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.
Highlights
ELECTROMYOGRAM (EMG) is defined as muscle contraction resulting because of bioelectric signals
EMG signals can be obtained by placing surface electrodes on the muscles or needle electrodes into the muscle tissue
We focus on the wrist muscles which are the flexor carpi radialis (FCR) and extensor carpi radialis (ECR)
Summary
ELECTROMYOGRAM (EMG) is defined as muscle contraction resulting because of bioelectric signals. The source of those bioelectric signals is various electrochemical processes occurring in the body. Data logging of electrical signals in the muscles, provides important information in the diagnosis of abnormalities in both motor system and muscles [1]. EMG signals can be obtained by placing surface electrodes on the muscles or needle electrodes into the muscle tissue. Differences between the needle and surface electrodes are not of a significant degree, the surface electrodes are preferred over the needle electrode. The most important reason for this is to be a safe and non-invasive measurement method [2]
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