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Record W2237736831 · doi:10.5539/mas.v10n1p76

Ergonomic Knob Design Validation for Improved Musculoskeletal Comfort

2015· article· en· W2237736831 on OpenAlexvenueno aff
Poh Kiat Ng, Yue Hang Tan, Kian Siong Jee, Li Wah Thong, Jian Ai Yeow, Chiew Yean Ng

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldEngineering
TopicErgonomics and Human Factors
Canadian institutionsnot available
Fundersnot available
KeywordsReplicaComputer sciencePinchSAFERMechanical engineeringSimulationEngineering

Abstract

fetched live from OpenAlex

Ergonomic control devices can minimise risks of hand-related injuries. The pinch force exerted on a control knob during operations can be minimised so that excessive physical stress and strain on the hands are reduced. This improves working performance. Studies indicate that milling machine control knobs are difficult to operate. Hence, this study aims to validate an ergonomic knob design against a milling machine knob design to determine the extent of musculoskeletal comfort improvement. An ergonomic knob is designed based on a knurled spherical knob with ergonomics features. A validation is performed by requesting 12 participants to turn the knobs in clockwise and counterclockwise directions. Pinch force data is recorded. Findings show that the ergonomic knob reduces more than 55 % of pinch force compared to the milling machine knob replica. This study is useful for machine designers in the development of safer and more ergonomic knobs for various equipment, apparatus and devices.

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)0.003
metaresearch head score (Gemma)0.008
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.038
GPT teacher head0.247
Teacher spread0.209 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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