Session: 20-17-01: Rising Stars of Mechanical Engineering

Paper Number: 172418

A Task-Invariant Customization Framework for Lower-Limb Exoskeletons to Assist Volitional Human Motion 

This Faculty Early Career Development (CAREER) award will support research that advances knowledge in the control and optimization of lower-limb exoskeletons in providing their human users with customized assistance across locomotor tasks. Conventional customization paradigms often aim at optimizing parameters of pre-defined torque profiles for specific locomotor tasks, which cannot accommodate the continuously varying activities humans perform every day. Prevalent approaches also place emphasis on minimizing slow-converging energy expenditures associated with human locomotion, which is important for able-bodied persons but might not be of high priority for individuals with pathological gaits. The goal of this project is to provide a paradigm shift from task-specific, slow convergent customization to task-invariant, rapid customization. This research will facilitate active learning and adaptation of lower-limb exoskeletons to drastically reduce the cost of gait rehabilitation for nearly a million Americans who sustain a new stroke every year. Key parameters throughout gait rehabilitation such as body-weight support ratio will be automatically customized based on stroke subject's training progress, which will otherwise be tuned by a team of therapists and engineers that usually lasts for hours. This research will also promote the use of exoskeletons for able-bodied users in manufacturing sites, warehouses, battlefields, and other relevant scenes by reducing the associated costs in control parameter customization. The integrated education plan will help cultivate the next-generation wearable robot researchers and motivating K-12 students to further pursue STEM degrees. The human subject studies and exhibits will promote the awareness of wearable technologies among the public, especially among traditionally underrepresented groups.

The goals of this project are to: 1) investigate invariant assistive strategies in continuously varying locomotor tasks and environments, 2) construct a complete framework for customizing exoskeleton assistance across locomotor tasks, and 3) understand how the customization framework facilitates assistance adaptations to different user's volitional motion and activities. Utilizing a two-layer optimization structure will rapidly determine task-invariant assistive strategies in the inner-loop through tracking the desired energetics or centroidal momentum of a virtual reference model, meanwhile updating its parameters in the outer-loop based on human performance-based cost functions. The proposed research will explore task-invariant assistive strategies that modulate human body energetics and centroidal momentum. The PI and his research team will develop a comprehensive human-in-the-loop customization framework to rapidly tailor these quantities and validate the effectiveness of the proposed research through human subject experiments across diverse locomotor tasks. 

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.

Presenting Author: Ge Lv Clemson University

Presenting Author Biography: Ge Lv received the B.S. degree in automation and the M.S. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2011 and 2013, respectively, and the Ph.D. degree in electrical engineering from the University of Texas at Dallas, in 2018. He is currently an Assistant Professor with the School of Mechanical and Automotive Engineering, Clemson University. Prior to joining Clemson University in Spring 2020, he was a Postdoctoral Fellow with the Robotics Institute, Carnegie Mellon University. His research focuses on the design, control, and learning-based optimization of assistive robots to enhance human mobility. He received the Best Student Paper Award from the 2015 IEEE Conference on Decision and Control and the Faculty Early Career Development Program (CAREER) Award from the National Science Foundation in 2024.

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