Data-Driven Dynamic Motion Planning for Practical FES-Controlled Reaching Motions in Spinal Cord Injury.

Functional electrical stimulation (FES) is a promising technology for restoring reaching motions to individuals with upper-limb paralysis caused by a spinal cord injury (SCI). However, the limited muscle capabilities of an individual with SCI have made achieving FES-driven reaching difficult. We developed a novel trajectory optimization method that used experimentally measured muscle capability data to find feasible reaching trajectories. In a simulation based on a real-life individual with SCI, we compared our method to attempting to follow naive direct-to-target paths. We tested our trajectory planner with three control structures that are commonly used in applied FES: feedback, feedforward-feedback, and model predictive control. Overall, trajectory optimization improved the ability to reach targets and improved the accuracy for the feedforward-feedback and model predictive controllers ( ). The trajectory optimization method should be practically implemented to improve the FES-driven reaching performance.

Developing a Quasi-Static Controller for a Paralyzed Human Arm: A Simulation Study.

Individuals with paralyzed limbs due to spinal cord injuries lack the ability to perform the reaching motions necessary to every day life. Functional electrical stimulation (FES) is a promising technology for restoring reaching movements to these individuals by reanimating their paralyzed muscles. We have proposed using a quasi-static model-based control strategy to achieve reaching controlled by FES. This method uses a series of static positions to connect the starting wrist position to the goal. As a first step to implementing this controller, we have completed a simulated study using a MATLAB based dynamic model of the arm in order to determine the suitable parameters for the quasi-static controller. The selected distance between static positions in the path was 6 cm, and the amount of time between switching target positions was 1.3 s. The final controller can complete reaches of over 30 cm with a median accuracy of 6.8 cm.