  Electromyography is a promising approach to the gesture recognition of humans
if an efficient classifier with high accuracy is available. In this paper, we
propose to utilize Extreme Value Machine (EVM) as a high-performance algorithm
for the classification of EMG signals. We employ reflection coefficients
obtained from an Autoregressive (AR) model to train a set of classifiers. Our
experimental results indicate that EVM has better accuracy in comparison to the
conventional classifiers approved in the literature based on K-Nearest
Neighbors (KNN) and Support Vector Machine (SVM).
