Classification of the foot-ground contact events in gait using emg and inertial measurement units

This thesis focuses on analyzing normal and pathological walking patterns by utilizing joint angle measurements and surface electromyography (EMG). The aim is to identify alterations in gait cycle typology by comparing EMG patterns and joint angles. Twelve volunteers participated in the study, where their EMG data was collected while walking normally, with a flatfooted gait, and with a toe-first gait. The collected data was then preprocessed using noise filtering techniques. Overall, this thesis contributes to the field of gait analysis by providing valuable insights into the characterization of normal and pathological walking patterns. The findings highlight the effectiveness of using joint angle measurements and surface electromyography in predicting walking abnormalities, particularly through the application of binary- neural network models.