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People with lower-limb loss, the majority of which use passive prostheses,exhibit a high incidence of falls each year. Powered lower-limb prostheses havethe potential to reduce fall rates by actively helping the user recover from astumble, but the unpredictability of the human response makes it difficult todesign controllers that ensure a successful recovery. This paper presents amethod called TRIP-RTD (Trip Recovery in Prostheses via Reachability-basedTrajectory Design) for online trajectory planning in a knee prosthesis duringand after a stumble that can accommodate a set of possible predictions of humanbehavior. Using this predicted set of human behavior, the proposed methodcomputes a parameterized reachable set of trajectories for the human-prosthesissystem. To ensure safety at run-time, TRIP-RTD selects a trajectory for theprosthesis that guarantees that all possible states of the human-prosthesissystem at touchdown arrive in the basin of attraction of the nominal behaviorof the system. In simulated stumble experiments where a nominal phase-basedcontroller was unable to help the system recover, TRIP-RTD producedtrajectories in under 101 ms that led to successful recoveries for all feasiblesolutions found.

Trip Recovery in Lower-Limb Prostheses using Reachable Sets of Predicted Human Motion