Design and Implementation of Hand Movement Detection and Classification Method using Electromyogram Signal for Human-Computer Interface

One of the methods of artificial hand prosthesis control is the use of a surface electromyogram signal. There are many methods to control the prosthesis, each of which has advantages and disadvantages. In this study, first, the surface electromyogram signal of people's hands, which is related to the 6 movements that are the most active in daily movements, is recorded and then stored. To identify the movement pattern, 8 temporal features are extracted from the signal, and then the best feature is selected using a blind search of sources and given to the input of the neural network. The results showed that the average classification accuracy by multilayer perceptron and PCA output is 96.77%, while the average classification accuracy using all features is 82.77%