Powered lower-limb exoskeletons have been shown to assist and augment walking, but most such devices do not currently have the ability to explicitly accommodate a stumble perturbation. A major challenge in doing so is identifying a stumble event and selecting in real-time which recovery strategy (elevating or lowering) to employ, particularly since the exoskeleton should ideally select the same strategy selected by the user. In order to do so, the authors conducted experiments involving five young, healthy adults wearing a knee exoskeleton. Each participant underwent a stumble experiment in order to collect an exoskeleton sensor dataset of stumbles throughout swing phase, which was used for stumble detection and recovery strategy identification algorithm development and testing. Overall, the proposed detection and identification algorithms provide improved accuracy with fewer required sensors relative to previous works, and were tested on the largest exoskeleton sensor stumble dataset to date, showing the feasibility of such algorithms for real-time implementation, which is an essential first step in developing lower-limb assistive devices that are robust to stumbles.

This paper presents a stumble recovery controller for a knee exoskeleton that detects a stumble perturbation; selects an anticipated recovery strategy; and provides appropriate recovery assistance. In order to assess the efficacy of the controller in providing an assistive response to a stumble perturbation, the controller was implemented in a knee exoskeleton and evaluated in a single healthy adult participant against several other controller reactions, and against the participant’s response without an exoskeleton. Results show that the stumble recovery controller successfully detected the perturbation and correctly selected the strategy that matched the participant’s response for all 29 trials in which the exoskeleton was used. Further, results show improvements in stumble recovery metrics when using the exoskeleton with the stumble recovery controller, compared to the control cases of: 1) no change in the nominal controller when stumble is detected; 2) turning off exoskeleton torque when a stumble is detected; and 3) not wearing an exoskeleton.