International Research Journal of Education and Technology

Electromyography (EMG) signals play a pivotal role in the development of advanced human-machine interfaces, prosthetic control systems, and rehabilitation devices. These electrical signals, generated by muscle fibers during contraction, are captured through non-invasive sensors placed on the skin. By interpreting EMG signals, it is possible to translate human muscle activity into meaningful commands for machines or computers, allowing for the control of robotic arms, virtual environments, and other devices using specific muscle movements. However, the complexity of EMG signals, which are often noisy and influenced by factors such as muscle fatigue, electrode placement, and cross-talk, poses a significant challenge in accurate gesture recognition. Raw EMG data requires extensive preprocessing, decomposition, and feature extraction to be interpreted effectively. The signal decomposition process involves isolating individual motor unit activities and extracting relevant features, which can then be analyzed by machine learning algorithms to distinguish between various gestures. This paper explores the methodologies for EMG signal processing and highlights the integration of machine learning for efficient gesture recognition, contributing to the development of more intuitive and responsive control systems for assistive technologies.

Key Words: Electromyography (EMG), Gesture Recognition, Human-Machine Interface, Prosthetic Control, Rehabilitation Devices, Signal Processing, Feature Extraction, Machine Learning, Motor Unit Activities, Gesture Classification