Abstract
Even though extensive research have been done on electromyogram (EMG) based prosthetic hand control, relatively lesser attention have been received for control of individual prosthetic hand fingers. An EMG based recognition of individual finger movements will enable a more dexterous control of prosthetic hands. This paper reports the recognition of index finger movements: flexion-extension and abduction-adduction based on two channel EMG. The focus was on deriving an EMG feature vector through combination of features for higher recognition rate based on a stability index. Feature vector derived through linear discriminant analysis of the average combination of three time and frequency domain features with the highest stability index resulted into a recognition of 98%. The recognition was using Levenberg-Marquardt (L-M) algorithm based back propagation neural network. The experimental result shows that the stability index holds promise in deriving a feature vector for higher recognition rate of finger movements.
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