EMG Classification

EMG classification focuses on accurately identifying different muscle activities from electromyography (EMG) signals, primarily to improve control in prosthetic devices and human-machine interfaces, and for diagnosing neuromuscular disorders. Current research emphasizes robust classification methods that are resilient to signal variability over time, employing techniques like deep learning (including deep residual networks), canonical correlation analysis, and optimized feature extraction methods such as multiresolution decomposition. These advancements aim to enhance the accuracy and reliability of EMG-based systems, leading to more intuitive prosthetic control and improved diagnostic tools for clinicians.