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Record W4310461599 · doi:10.1177/20556683221139613

Surrogate lower limb design for ankle-foot orthosis mechanical evaluation

2022· article· en· W4310461599 on OpenAlexafffund
Alexis Thibodeau, Patrick Dumond, Joong-Ho Kim, Edward D. Lemaire

Bibliographic record

VenueJournal of Rehabilitation and Assistive Technologies Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsForefootAnkleComputer scienceOrthodonticsMaterials scienceStructural engineeringMedicineEngineeringSurgery

Abstract

fetched live from OpenAlex

Purpose: This study designs and provides a pilot evaluation of a novel surrogate lower limb (SLL) that provides anatomically realistic three-dimensional (3D) foot motion, based on a literature consensus of passive lower limb motion. This SLL is intended to replace single axis surrogates currently used in mechanical testing of ankle-foot orthoses (AFO). Material and Methods: The SLL design is inspired by the Rizzoli foot model, with shank, hindfoot, midfoot, forefoot, and toe sections. Ball and socket joints were used between hindfoot-midfoot (HM)-forefoot sections. Forefoot-toes used a hinge joint. Three-dimensional printed nylon, thermoplastic polyurethane (TPU) and polylactic acid (PLA), as well as casted silicone rubber were used to re-create foot components. After fabrication, motion capture was performed to measure rotation using fiducial markers. The SLL was then loaded under both static and cyclic loads representing a 100 kg person walking for 500,000 cycles. Results: Most joints were within 5° of target angles. The SLL survived static loads representing 1.5 times body weight for both static and cyclical loading. Conclusions: This SLL moved as designed and survived testing loads, warranting further investigation towards enabling essential mechanical testing for AFO currently on the market, and helping to guide device prescription.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.516
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.022
GPT teacher head0.245
Teacher spread0.222 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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