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Record W4387624544 · doi:10.2316/j.2023.201-0390

NOVEL CHAIR DESIGN TO MANAGE PRESSURE DISTRIBUTION, 1-10.

2023· article· en· W4387624544 on OpenAlexvenueno aff
Hao Wu, G.K.H. Pang

Bibliographic record

VenueMechatronic systems and control · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicOperations Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsDistribution (mathematics)EngineeringComputer scienceMathematics

Abstract

fetched live from OpenAlex

Sitting pressure distribution is closely related to sitting comfort, health, and chair ergonomics.This study describes the development of a novel chair that can monitor and adjust seat pressure with the aim of relieving seat pressure.A mat device was developed to collect pressure distribution data from the seat surface.Electric extension/retraction mechanisms were designed from the base of the seat to redistribute seat pressure.Evaluations are presented for monitoring the pressure distributions of three different seat materials when a person was in four different postures, demonstrating that the performance of the pressure sensing mats was satisfactory.In an evaluation of the efficacy of the electric extenders' mechatronics features, the retraction function is viable for decreasing surface pressure.We expect that this study will contribute to novel chair design, and successful engineering development could contribute to the prevention of pressure ulcers by adjusting seat pressure for people in prolonged sitting positions.

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)0.001
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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.070
GPT teacher head0.331
Teacher spread0.261 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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