Machine Learning Interpretation of Surface Electromyography Signal to Classify Gait Sub-phases

The aim the present study was the classification of the main four gait sub-phases (Initial Stance, Mid Stance, Terminal Stance and Swing) and the prediction of the related transition events (HS, CTO, HR, TO) through a novel machine learning approach applied to only sEMG signals. sEMG signals collected from the Tibialis Anterior, Gastrocnemius Lateralis, Rectus Femoris, Vastus Lateralis and Hamstring muscles of both legs are processed and organized to feed two supervised learning classifiers implemented in cascade: a Support Vector Machine (SVM) binary classifier to discriminate between stance and swing phases followed by a further SVM 3-classes classifier to identify the three sub-phases of the stance.