Designing of a biopotential amplifier for the acquisition and processing of subvocal electromyography signals

Abstract In the technologically advancing global network, the seamless integration of the capabilities of the assistive technology for improving the physical mobility of the physically challenged individuals has led to the development of new medical devices over the last few decades. The mobility impairment makes the proactive participation of an individual more herculean in necessary events. In the worst scenario, it prevents the active contribution of the individual. Globally, the utilization of biosignals to control the assistive devices has become the gravitating research. The current study discusses the designing and the development of a biopotential amplifier for the acquisition of subvocal electromyogram (svEMG) signals for assistive device control. The svEMG signals were acquired and processed using a single channel biopotential amplifier. Subsequently, the statistical parameters were calculated. The statistical parameters of the signals were analyzed using various statistical methods and classified using multilayer perceptron (MLP) artificial neural networks. Among the various networks, MLP 5-7-4 network attained a maximum classification efficiency of 97.5%. Thus the proposed biopotential amplifier exhibits a comprehensive capability to be used for controlling svEMG-based assistive devices.