The Effect of Typing Posture on Wrist Extensor Muscle Loading
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".