Investigating Glenohumeral Joint Contact Forces and Kinematics in Different Keyboard and Monitor Setups using Opensim
Bibliographic record
Abstract
Background: The musculoskeletal complaints of the shoulder are prevalent in people who work with computers for a long time.Objective: This study aimed to investigate the glenohumeral joint contact forces and kinematics in different keyboards and monitor setups using OpenSim.Material and Methods: Twelve randomly selected healthy males participated in an experimental study. A 3×3 factorial design was used in which three angles were considered for the monitor and three horizontal distances for the keyboard while performing standard tasks. The workstation was adjusted based on ANSI/HFES-100-2007 standard to maintain a comfortable ergonomic posture for controlling confounding variables. Qualisys motion capture system and OpenSim were used.Results: The maximum mean range of motion (ROM) of both shoulders’ flexion and adduction was observed when the keyboard was 15 cm from the edge of the desk, and the monitor angle was 30°. The maximum mean ROM of both shoulders’ internal rotation was recorded for the keyboard at the edge of the desk. Peak forces for most right shoulder complex muscles were obtained in two setups. 3D shoulder joint moments were significantly different among nine setups (P-value<0.05). The peak anteroposterior and mediolateral joint contact forces were recorded for the keyboard at 15 cm and the monitor at zero angles (0.751 and 0.780 N/BW, respectively). The peak vertical joint contact force was observed for the keyboard at 15 cm and the monitor at 15° (0.310 N/BW). Conclusion: The glenohumeral joint contact forces are minimum for the keyboard at 8 cm and the monitor at zero angles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".