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Record W2147015194 · doi:10.1682/jrrd.2012.02.0031

Mechanical and biomechanical analysis of a linear piston design for angular-velocity-based orthotic control

2013· article· en· W2147015194 on OpenAlexafffund
Edward D. Lemaire, R. Samadi, Louis Goudreau, Jonathan Kofman

Bibliographic record

VenueThe Journal of Rehabilitation Research and Development · 2013
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsOttawa Hospital
FundersCanadian Institutes of Health Research
KeywordsKnee JointAngular velocityKnee flexionPhysical medicine and rehabilitationGaitAnkleTorqueOrthodonticsMedicinePhysicsAnatomySurgery

Abstract

fetched live from OpenAlex

A linear piston hydraulic angular-velocity-based control knee joint was designed for people with knee-extensor weakness to engage knee-flexion resistance when knee-flexion angular velocity reaches a preset threshold, such as during a stumble, but to otherwise allow free knee motion. During mechanical testing at the lowest angular-velocity threshold, the device engaged within 2 degrees knee flexion and resisted moment loads of over 150 Nm. The device completed 400,000 loading cycles without mechanical failure or wear that would affect function. Gait patterns of nondisabled participants were similar to normal at walking speeds that produced below-threshold knee angular velocities. Fast walking speeds, employed purposely to attain the angular-velocity threshold and cause knee-flexion resistance, reduced maximum knee flexion by approximately 25 degrees but did not lead to unsafe gait patterns in foot ground clearance during swing. In knee collapse tests, the device successfully engaged knee-flexion resistance and stopped knee flexion with peak knee moments of up to 235.6 Nm. The outcomes from this study support the potential for the linear piston hydraulic knee joint in knee and knee-ankle-foot orthoses for people with lower-limb weakness.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.380
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 head0.037
GPT teacher head0.304
Teacher spread0.267 · 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 designSimulation or modeling
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

Citations17
Published2013
Admission routes2
Has abstractyes

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