Learning Non-Euclidean Representations With SPD Manifold for Myoelectric Pattern Recognition
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PROJ20041
Description
The electromyography is the measure of electrical activity produced by the muscles which is usually represented as a function of time. This electromyography can be used in various applications including identifying neuromuscular diseases ,control signal for prosthetic devices ,controlling machines, robots etc. EMG based hand gesture can help to develop good computer interface that increases the quality of life of the disabled or aged people. Myoelectric signals generates from the movement of muscle and vein of lower portion of forearm as well as from the upper portion of wrist connected to forearm. From the received forearm electromyography (EMG) signals separation of individual signals through segmentation and corresponding evaluated values are used as the input for the classification of hand gestures.
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