Skip to main content
Dryad

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

Data files

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.