Development of low cost EMG data acquisition system for arm activities recognition

Abstract

Electromyography (EMG) signals are becoming continuously more important in many fields, including biomedical/clinical, prosthesis, human machine interaction and rehabilitation devices. In the present study, to meet the requisites of EMG data acquisition systems, a high resolution, and highly competitive eight channel system has been developed, which is cost efficient and compact as compared to commercially available systems. To validate the developed system, EMG signals have been acquired from various muscles for different arm activities and also machine learning techniques have been utilized for activity recognition. For the current study 8 Male and 4 Female healthy subjects have been selected. For classification purpose, various time and frequency domain features have been extracted and a comparative study of different classification techniques is presented. The classification accuracy ranges from 43.64% to 92.61% for different classification algorithms. For this piece of work MATLAB 15a is utilized for signal processing and machine learning.