The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Personalizing exoskeleton assistance while walking in the real world
Open accessAbstract
Personalized exoskeleton assistance provides users with the largest improvements in walking speed 1 and energy economy 2–4 but requires lengthy tests under unnatural laboratory conditions. Here we show that exoskeleton optimization can be performed rapidly and under real-world conditions. We designed a portable ankle exoskeleton based on insights from tests with a versatile laboratory testbed. We developed a data-driven method for optimizing exoskeleton assistance outdoors using wearable sensors and found that it was equally effective as laboratory methods, but identified optimal parameters four times faster. We performed real-world optimization using data collected during many short bouts of walking at varying speeds. Assistance optimized during one hour of naturalistic walking in a public setting increased self-selected speed by 9 ± 4% and reduced the energy used to travel a given distance by 17 ± 5% compared with normal shoes. This assistance reduced metabolic energy consumption by 23 ± 8% when participants walked on a treadmill at a standard speed of 1.5 m s−1. Human movements encode information that can be used to personalize assistive devices and enhance performance.
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Cites
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Self-citationHuman-in-the-loop optimization of exoskeleton assistance during walking
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Self-citationVariable impedance actuators: A review
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Self-citationTranslating Fatigue to Human Performance
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Artificial intelligence-powered electronic skin
Xu, C., Solomon, S. A., & Gao, W. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00760-z · Cited by 259
Artificial intelligence meets medical robotics
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Task-agnostic exoskeleton control via biological joint moment estimation
Molinaro, D. D., Scherpereel, K. L., Schonhaut, E. B., Evangelopoulos, G., Shepherd, M. K., & Young, A. J. (2024) · Nature · doi:10.1038/s41586-024-08157-7 · Cited by 107
A versatile knee exoskeleton mitigates quadriceps fatigue in lifting, lowering, and carrying tasks
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Deep Learning Approaches for Enhanced Lower-Limb Exoskeleton Control: A Review
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Metadata
- Authors
- Slade, P., Kochenderfer, M. J., Delp, S. L., & Collins, S. H.
- Venue
- Nature
- Published
- 2022-10-12
- Volume
- 610
- Issue
- 7931
- Pages
- 277-282
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4304777328
- Cited by
- 328
- References
- 63
- FWCI
- 17.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
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