Classification of Hand Gestures Based on Multi-channel EMG by Scale Average Wavelet Transform and Convolutional Neural Network

Abstract

Predicting and accurately classifying intentions for human hand gestures can be used not only for active prosthetic hands, rehabilitation robots and entertainment robots but also for artificial intelligence robots in general. In this paper, first of all, source data of three hand gestures of grasping and three hand gestures of sign language are acquired by using the armband combined with eight sEMG (surface Electromyography) sensors. To classify these hand gestures, basically simple CNN (convolutional neural network) models with raw data, short-time Fourier transform (STFT), wavelet transform (WT), and scale average wavelet transform (SAWT) are applied, and their performances are compared. Finally, it is shown that by using a CNN with SAWT images, the accuracy can be improved up to 94.6% for selected hand gestures with higher accuracy and lower computational burden than conventional multi-channel STFT or WT.