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Record W2120328582 · doi:10.1109/tnsre.2003.810433

Estimation of the 3-D center of mass excursion from force-plate data during standing

2003· article· en· W2120328582 on OpenAlexaff
Franck Barbier, Paul Allard, Kevin Guelton, Briac Colobert, Anne-Pascale Godillon-Maquinghen

Bibliographic record

VenueIEEE Transactions on Neural Systems and Rehabilitation Engineering · 2003
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversité de MontréalPolytechnique Montréal
Fundersnot available
KeywordsExcursionCenter of mass (relativistic)GeodesyTrajectoryQUIETPosition (finance)Vertical displacementDisplacement (psychology)Force platformRoot mean squareCenter of pressure (fluid mechanics)MathematicsSquare (algebra)GeometryGeologyPhysicsMechanicsClassical mechanics

Abstract

fetched live from OpenAlex

Biomechanical models are used with force-plate information to determine the center of mass (COM) trajectory during standing. They are usually based on simplifying assumptions and are often limited to a single-plane analysis. The objectives of this study were to present a three-dimensional (3-D) model to calculate the excursion of the COM of the human body, validate it and compare its performance to a video-based system during quiet standing and antero-posterior (AP) and medio-lateral (ML) self-imposed oscillations. In addition to the vertical displacements of the COM, the originality of the method lies in eliminating the accelerations terms in the model and their related assumptions. The model was able to estimate closely the COM displacements in quiet standing [a root mean square (RMS) of 0.9 mm or less]. For the self-imposed oscillations, the RMS differences were 6.6 mm in the AP and ML directions and 1.6 mm along the vertical axis. For all three testing conditions, the coefficients of correlation of the COM displacements between the model and the video methods were above 0.8 with the exception of the vertical direction, where the values were more variable.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.331

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 head0.020
GPT teacher head0.292
Teacher spread0.272 · 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

Citations35
Published2003
Admission routes1
Has abstractyes

Explore more

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