A Machine Learning Approach for FEMG Pattern Recognition System to Control Hands-Free Wheelchair

Abstract

Recently the classification of dissimilar facial emotion using facial Electromyogram (FEMG) signal has gained increased attention of the researcher in order to interface with the bio- tribology application. The facial emotion recognition study is a cumbersome analysis where classification accuracy is a key factor. In this paper, EMGs of five facial emotion were acquired from twenty subjects. The EMG signals were recorded using a wireless data acquisition device with one pair of surface electrodes used in a bipolar configuration. Before analysis, the recorded FEMG signals were pre-processed and segmented into well-defined portions. Proposed wavelet decomposition method, transformed the FEMG signal into a set of various levels of coefficients sub-bands. Different features were extracted from this decomposed sub-bands coefficients. The extracted features sets were classified into five different classes i.e. Happiness, Anger, Disgust, Fear and Surprise using Artificial Neural Network. Finally, 93.3% classification accuracy has been achieved by using our proposed method. The findings of this work are in the process of practical applications for the processing and recognition of the facial emotion, essentially, in order to design reliable interfaces for controlling a hands-free wheelchair. This study can also be extended in the fields of Tribology in Bio-Medical applications.