A versatile knee exoskeleton mitigates quadriceps fatigue in lifting, lowering, and carrying tasks

The quadriceps are particularly susceptible to fatigue during repetitive
lifting-lowering and carrying (LLC), affecting worker performance,
posture, and ultimately lower-back injury risk. Although robotic
exoskeletons have been developed and optimized for specific use cases like
lifting-lowering, their controllers lack the versatility or
customizability to target critical muscles across many fatiguing tasks.
Here we present a task-adaptive knee exoskeleton controller that
automatically modulates virtual springs, dampers, and gravity and inertia
compensation to assist squatting, level walking, and ramp and stairs
ascent/descent. Unlike end-to-end neural networks, the controller is
composed of predictable, bounded components with interpretable parameters
that are amenable to both data-driven optimization for biomimetic
assistance and subsequent application-specific tuning, for example,
maximizing quadriceps assistance over multi-terrain LLC. When deployed on
a backdrivable knee exoskeleton, the assistance torques holistically
reduced quadriceps effort across multi-terrain LLC tasks (significantly,
except for level walking) in 10 human users without user-specific
calibration. The exoskeleton also significantly improved fatigue-induced
deficits in time-based performance and posture during repetitive
lifting-lowering. Finally, the system facilitated seamless task
transitions and garnered high effectiveness ratings post-fatigue over a
multi-terrain circuit. These findings indicate this versatile control
framework can target critical muscles across multiple tasks, specifically
mitigating quadriceps fatigue and its deleterious effects.