In order to improve the accuracy of gesture recognition based on surface electromyography signal（ s EMG）

a gesture recognition method based on the combined energy characteristics of electromyography signal is proposed. Firstly

the energy spectrum of s EMG signal and its high-order differential signal are calculated. Then

the combined features based on energy spectrum are extracted. Finally

PCA algorithm is used to reduce the multigroup energy feature dimension

and the LDA classifier is used to recognize gestures. Eight groups of gestures are collected by electromyography for recognition

and the recognition accuracy of electromyography signal gesture recognition method based on combined energy features can reach 97. 5%. which is higher than that based on typical feature extraction algorithm. By using the EMG signal in UCI database

the accuracy of gesture recognition can reach 94. 5 %. Experiments show that the combined energy feature extraction algorithm is applicable to different databases

and the extracted EMG combined energy feature can make the difference between different gestures more obvious

and the accuracy of the whole gesture recognition method is higher.