Robots are used more and more to support human motor function in neurorehabilitation, performance augmentation, robot-assisted manufacturing, and powered prosthetics. While improvement in technological capabilities has been impressive during the last 20 years, progress has been limited by poor fundamental understanding of the human component during human-robot-assisted motor function. This CAREER project will address this limitation by noninvasively measuring brain function and muscle force and stiffness while a subject interacts with a robot to perform wrist tasks within a magnetic resonance scanner. This framework will be used to study how neuromotor impairment affects muscle coordination, paving the way for refined neuromusculoskeletal models of robot-assisted motor function and for personalized human-robot interaction strategies for performance augmentation and neurorehabilitation. This project will provide training for graduate and undergraduate students in problems at the intersection between biomechanics, neuroscience, and robotics, helping create a multidisciplinary workforce ready to tackle the complex challenges of rehabilitation engineering research in both academia and industry. The computational model developed will form the basis of a new outreach program that introduces gaming within the context of neuromuscular modeling, which will be administered to middle and high school students to raise interest in pursuing STEM education.
The investigator’s long-term research goal is to develop novel neuroscience-grounded interventions for movement rehabilitation after neuromotor disorders. Toward this goal, the investigator’s current research is focused on 1) developing and validating robotic technologies for human assistance and rehabilitation and on 2) using such novel technologies in experimental studies modeling the healthy and impaired human motor control. This CAREER project seeks to use MRI-compatible robots to non-invasively measure brain function via functional magnetic resonance imaging (fMRI) and muscle function via magnetic resonance elastography(MRE) during motor tasks that require subjects to perform dynamic point-to-point and isometric wrist tasks in an MR scanner. Combining neuromechanical modeling with advanced experimental methods and with hypothesis-driven experiments, this framework will be used to quantify how the intrinsic neuromuscular dynamics of the human body affect physical human-interaction and to study optimal control policies for human-robot interaction in the presence of realistic neuromuscular dynamics. The project builds on the investigator’s success in developing a family of MRI-compatible robotic devices and a multi-muscle magnetic resonance elastography (MM-MRE) imaging technique for identifying fundamental criteria of muscle-coordination. The research plan is divided into two thrusts. The FIRST thrust focuses on studying the neural mechanisms underscoring force and impedance control using a multifaceted approach that combines modeling, behavioral experiments, and neuroimaging. A computational framework will be developed for analyzing force and impedance control for wrist pointing movements that includes accurate musculoskeletal dynamics. This framework will be validated in experiments conducted on healthy subjects that learn to control force or impedance in stable or unstable tasks. Brain regions involved in learning to generate proper force and impedance required for task execution will be identified. A neuroimaging experiment that uses fMRI and an MRI-compatible wrist robot will be used to test hypotheses on the neural representation of force and impedance during dynamic tasks, specifically that "force and impedance are controlled in different cortical regions" and that "the representation of muscle co-activation overlaps with the representation of the fast learning state." The SECOND thrust focuses on using MM-MRE to estimate the mechanical properties of multiple muscles in the forearm. The new technique integrates muscle-specific MRE measurements with measurements of joint torque obtained via an MRI-compatible instrumented handle. This technique will be used to identify criteria of muscle coordination that apply for tasks of the hand and wrist and to quantify how these criteria change in post-stroke individuals. Based on the unique measurements obtained, the currently accepted short-range stiffness model, not yet directly validated in-vivo and in-humans, will be tested using robotic perturbations applied by a high power MRI-compatible robot.
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.
