The focus of this work is a proposed novel artificial intelligence (AI) system to self-adapt control policy in powered exoskeletons to aid deployment systems that personalize to individual patient gait. The investigators hypothesize that walking speed will improve with the use of the self-adaptive control policy system. Individuals post stroke have a broad range of mobility challenges including asymmetric gait, substantially decreased SSWS, and reduced stability, and therefore have greatly impaired overall mobility independence in the community. The investigators expect the proposed novel controller, capable of personalization to such variable and asymmetric gait patterns, will have significant benefits towards increasing community independence and mobility for patients post stroke. 12 patients post stroke will be fit with the hip exoskeleton and proceed to walk at varying speeds and inclines on a Motek CAREN system while the investigators measure self-selected walking speed. The self-adaptive system will be compared directly to a best-on-average control policy (non-adaptive) as well as to a 'standard of care' baseline. This baseline will be walking with no exoskeleton plus any clinically prescribed passive orthoses. The investigators expect the self-adaptive system to learn the control policy that best matches the patient's gait patterns, thus yielding advantages in walking speed.
This work will focus on new algorithms for robotic exoskeletons and testing these in human subject tests. Individuals who have previously had a stroke will walk while wearing a robotic exoskeleton on a specialized treadmill. The study will compare the performance of the advanced algorithm with not using the device to determine the clinical benefit.