CAREER: Leveraging Human Neuromotor Learning to Enhance Robotic Exoskeleton Performance

About This Grant

This Faculty Early Career Development (CAREER) award supports research seeking to advance our fundamental understanding of how people learn to walk more efficiently with wearable robotic exoskeletons. Wearable robots have the potential to enhance mobility for a wide range of users, including individuals with neurological or musculoskeletal impairments, older adults, and able-bodied individuals. Unlike traditional walking aids, robotic exoskeletons can adapt their mechanical assistance to meet each user's unique needs and adjust over time to optimize gait efficiency. However, current personalization approaches focus primarily on adapting the robot without fully considering the human user's natural ability to learn and adapt. This research project attempts to address this critical gap by investigating novel ways to guide and leverage human neuromotor learning to further enhance the performance of gait-assistive exoskeletons. This award will also support training the next generation of researchers to develop robotic systems that enhance human locomotion through three key activities: (1) hosting a regional locomotion research symposium for students, (2) engaging students in hands-on human-robot physical interaction projects, and (3) raising public awareness of the potential of robotic technology to improve mobility. The overall goal of this CAREER award is to develop gait-assistive robotic exoskeletons that not only adapt their mechanical assistance to the user but also actively "coach" the user to work with the device to walk more efficiently. By influencing both human adaptation and its own behavior, an exoskeleton can optimize cooperative learning between itself and the user, leading to faster and greater improvements in walking efficiency for diverse users. To achieve this goal, the PI's team will conduct a series of foundational experiments to attempt to: (1) develop new metrics to assess the user's proficiency in operating gait-assistive exoskeletons; (2) determine how the characteristics of exoskeleton assistance adaptation algorithms influence user learning; and (3) identify effective multimodal interaction methods—such as auditory and/or vibrotactile cues—to guide and accelerate (i.e., "coach") user learning. The results of these experiments intend to ultimately inform development of novel adaptive exoskeleton control algorithms that incorporate multimodal "coaching" signals to enhance human-robot cooperative learning and maximize gait efficiency. 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.