CRCNS Research Proposal: Collaborative Research: Data-driven approaches for restoring naturalistic motor functions using functional neural stimulation

Oregon State University, Corvallis OR

Investigators

Abstract

Individuals with lower limb paralysis retain the ability to plan and initiate gait within the central nervous system but, due to conditions such as spinal cord injury or stroke, are unable to transmit those directives to the muscles of the lower limb. This research project uses a biologically-inspired, data-driven approach to address this deficiency. Specifically, the research team is developing and evaluating machine learning methods and electrical stimulation of nerves to control movements of the lower limbs. The goal is to develop and evaluate methods to restore natural, coordinated, and graceful gait in an animal model of paralysis. This activity is a first step toward providing benefit to the paralyzed community by creating pathways toward the development and commercialization of functional gait restoration systems that evoke more natural, controlled movement of paralyzed limbs. In addition, the project offers unique opportunities to train engineering students in the performance of pre-clinical studies, placing these future researchers at the forefront of engineering technology and medical research. This research project aims to develop and evaluate methods to restore natural, coordinated, and graceful gait in an animal model of paralysis using a synergistic collaboration of a multi-disciplinary and multi-university team of investigators and a combination of innovative modeling and algorithm development supported by a series of experiments. Specifically, the project aims to achieve the following sub-goals involving application of data-driven algorithms in a series of experiments that are designed to evoke increasingly complex movements over the course of the proposed work: (a) Develop, characterize, and evaluate advanced controllers of joint angle and joint torque production of a single joint in only a single direction, to allow comparison of the data-driven model to earlier, classical controls methods; (b) Develop, characterize, and evaluate advanced controllers of joint angle and joint torque production of a single joint in both directions, to elucidate methods used by the advanced controller's solution to the under-constrained problem of agonist-antagonist muscle pair control; (c) Develop, characterize, and evaluate advanced controllers of joint angle and joint torque production of multiple joints in both directions, to elucidate methods used by the advanced controller's solution to the competing-goals problem of biarticular muscle control; and, (d) Recreate natural, coordinated, and graceful gait by use of the advanced controllers arising from the first three goals, and demonstrate this result on a treadmill platform.