Analysis of sliding and pressure distribution during a repositioning of persons in a simulator chair
Bibliographic record
Abstract
This study was undertaken to investigate the effect of system tilt and back recline angles on sliding and pressure distribution of seated subjects. Ten able-bodied subjects adopted successively 12 postures on a multiadjustable simulator chair. The system tilt angle was varied from 0 degrees to 45 degrees posterior tilt, while the seat to back angle varied from 90 degrees to 120 degrees. A maximum of 40.2% of weight shift was found when combining a system tilt angle of 45 degrees to a seat to back angle of 120 degrees. Maximum value of 74 mm of sliding was observed for the acromion marker during repositioning. Significant weight shift at the level of the seat is obtained only when the system tilt angle exceeds 15 degrees in a posterior direction. We can put forward here that a small tilt < or =15 degrees can be used to adjust back pressure distribution, whereas large posterior tilts are used for an effective weight shift at the seat level. The peak pressure gradient remains in general in the interval of +/-30% from the neutral posture for the able-bodied subjects and is fairly constant at 15 degrees of tilt. A significant amount of displacement along the back and seat reference plane were found for the shoulder and hip markers, but this displacement does not necessarily correspond to a pure translation motion of the pelvic segment.
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 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) | 0.000 | 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) | 0.001 | 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".