Finger Movement Classification from EMG Signals Using Gaussian Mixture Model

Hands are the most used parts of the limbs while performing complex and routine tasks in our daily life. Today, it is an important requirement to determine the user’s intention based on muscle activity in exoskeletons and prostheses developed for individuals with limited mobility in their hands due to traumatic, neurologic injuries, stroke etc. In this study, 5-finger movements were classified using surface electromyography (EMG) signals. The signals were acquired from forearm via the 8-channel Myo Gesture Control Armband. EMG signals from three participants were analyzed for the movements of each finger, and the activity levels of the channels were compared according to the movements. Following, movement classification was performed using the Gaussian mixture network, a statistical artificial neural network model. According to the experimental results, it was seen that the model achieved an accuracy of 73.3% in finger movement classification.