracy of 96.40%and 94.11%for 17 gesture actions in Ninapro DB2 and DB3,respectively,which is an improvement of 4.87%and 5.83%compared to the ResNet50 network model method.Achieved accurate recognition of gesture actions in situations with numerous and highly similar categories.A new scheme is provided for prosthetic hand based on non-invasive sensors and machine learning control.
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