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Record W2132424064 · doi:10.1109/tbme.2004.834290

Development and Evaluation of a New Body-Seat Interface Shape Measurement System

2004· article· en· W2132424064 on OpenAlexaff
Yue Li, Rachid Aïssaoui, Marine Lacoste, J. Dansereau

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2004
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsÉcole de Technologie SupérieurePolytechnique MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsInterface (matter)Computer scienceEngineeringHuman–computer interaction

Abstract

fetched live from OpenAlex

A new system has been developed to capture the body-seat interface shape. It can repeatedly and accurately measure interface deformation. The shape sensing array system uses optical fiber technology and is noninvasive. The system can cover an interface as large as 400 x 480 mm and the shape is measured over a 10 x 12 array of sensors laminated on ribbon substrates. The accuracy and repeatability of this system were assessed. Measurement errors were evaluated by comparing the shape with a reference shape obtained by a mechanical digitizer. The root-mean-square error in the Z direction for the system was 3.79 mm. The repeatability of the system was within 0.38 mm under controlled conditions. Different interface materials noticeably affected measurements. With the development of this interface shape measurement device, the basic information gathered through its use may prove to be fundamental in the successful design of generic-shape contoured support surfaces. Furthermore, we expect that the new shape measurement device will provide a quick and effective tool for cushion evaluation and clinical guidelines for cushion prescription.

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: none
Teacher disagreement score0.797
Threshold uncertainty score0.478

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 head0.037
GPT teacher head0.286
Teacher spread0.249 · 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

Citations11
Published2004
Admission routes1
Has abstractyes

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