An event-driven control to achieve adaptive walking assist with gait primitives

This paper presents a control method for walking assist with hip-mounted exoskeleton robots. For modeling a user´s current walking motion, a novel finite state machine is first constructed. We divide a walking cycle uniformly using the inevitable zero crossing events. When state transitions occur, we capture the current walking spatio-temporal sensor data as discrete form. By using the sensed hip data as boundary conditions, we also develop a gait primitives based motion reconstruction method. Gait primitives are a form of basis trajectories to represent various joint motions. From those methods we estimate the moment of heel landing with interpolated knee joint motions. Utilizing the user´s previous opposite step motion, we predict the positive or negative work intervals of the current step motion. This makes it possible to achieve natural `one shot´ assist by driving adapted torques fast. This assist strategy is also effective to enhance gait regularity. The measures of stride time variability are improved by over 30% for the simulated experiment. Various real experimentations demonstrate the feasibility of our approach.