Nonlinear Optimization for Personalized Path Planning for a Hybrid FES-Exoskeleton System

Abstract

Hybrid Functional Electrical Stimulation (FES)- exoskeleton systems are emerging as promising assistive technologies because they can drive users through trajectories that mimic functional movements with higher precision than FES-alone and reduced torque consumption compared to an exoskeleton alone. Limitations in FES movement accuracy and exoskeleton power and size requirements from high torque commands prevent hybrid systems as prevalent assistive devices in real-world settings. Additionally, difficulties in effectively coordinating the two subsystems and heterogeneity of motor capabilities across neurologically impaired individuals limit their widespread adoption. This letter presents a methodology for nonlinear trajectory optimization to create feasible, personalized trajectories between desired set-points that were evaluated in simulation with seven neurologically intact user models. The personalized trajectories ensure dynamic feasibility while maintaining significant exoskeleton torque reduction in the hybrid FES-exoskeleton system compared to the exoskeleton-alone. The personalized trajectories also maintain similar tracking accuracy compared to minimum jerk trajectories.