Real-Time Gait Phase and Task Estimation for Controlling a Powered Ankle Exoskeleton on Extremely Uneven Terrain

Positive biomechanical outcomes have been reported with lower limb exoskeletons in laboratory settings, but these devices have difficulty delivering appropriate assistance in synchrony with human gait as the task or rate of phase progression change in real-world environments. This article presents a controller for an ankle exoskeleton that uses a data-driven kinematic model to continuously estimate the phase, phase rate, stride length, and ground incline states during locomotion, which enables the real-time adaptation of torque assistance to match human torques observed in a multiactivity database of ten able-bodied subjects. We demonstrate in live experiments with a new cohort of ten able-bodied participants that the controller yields phase estimates comparable to the state of the art, while also estimating task variables with similar accuracy to recent machine learning approaches. The implemented controller successfully adapts its assistance in response to changing phase and task variables, both during controlled treadmill trials ( $N=10$ , phase root-mean-square error (RMSE): 4.8 $\pm$ 2.4%) and a real-world stress test with extremely uneven terrain ( $N=1$ , phase RMSE: 4.8 $\pm$ 2.7%).