Influence of Input Hardware and Work Surface Angle on Upper Limb Posture in a Hybrid Computer Workstation
Bibliographic record
Abstract
OBJECTIVE: We evaluated the effect of work surface angle and input hardware on upper-limb posture when using a hybrid computer workstation. BACKGROUND: Offices use sit-stand and/or tablet workstations to increase worker mobility. These workstations may have negative effects on upper-limb joints by increasing time spent in non-neutral postures, but a hybrid standing workstation may improve working postures. METHOD: Fourteen participants completed office tasks in four workstation configurations: a horizontal or sloped 15° working surface with computer or tablet hardware. Three-dimensional right upper-limb postures were recorded during three tasks: reading, form filling, and writing e-mails. Amplitude probability distribution functions determined the median and range of upper-limb postures. RESULTS: The sloped-surface tablet workstation decreased wrist ulnar deviation by 5° when compared to the horizontal-surface computer when reading. When using computer input devices (keyboard and mouse), the shoulder, elbow, and wrist were closest to neutral joint postures when working on a horizontal work surface. The elbow was 23° and 15° more extended, whereas the wrist was 6° less ulnar deviated, when reading compared to typing forms or e-mails. CONCLUSION: We recommend that the horizontal-surface computer configuration be used for typing and the sloped-surface tablet configuration be used for intermittent reading tasks in this hybrid workstation. APPLICATION: Offices with mobile employees could use this workstation for alternating their upper-extremity postures; however, other aspects of the device need further investigation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".