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

Divekar, Nikhil1; Thomas, Gray2; Yerva, Avani1; Frame, Hannah3; Gregg, Robert1

Published Sep 04, 2024 on Dryad. https://doi.org/10.5061/dryad.z34tmpgks

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Sep 04, 2024 version files 912.35 KB

Abstract

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.