Electromyography Signal Acquisition and Processing for Movement Analysis

This reprint focuses on recent advances in the processing of surface electromyography (EMG) signals acquired during human movement, as well as on innovative approaches to sense muscle activity.
A wide range of methods is examined, including machine learning techniques to detect the onset/offset timing of muscle activity and approaches to evaluate muscle fatigue and analyze muscle synergies and co-contractions. Applications of these techniques are explored in different medical scenarios, e.g., for the benefit of patients suffering from low back pain, stroke survivors, and patients requiring polysomnography.