Simulation of Intelligent Recognition Method for Hand Muscle Fatigue Actions from Multiple Perspectives
At present,the interference signal frequency and harmonic frequency noise may affect the effect of ac-tion recognition.In order to improve the accuracy of identifying hand muscle,a method of identifying hand muscle fa-tigue action from multiple perspectives based on electromyography(EMG)signal was proposed.Firstly,an EMG ac-quisition system was used to collect the EMG signals of hand muscle fatigue action.Then,a spatial filtering algorithm was applied to optimize the EMG signals and eliminate the physiological noise caused by the interference signal power frequency and harmonic frequency,thus improving the recognition accuracy.From the perspectives of time domain and frequency domain,the EMG features were extracted and input into a support vector machine(SVM).Based on the classification results of SVM,multi-view hand muscle fatigue action recognition was achieved.Experimental results show that the proposed method has better recognition performance and higher accuracy,and can effectively improve the multi-view hand muscle fatigue action recognition effect.
Hand musclesEMG signalNoise reductionFeature extractionSupport vector machine
NETL
NSTL
万方数据
维普
