MétaCan
Menu
Back to cohort
Record W4293199906 · doi:10.17077/dhm.31774

Evaluation of personalized human body buttock-thigh finite element models in terms of soft tissue deformation for seat comfort assessment

2022· article· en· W4293199906 on OpenAlexaff
Goutham Sridhar, Léo Savonnet, Yoann Lafon, Xuguang Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsIschial tuberosityFinite element methodDeformation (meteorology)Soft tissueThighBiomechanicsInterface (matter)Biomedical engineeringMaterials scienceStructural engineeringEngineeringMedicineComposite materialAnatomySurgeryContact angle

Abstract

fetched live from OpenAlex

Finite element models (FEM) of human body models (HBM) are used to analyze static seating discomfort mainly in terms of interface pressure distribution on the seat surface. However, most of the HBMs are not validated under actual seating conditions due to the difficulty of measuring internal body loads such as soft tissue deformation, intervertebral disc pressures, etc. The rare HBM-related studies claiming validation have only analyzed the interface pressure distribution. Recent experiments conducted with and without foam for different seat pan inclinations using open MRI indicate that soft tissue deformation below the ischial tuberosity (IT) is affected by both contact pressure and shear and thus could be an objective indicator in seat discomfort assessment. The aim of this present study is to report a preliminary evaluation of FE-HBMs against these subject-specific experimental data in terms of interface pressure and soft tissue deformation.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.626
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0020.000

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 head0.055
GPT teacher head0.393
Teacher spread0.338 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same topicErgonomics and Musculoskeletal DisordersFrench-language works237,207