This project will advance the progress of customizable soft robotic exosuits for seamless assistance in everyday activities, to mitigate impairment or to augment normal functionality. Soft robotic exosuits are a new class of functional clothing that apply mechanical assistance to wearers' joints in parallel with their muscles. Millions of Americans with neurologically-based walking impairments, such as the nearly seven million people living post-stroke, could benefit from the unobtrusive assistance that exosuits can provide during walking. Similarly, healthy individuals who carry heavy loads over long distances (e.g., first responders or soldiers) could benefit from using exosuit technology to partially alleviate their burden. Synchronizing mechanical assistance to the wearer's natural rhythm is essential to realizing the potential of these devices. The soft robotic exosuits created in this project will monitor the wearer's walking pattern using body-worn sensors, apply machine learning methods to personalize the robotic assistance pattern, and continuously update that assistance pattern as the wearer's gait changes. In contrast, current methods rely on expert clinicians and technicians to manually tune assistance patterns. This project will contribute new knowledge to advance the national health and prosperity. The multidisciplinary research team includes roboticists, movement scientists, and clinicians, who will work closely with persons poststroke in laboratory and clinical environments.
Recent work developing human-in-the loop optimization strategies for exoskeletons suggests that it is possible to lower the metabolic cost of walking by treating the exosuit control as an optimization problem, with direct measurements of metabolic cost serving as the objective. However, recording such measurements require bulky devices that interfere with normal breathing, meaning the method cannot currently work in everyday environments. Additionally, lowering the metabolic cost of walking is not the only goal of exosuits in persons poststroke, whose gait is characterized by slow movement and compensations such as hip hiking, circumduction, and vaulting. Successful human-in-the-loop optimization in clinical populations will need to address all these factors, not just metabolic cost. An initial basic-science exploration will find effective proxies for metabolic cost in healthy populations and develop a multi-objective function in people poststroke. In both cases, the objective function must be unobtrusive to measure, accurate, and responsive to changes in exosuit control. A second implementation phase will use human-in-the-loop optimization to automatically adapt control parameters of portable systems assisting healthy individuals and people poststroke.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
