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Record W2766753125 · doi:10.1115/detc2017-67812

Design and Analysis of a Shell Mechanism Based Two-Fold Force Controlled Scoliosis Brace

2017· article· en· W2766753125 on OpenAlexfundno aff
Joep Nijssen, Giuseppe Radaelli, Just L. Herder, Charles J. Kim, J. B. Ring

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsnot available
FundersPolytechnique MontréalNational Science Foundation
KeywordsBraceKinematicsStiffnessComputer scienceStructural engineeringEllipsoidDeflection (physics)Control theory (sociology)PhysicsEngineeringClassical mechanicsArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper a first iteration of a new scoliosis brace design and correction strategy is presented using compliant shell mechanisms to create both motion and correction. The motion profile of the human spine was found using a segmented motion capture approach. The brace was designed for a case study using a conceptual ellipsoid design approach. The force controlled correction profile was re-invented using a two fold zero and positive stiffness profile. These force generators were built and validated to prove their zero stiffness characteristic. The kinematic part of the brace was detail designed with the correct order of magnitude and validated through their force-deflection characteristic. The end result was a first iteration of a new brace validated and analysed on some critical components which can form the basis for a future biomechanical study.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.234
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations9
Published2017
Admission routes1
Has abstractyes

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