  In recent years, real-time control of prosthetic hands has gained a great
deal of attention. In particular, real-time analysis of Electromyography (EMG)
signals has several challenges to achieve an acceptable accuracy and execution
delay. In this paper, we address some of these challenges by improving the
accuracy in a shorter signal length. We first introduce a set of new feature
extraction functions applying on each level of wavelet decomposition. Then, we
propose a postprocessing approach to process the neural network outputs. The
experimental results illustrate that the proposed method enhances the accuracy
of real-time classification of EMG signals up to $95.5\%$ for $800$ msec signal
length. The proposed postprocessing method achieves higher consistency compared
with conventional majority voting and Bayesian fusion methods.
