Motion Identification of fingerspelling by Wrist EMG Analysis

Abstract

Recent years, interfaces using biometric information are progressing. Electromyogram(EMG) has been used in a variety of situations. Many studies have measured EMG in the shoulders and arms, where there is a lot of muscle mass. In addition, wet type sensors have been often used. However, those are inconvenient to use in everyday life and high cost. In this research, we measure wrist EMG for convenience and cost. Currently, researches have been done on the wrist EMG motion identification and personal identification. These studies have conducted simple movements and a large number of electrodes for discrimination. Furthermore, authentication by password sequence with gestures has not been done. In this paper, we propose to realize motion identification and personal authentication with complex movements using a small number of electrodes. The measured data was preprocessed such as removing noise and smoothing. We compared the accuracies obtained using Support Vector Machine(SVM) and Long Short-term memory(LSTM) for motion identification and authentication. The accuracies obtained using SVM and LSTM were 60.4% and 62.4%, respectively. In this case, the number of data was small. It is therefore necessary for increasing the number of data to perform deep learning.