A Method for Selecting Stumble Recovery Response in a Knee Exoskeleton

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 recov- ery 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.