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Record W2097818420 · doi:10.1518/0018720024497655

The Effect of Typing Posture on Wrist Extensor Muscle Loading

2002· article· en· W2097818420 on OpenAlexaff
Peter J. Keir, Richard Wells

Bibliographic record

VenueHuman Factors The Journal of the Human Factors and Ergonomics Society · 2002
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsUniversity of WaterlooYork University
Fundersnot available
KeywordsWristForearmPhysical medicine and rehabilitationElectromyographyIndex fingerWork (physics)Upper limbBiomechanicsExertionMedicinePhysical therapyAnatomyEngineering

Abstract

fetched live from OpenAlex

High static loading of the forearm extensor musculature has been observed during keying tasks. To reduce the level of loading, one must first understand the contributing factors. A simulation of the human finger was used to determine muscle force contributions during a static index finger key press at several wrist postures. The planar model included active and passive muscle forces of the intrinsic and extrinsic finger muscles. The model was expanded to include the passive forces from the other fingers as well as the weight of the hand to determine the exertion required of the wrist extensor muscles to maintain the given wrist and finger postures. Model results indicated that greater than 25% of maximal exertion is required of the wrist extensors when the wrist is extended to 30. The increased moment contribution from passive forces of the extrinsic finger flexor muscles was responsible for the majority of the increased wrist extensor contribution as the wrist was extended. These findings are in relative agreement with previous electromyographic studies and may indicate a mechanism for forearm extensor pain in office workers. Potential applications of this research include ergonomic modeling of the upper limb to determine internal loads that may lead to work-related disorders.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.272
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.

Study designObservational
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

Citations33
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueHuman Factors The Journal of the Human Factors and Ergonomics SocietySame topicErgonomics and Musculoskeletal DisordersFrench-language works237,207