Techniques of EMG signal analysis: detection, processing, classification and applications

TLDR

EMG signals are used in clinical and biomedical applications, evolvable hardware chip development, and human–computer interaction, and their acquisition requires advanced detection, decomposition, processing, and classification methods. The paper aims to illustrate various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. It presents hardware implementations for prosthetic hand control, grasp recognition, and human–computer interaction, and compares the performance of different EMG signal analysis methods. The study gives researchers a solid understanding of EMG signals and analysis procedures, enabling them to develop more powerful, flexible, and efficient applications.

Abstract

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.