Emg Signal Classification Using Fuzzy Logic

Year 2017, Volume: 5 Issue: 2, 97 - 101, 01.09.2017

Osman Ulkır Gokhan Gokmen Erkan Kaplanoglu

https://doi.org/10.17694/bajece.337941

Cited By: 12

Abstract

Electromyography
(EMG) signals are an important technique in the control applications of
prostatic hand. These signals, which are measured from the skin surface, are used
to perform movements such as wrist flexion / extension, forearm supination /
pronation and hand opening / closing of prosthetic devices. In this study, root
mean square, waveform length and kurtosis methods were applied to extracted EMG
signals from flexor carpi radialis and extensor carpi radialis muscles by using
two channel surface electrodes. A fuzzy logic based classification method has
been applied to classify the extracted signal features. With this method,
classification for different gripping movements has been successfully
accomplished.   

Keywords

Surface EMG, fuzzy logic, feature extraction, EMG classification

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