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Task-agnostic exoskeleton control via biological joint moment estimation
Abstract
Lower-limb exoskeletons have the potential to transform the way we move 1-14, 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 R 2 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.
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Metadata
- Authors
- Molinaro, D. D., Scherpereel, K. L., Schonhaut, E. B., Evangelopoulos, G., Shepherd, M. K., & Young, A. J.
- Affiliations
- Georgia Institute of Technology (US)
- Google (United States) (US)
- Northeastern University (US)
- Corresponding author
- Molinaro, Dean D.
- ORCID
- Venue
- Nature
- Published
- 2024-11-13
- Volume
- 635
- Issue
- 8038
- Pages
- 337-344
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4404305449
- Cited by
- 107
- References
- 67
- Citations by year
- 2026: 55 · 2025: 51 · 2024: 1
- FWCI
- 11.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
- Keywords
- lower limb exoskeleton, deep neural network, metabolic cost reduction, gait assistance, impedance control
- MeSH terms
- Exoskeleton Device, Deep Learning, Adult, Biomechanical Phenomena, Energy Metabolism, Female, Hip Joint, Humans, Knee Joint, Locomotion, Male, Running, Walking, Weight-Bearing, Neural Networks, Computer, Torque, Lower Extremity, Young Adult
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