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Record W1994918768 · doi:10.1682/jrrd.2011.06.0107

Design and development of ankle-foot prosthesis with delayed release of plantarflexion

2013· article· en· W1994918768 on OpenAlexaff
Michael Mitchell, Katelynn Craig, Peter Kyberd, E. Biden, Greg Bush

Bibliographic record

VenueThe Journal of Rehabilitation Research and Development · 2013
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsAnkleCadenceGaitProsthesisPhysical medicine and rehabilitationKinematicsGait analysisFoot (prosody)Ground reaction forceMedicineSurgeryPhysics

Abstract

fetched live from OpenAlex

A computer-controlled mechanism that fits a standard ankle-foot prosthesis was designed to capture the absorbed energy in the ankle and delay its release until specific times in the gait cycle. This mechanism used a direct current motor to take up and hold the compression of a carbon-fiber ankle joint. Based on the timing of the contact forces between the foot and the ground, a microprocessor released the spring at preset times later in the gait cycle. This mechanism was added to a Talux prosthetic foot and was employed by a user of a conventional energy-storage ankle-foot prosthesis. His gait was recorded using a motion analysis system. Five settings: 0, 55, 65, 75, and 85 ms delay were tested on separate days, and the standard kinematic and kinetic gait data were recorded. The user reported some settings were more comfortable than others. When these preferences were tested with a randomized double-blind trial, the preferences were not consistent. A second user showed a preference for the 55 ms delay. The modifications to the device resulted in changes to the gait of the subjects, including increased cadence and kinematics of the unaffected joints and a longer, slower push from the ankle, which was noticed by both of the subjects.

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.

How this classification was reachedexpand

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.551
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.029
GPT teacher head0.265
Teacher spread0.236 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2013
Admission routes1
Has abstractyes

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