Korean Finger Number Gesture Recognition Based on CNN Using Surface Electromyography Signals

Abstract

This study proposes a recognition strategy for Korean finger number gestures based on convolutional neural network (CNN) using surface electromyography (sEMG) signals. A few studies have reported Chinese finger number gesture recognition using sEMG signals. However, finger number gestures vary across different regions, prompting the need to investigate finger number gesture recognition specific to Koreans. To this end, six Korean finger number gestures ranging from zero to five were selected and recognized by CNN using sEMG signals acquired from four pairs of electrodes on forearm muscles. In this study, we investigated the feasibility of CNN in finger number gesture recognition using sEMG time series data. The experimental results show that CNN achieved a 100% recognition rate over six Korean finger number gestures using sEMG time-series data. A comparative analysis of different studies indicates that the proposed approach may be at least comparable to the existing studies selected in this work. It is therefore a more convenient and promising platform for recognition of finger number gestures.