Robust Gait Event Detection Using GRF and EMG Signals

Gait Event Detection (GED) plays a pivotal role in understanding human locomotion, with applications spanning rehabilitation, prosthesis design, sports science, and biomechanics. Accurate identification of key gait events—such as Heel Strike (HS), Loading Response (LS), Mid-Stance (MS), and Heel Off (HO)—during the stance phase of the gait cycle is essential for analyzing movement patterns, diagnosing gait abnormalities, and developing assistive technologies. This study leverages bio-signals, including Ground Reaction Forces (GRF) and Electromyography (EMG), to classify these events with high precision. GRF provides critical insights into the forces exerted during walking, while EMG captures muscle activation patterns, enabling a comprehensive analysis of gait dynamics.  This work underscores the significance of bio-signals in advancing gait analysis and highlights their potential to transform clinical and technological applications in human movement science.