Gait training algorithm based on inverse dynamics of walking rehabilitation robot, DDgo Pro

This paper describes a gait training algorithm based on the inverse dynamics of DDgo Pro, an end-effector-type walking rehabilitation robot. As a major feature, a gait training algorithm with high responsiveness and flexibility was achieved by analyzing the inverse dynamics of the five-link mechanism with a foothold attached to the end of the mechanism and compensating for the required torque for the link motion and the patient’s weight during gait training. To this end, inverse dynamics was solved using Newton Euler’s method, and three training modes were proposed. First, passive mode training performs three tasks: motion compensation of the five-link mechanism for the given constant walking speed through the inverse dynamics, patient’s weight compensation, and tracking of the gait trajectory of the foothold using PD control to practice the normal walking pattern and restore muscle strength for standing. Second, Active-assisted mode training was implemented for the patient to receive muscle assistance from an electric motor in proportion to the patient’s walking intention in addition to the reduced compensations of the motion of the five-link mechanism and patient’s weight. Third, active mode training was implemented so that the patient can perform gait training while feeling their own weight with the time-varying motion compensation of the five-link mechanism. Finally, the performance of each gait training mode was experimentally verified, and the muscle activity was measured using EMG sensors to compare the muscle assistance performances of the training modes. Furthermore, the correction of the gait motion of the affected leg according to the motor assistance in active-assisted mode was also verified.