A real-time EMG pattern recognition method for virtual myoelectric hand control

This study proposes a real-time electro-myogram (EMG) pattern recognition approach for the control of multifunction myoelectric hands. In techniques, time and frequency information is extracted by wavelet packet transform (WPT) and the node energy of the WPT coefficients is selected as the feature of the EMG signals. Then a novel feature selection method based on a depth recursive search algorithm is developed so that the high-dimensional features can be reduced by a supervised feature reduction algorithm. Consequently, the support vector machine (SVM) is adopted to give the recognition result. In the experiment, a real-time EMG pattern recognition system is developed to control a virtual hand with EMG signals from antebrachium. The experimental results show both the high accuracy and better real-time performance of the proposed method.