Classification of surface electromyographic signal using fuzzy logic for prosthesis control application

This paper describes the classification stage of an electromyographic (EMG) control system for prosthetic hand application. Moving ApEn was used as main method to extract features from the two channels of surface EMG signal at the forearm of the upper limb. A fuzzy logic system is used to classify the extracted information in discriminating the final grip posture. The results demonstrate the ability of the system to classify the information related to different grip postures.