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Record W3164219617 · doi:10.1126/science.aba9947

Removing energy with an exoskeleton reduces the metabolic cost of walking

2021· article· en· W3164219617 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScience · 2021
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsExoskeletonEnergy (signal processing)Energy consumptionSwingComputer scienceGaitElectric potential energyGait cycleMechanical energySimulationPhysical medicine and rehabilitationEngineeringMedicineElectrical engineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Evolutionary pressures have led humans to walk in a highly efficient manner that conserves energy, making it difficult for exoskeletons to reduce the metabolic cost of walking. Despite the challenge, some exoskeletons have managed to lessen the metabolic expenditure of walking, either by adding or storing and returning energy. We show that the use of an exoskeleton that strategically removes kinetic energy during the swing period of the gait cycle reduces the metabolic cost of walking by 2.5 ± 0.8% for healthy male users while converting the removed energy into 0.25 ± 0.02 watts of electrical power. By comparing two loading profiles, we demonstrate that the timing and magnitude of energy removal are vital for successful metabolic cost reduction.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.108

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.222
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it