Real-Time Gait State Estimation for Controlling an Ankle Exoskeleton on Extremely Uneven Terrain

We developed the first exoskeleton controller that estimates the user’s gait phase, phase rate, step length, and ground slope to provide adaptive assistance in extremely uneven outdoor terrain. The controller is based on an Extended Kalman Filter (EKF) and uses a regressed gait model to estimate phase and task variables using its sensors for global shank angle, global foot angle, forward heel position, and vertical heel position. The ankle exoskeleton adapts a biomimetic assistive torque profile to the real time estimates of the gait state. We demonstrate the adaptive controller in the University of Michigan’s Mars Yard and Wave Field. This video is supplemental multimedia for the article: R. Medrano, G. Thomas, C. Keais, E. Rouse, and R. Gregg, “Real-Time Gait Phase and Task Estimation for Controlling a Powered Ankle Exoskeleton on Extremely Uneven Terrain,“ under review. This work was supported by the National Institute for Child Health and Human Development.