Task-agnostic exoskeleton control via biological joint moment estimation

Dean D. Molinaro (), Keaton L. Scherpereel, Ethan B. Schonhaut, Georgios Evangelopoulos, Max K. Shepherd and Aaron J. Young
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Dean D. Molinaro: Georgia Institute of Technology
Keaton L. Scherpereel: Georgia Institute of Technology
Ethan B. Schonhaut: Georgia Institute of Technology
Georgios Evangelopoulos: X, The Moonshot Factory
Max K. Shepherd: Northeastern University
Aaron J. Young: Georgia Institute of Technology

Nature, 2024, vol. 635, issue 8038, 337-344

Abstract: Abstract Lower-limb exoskeletons have the potential to transform the way we move1–14, but current state-of-the-art controllers cannot accommodate the rich set of possible human behaviours that range from cyclic and predictable to transitory and unstructured. We introduce a task-agnostic controller that assists the user on the basis of instantaneous estimates of lower-limb biological joint moments from a deep neural network. By estimating both hip and knee moments in-the-loop, our approach provided multi-joint, coordinated assistance through our autonomous, clothing-integrated exoskeleton. When deployed during 28 activities, spanning cyclic locomotion to unstructured tasks (for example, passive meandering and high-speed lateral cutting), the network accurately estimated hip and knee moments with an average R2 of 0.83 relative to ground truth. Further, our approach significantly outperformed a best-case task classifier-based method constructed from splines and impedance parameters. When tested on ten activities (including level walking, running, lifting a 25 lb (roughly 11 kg) weight and lunging), our controller significantly reduced user energetics (metabolic cost or lower-limb biological joint work depending on the task) relative to the zero torque condition, ranging from 5.3 to 19.7%, without any manual controller modifications among activities. Thus, this task-agnostic controller can enable exoskeletons to aid users across a broad spectrum of human activities, a necessity for real-world viability.

Date: 2024
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DOI: 10.1038/s41586-024-08157-7

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