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Design and evaluation of a modularized ankle-foot orthosis with quick release mechanism

2020· article· en· W3081812198 on OpenAlexaff
Wentao Li, Edward D. Lemaire, Natalie Baddour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsStiffnessComputer scienceMechanism (biology)SimulationEngineeringStructural engineering

Abstract

fetched live from OpenAlex

Ankle foot orthosis (AFO) stiffness affects ankle range of motion but can also provide energy storage and return to improve mobility. To perform multiple activities during the day, a person may want to change their AFO stiffness to meet their activity's demand. Carrying multiple AFOs and changing AFOs is inconvenient and could discourage users from engaging in multiple activities. This project will develop a new quick-release mechanism (QRM) that allows users to easily change posterior strut elements to change AFO stiffness. The QRM attaches to the AFO and requires no tools to operate. The proposed QRM includes a quick-release key, weight-bearing pin, receptacle anchor, and immobilization pin. A prototype was modelled with SolidWorks and simulated with SolidWorks Simulation. The QRM was designed to have no mechanical failure during intense activities such as downhill walking and jogging. Unlike a solid screw connection, the QRM needed an additional part to eliminate unsecured motion related to clearance between the quick release key and receptacle anchor. Mechanical test results and measurement data proved no deformation on each part after mechanical testing.Clinical Relevance- The quick release AFO has the potential to improve user's activities range by tuning from stiffness free mode to high stiffness mode.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.619
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.028
GPT teacher head0.226
Teacher spread0.199 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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