Comparison of sitting positions on a pressure sensing mat over time
Bibliographic record
Abstract
Abstract These days, many jobs like working in an office include sitting in chairs for a long time which could lead to work-related disorders such as musculoskeletal issues. Inappropriate postures can cause muscle fatigue in certain regions, resulting in pain and discomfort. Analysis of different types of postures to indicate discomfort could help us choose an optimal posture. This study evaluates five different sitting postures in an office chair for comfort and discomfort. Each posture was held for 18 minutes with a two minute break between postures. Six participants with an equal number of male and female subjects were chosen. The sitting posture correlates with the distribution of the weight on the seat, which can be measured by pressure sensors. The pressure distribution was obtained using a custom-built pressure mat and the maximum pressure were evaluated. The McGill Questionnaire construct was used to find subjective discomfort at each minute. There was no difference in results for both sexes. Overall, leaning to one side was felt more comfortable while sitting with a curved back caused the highest discomfort.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".