With the development of artificial intelligence technology,the recognition effect of deep learning in gesture recognition has been significantly improved.Surface electromyography(SMG)signal is an electrophysiological signal generated during muscle activity in the human body.Due to its non-invasive and easy to collect nature,it has been used as a signal source for rehabilitation aids and prosthetic control.When applying surface electromyography signals,pre-processing such as amplification and filtering is required;Then,feature extraction is carried out to obtain effective information of surface electromyography signals in the time domain,frequency domain,and timely frequency domain;Finally,by inputting this information into the machine learning model,the relevant muscle movements of the human body can be analyzed,and then the movements of the relevant instruments can be controlled.To this end,a review is mainly conducted on the feature extraction and machine learning classification models,elaborating on the current research progress and future development direction of gesture recognition based on surface electromyography signals.
维普
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