Electromyography Data

Electromyography (EMG) data, reflecting electrical activity in muscles, is increasingly used to understand and control human movement. Current research focuses on improving the accuracy and efficiency of EMG-based gesture and gait phase recognition using machine learning techniques, including neural networks, random forests, and Gaussian processes, often coupled with signal processing methods like canonical correlation analysis to enhance robustness. These advancements have significant implications for prosthetic control, exoskeleton design, and human-computer interfaces, offering more intuitive and personalized assistive technologies. Furthermore, the analysis of EMG data is proving valuable in diagnosing conditions like diabetic sensorimotor polyneuropathy.