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Segment-embedded frame definition affects the hip joint centre precision during walking

2013· article· en· W2131566731 on OpenAlexafffund
Andy Roosen, Matthew T.G. Pain, A. Thouzé, Tony Monnet, Mickaël Begon

Bibliographic record

VenueMedical Engineering & Physics · 2013
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsKinematicsGaitJoint (building)PelvisBiomechanicsComputer scienceGait analysisThighMathematicsPhysical medicine and rehabilitationMedicineAnatomyPhysicsEngineeringStructural engineering

Abstract

fetched live from OpenAlex

Due to marker-specific soft tissue artefacts, the choice of the markers defining the segment-embedded frame affects the functional joint centre location, with subsequent error propagation to joint kinematics and kinetics in gait analysis. Our aim was to assess the effect of the number and placement of markers on the precision of the hip joint centre (HJC) location during walking. Twelve markers (2x6) were attached to the pelvis and left thigh of 15 young male subjects. Set-up movements were collected to locate an optimised functional HJC. For all permutations of three from six markers, a HJC was located and subsequently reconstructed in a static trial and during walking. Precision measures with two different definitions of the origin, namely a single maker or their mean-point, and using three, four, five and six were calculated. Finally, marker triads that reduced the variability of the HJC location were determined. Both the number of markers and method for defining the origin significantly affected the HJC precision during static and walking trials. For walking, precision of 39 mm using three markers improved to 5mm using redundant markers and the mean marker position as the segment origin. Markers placed close to the joint gave more consistent results.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.765
Threshold uncertainty score0.536

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 head0.010
GPT teacher head0.217
Teacher spread0.207 · 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 designOther design
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

Citations7
Published2013
Admission routes2
Has abstractyes

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