The Relationship among Seated Pressure Distribution, Posture and Discomfort Across A Seated Task: A Pilot Study
Bibliographic record
Abstract
The rise in computing technology has prolonged sedentary behaviour, which is alarmingly increasing the number of population suffering from back pain.Poor posture during extended sedentary periods in which office workers are sitting down and operating a computer, is a major cause of back pain.Detecting postural change is an important step in addressing this issue.However, such detection usually requires attachment of position sensors to the body which can hinder body movements in seated positions.This pilot study utilised CONFORMAT pressure mat to investigate if seating pressure can predict discomfort levels of different body parts along with postural changes during 30 minutes of sitting.The study involved one female participant sitting for 30 minutes while working on a computer.During sitting, Peak Pressure Ratio (PPR) was measured in 5 minute intervals, and the number of postural shifts were measured over the first 3 and last 3 minutes.Borg's scale was also used to evaluate the level of discomfort of different body regions.Results showed that the buttocks were the major areas of discomfort.Across the 30 minutes of sitting, there was strong significant (p<0.05)correlation between PPR and Borg's scores at the neck, shoulder and buttock regions (r = 0.94, 0.85, 0.87 respectively).Meanwhile, a positive relationship was found between the number of postural shifts and Borg's scores after prolonged sitting as well (Increase of postural shifts from 21 to 23 in the last 3 minutes of the task along with an increase of 1.45 from 0.95 for average Borg scores).The findings of this study provide insight into the use of pressure sensing mats in predicting onset of discomfort over body parts along with postural changes during prolonged sitting.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".