Assistive robotic devices such as exoskeletons can be used to enhance human capabilities, help with physically-intense labor, and improve rehabilitation. User-perceived comfort is critically important for the wide-spread adoption of assistive robotic devices, yet the definition and measurement of comfort remains elusive. The research objective of this project is to systematically define, model, and optimize the comfort perceived by human users while walking with powered leg exoskeletons. The project pursues three objectives: The first develops a model of comfort based on biosignals recorded during walking with lower limb exoskeleton robots. The second optimizes parameters of exoskeleton control based on verbalized reports of user comfort. The third optimizes exoskeleton control for comfort without direct user reporting. If successful, the project will lead to a new generation of exoskeleton devices that are more compatible with the humans they are designed to serve. This would benefit a large population of people with gait impairments, thereby advancing the national health and welfare. Broader impacts of this work include novel, hands-on outreach activities serving underrepresented minorities in central Pennsylvania and in Dallas, Texas.
This project takes significant steps towards the development of robotic controllers that adapt continuously to each user and minimize user discomfort. This will be achieved by using neural network models to analyze and model a selected set of the users' biological signals (e.g., metabolic cost, heart rate, muscle activation, kinematics, and kinetics) to develop a novel comfort predictor, and then by creating intelligent controllers that maximize the user comfort via human-in-the-loop reinforcement learning. Human subject experiments are planned using two devices (a knee and hip device, and an ankle device) to verify that the comfort predictor can be used effectively with the optimization method. If successful, the project could benefit a large population of people with gait impairments and those requiring robotic assistance with physically-intense labor.
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.
|Effective start/end date
|1/1/20 → 12/31/23
- National Science Foundation: $417,682.00