Gesture Recognition Using EMG Signals-Emerging Trends Analyze and Robotize

Abstract

The field of electromyography (EMG)-based gesture recognition has seen significant advancements over the last decade, driven by improvements in wearable sensors, embedded processing, and machine learning. This progress has enabled the interpretation of muscle activity and its translation into robotic actions, paving the way for more natural human-robot interactions. As a result, new possibilities have emerged in areas like prosthetics, rehabilitation, and smart assistive devices aimed at enhancing independence and mobility. However, EMG signals remain complex and are influenced by factors such as noise, muscle fatigue, and individual user variations. This review summarizes recent research (2015-2023) on modern sensing technologies, signal processing, machine learning models, and embedded systems that are shaping gesture-based control systems. Key challenges and future research opportunities are also highlighted, with a focus on making EMG-driven robotic systems more reliable, accessible, and deployable.