Acquisition and classification of EMG using a dual-channel EMG biopotential amplifier for controlling assistive devices

Abstract

In recent years, the development of EMG-based assistive technologies has received special attention of the researchers. This can be attributed to the advantages of EMG signal over other biosignals. The present study proposes the development of a dual-channel EMG biopotential amplifier. The biopotential amplifier was designed using INA128 instrumentation amplifier and was used for acquiring EMG signals from twenty healthy male volunteers for seven types of hand movements. The EMG signals were processed using in-lab designed LabVIEW and MATLAB programs. The processed signals were classified with an accuracy of ≥ 95% using MLP and RBF artificial neural networks and, hence, can be explored for controlling various assistive devices.