Development of an Approach to Analysis and Classification of EMG Signals for Prosthesis Control

This paper presents a comprehensive overview of recent advancements in biosignal processing techniques tailored for prosthetic control, specifically focusing on the analysis and classification of electromyography (EMG) signals. EMG signals, derived from muscle electrical activity, play a crucial role in prosthetic devices by enabling intuitive control through the interpretation of muscle behavior. The review begins by elucidating the fundamentals of EMG signal acquisition and processing, with a particular emphasis on preprocessing steps such as noise reduction and feature extraction. Various signal processing methods, including the Fourier transform, wavelet transform, and discrete cosine transform, are elaborated upon, highlighting their applications in analyzing EMG signals in the time–frequency domain.