Using Person-Specific Muscle Fatigue Characteristics to Optimally Allocate Control in a Hybrid Exoskeleton -...

Area of Science:

• Rehabilitation Engineering
• Robotics
• Neuroscience

Background:

• Current controllers for simultaneously modulating functional electrical stimulation (FES) and powered exoskeletons during sit-to-stand (STS) movements are limited.
• Developing optimal real-time control for complex, nonlinear STS movements with multiple degrees of freedom presents significant challenges.

Purpose of the Study:

• To design an optimal shared control strategy for FES and a powered exoskeleton during STS movements using a hybrid exoskeleton.
• To develop a hierarchical control design that addresses the nonlinearities and uncertainties in the STS control model.

Main Methods:

• A hierarchical control design featuring a higher-level robust nonlinear controller for tracking STS movement profiles.
• A lower-level optimal control allocator that distributes control between FES and exoskeleton knee motors, considering muscle fatigue and recovery dynamics.
• Experimental validation with human participants, including individuals without disability and one with spinal cord injury (SCI).

Main Results:

• The proposed hierarchical control effectively tracks desired STS movement profiles.
• The optimal control allocator successfully distributes assistance between FES and the exoskeleton based on muscle fatigue.
• Experimental results demonstrate the feasibility of shared control in a hybrid exoskeleton for STS movements.

Conclusions:

• The developed hierarchical control design is a promising approach for achieving shared control in hybrid exoskeletons.
• This technology has the potential to improve mobility and independence for individuals with SCI.
• The system's ability to adapt to muscle fatigue enhances its applicability in real-world rehabilitation scenarios.