Wrist EMG Pattern Recognition System by Neural Networks and Multiple Principal Component Analysis

Abstract

This paper tries to recognize EMG signals by neural networks. The electrodes under the dry state are attached to wrists and then EMG is measured. These EMG signals are classified into seven categories, such as neutral, up and down, right and left, twist to inside, twist to outside by neural networks. The NN learns FFT spectra to classify them. Moreover, we structuralized NN using multiple PCA for accuracy improvement of the network. It is shown that our approach is effective to classify the EMG signals by means of computer simulations.