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Estimating human joint moments unifies exoskeleton control and reduces user effort

Molinaro, Dean1

Published Mar 21, 2024 on Dryad. https://doi.org/10.5061/dryad.8kprr4xsv

Data files

Mar 21, 2024 version files 2.38 GB

Abstract

Robotic lower-limb exoskeletons can augment human mobility, but current systems require extensive, context-specific considerations, limiting their real-world viability. Here, we present a unified exoskeleton control framework that autonomously adapts assistance based on instantaneous user joint moment estimates from a temporal convolutional network (TCN). When deployed on our hip exoskeleton, the TCN achieved an average RMSE of 0.142 ± 0.021 Nm/kg and R2 of 0.840 ± 0.045 across 35 ambulatory conditions without any subject-specific calibration. Further, the unified controller significantly reduced user metabolic cost and lower-limb positive work during level ground and incline walking compared to walking without wearing the exoskeleton (P < 0.05). This advancement bridges the gap between in-lab exoskeleton technology and real-world human ambulation, making exoskeleton control technology viable for a broad community.