Upper Limb Movement Assistance Through Model-Based Path Planning and Control of Hybrid FES-Exoskeleton Systems.

Hybrid FES-exoskeleton systems can drive users through trajectories that mimic functional upper limb movements with higher accuracy and reduced motor torque compared to either system alone, situating them well as assistive devices. Limitations in FES-driven movement accuracy, exoskeleton size, and electrical energy requirements prevent the widespread adoption of hybrid systems as assistive devices in real-world settings. Additionally, these systems are limited in their adoption due to difficulties in efficiently coordinating the two subsystems, and given the heterogeneity of motor capabilities across neurologically-impaired individuals. We present a methodology for nonlinear trajectory optimization to create feasible, personalized trajectories between desired set-points that preemptively considers each user's dynamic constraints. We evaluate personalized and minimum jerk trajectories experimentally with eight neurologically intact participants and in a case study with one individual with a cervical spinal cord injury, extending our prior simulation-based validation. The personalized trajectories maintain significant exoskeleton torque reduction from the exoskeleton-alone system to the hybrid FES-exoskeleton system without sacrificing functional tracking accuracy.