Visible to the public CPS: Large: Cybernetic Interfaces for the Restoration of Human Movement through Functional Electrical Stimulation

Project Details
Lead PI:Eric Perreault
Co-PI(s):Kevin Lynch
Matthew Tresch
Konrad Kording
Lee Miller
Performance Period:10/01/09 - 09/30/15
Institution(s):Rehabilitation Institute of Chicago
Sponsor(s):National Science Foundation
Award Number:0932263
1953 Reads. Placed 55 out of 803 NSF CPS Projects based on total reads on all related artifacts.
Abstract: The objective of this research is to develop an intuitive user interface for functional electrical stimulation (FES), which uses surgically-implanted electrodes to stimulate muscles in spinal cord-injured (SCI) patients. The challenge is to enable high-level tetraplegic patients to regain the use of their own arm. The approach is to develop a multi-modal Bayesian user-intent decoder; use natural muscle synergies to generate appropriate low-dimensional muscle activation signals in a feedforward controller; develop a feedback controller to enhance the performance of the feedforward controller; and test the system with SCI patients on daily living tasks, such as reaching, grasping, and eating. The challenge problem of restoring arm use to SCI patients will lead to new design principles for cyber-physical systems interfacing neural and biological systems with engineered computation and electrical power systems. The tight integration of the proposed user interface and controller with the users own control system requires a deep understanding of biological design principles such as nested feedback loops at different time and length scales, noisy signals, parallel processing, and highly coupled neuromechanical systems. This work will lead to new technology that dramatically improves the lives of spinal cord-injured patients. These patients often have no cognitive impairment and have long life spans after injury. The goal is to enable these patients to eat, reach, and grasp nearby objects. These tasks are critical for independent living and quality of life. This work will also help train a new generation of students in human-machine interfaces at the undergraduate, graduate, and postdoctoral levels.