Recognition of wrist EMG signal patterns using neural networks

Abstract

Information terminals in recent years, including cellular phones, have high performances due to new advances in IT. If a standard (such as Bluetooth) is used, it enables us to collect and to perform operational interfaces of various apparatuses using one equipment only. For example, a cellular phone can be turned on and off, made into manners mode, a CD player's volume can be easily regulated, and so on. We call this "total operation device". However, such a device is not available yet. Therefore, we propose a recognition system based on wrist movements by focusing on ElectroMyoGram (EMG), using the body signals generated by voluntary movements of subject muscles, as the initial stage for construction of the total operation device. This paper tries to recognize EMG signals using neural networks (NNs). The electrodes under the dry state are attached to wrists and then EMG signals are measured. These EMG signals are classified using NNs into seven categories: neutral, up and down, right and left, inside twist, outside twist. The NN learns the FFT spectra of these signals in order to classify them. Moreover, we introduce a modular structure of the NN for improving the recognition accuracy. Computer simulations show that our approach is effective to classifying the EMG signals.