Under Pressure: The Effect of System, Surface Geometry, and Load on the Measure of Contact Pressure
Bibliographic record
Abstract
Many human activities involve carrying payloads using a load carriage system, including backpacks, exoskeletons, and utility belts. Poorly designed systems can lead to musculoskeletal injury, pain, and discomfort. The distribution of skin contact pressure is a common biomechanical metric for assessing ergonomic designs that are both safe and comfortable. Although many studies highlighted the technical challenges of early pressure measurement systems, systematic evaluations of modern systems remain limited, despite their growing application in research and industry. This study compared three modern pressure measurement systems for absolute error and drift rate. The Novel Pliance®, Tekscan I-Scan™, and XSENSOR™ X3 systems were calibrated according to the manufacturer's protocol and tested on a flat and curved surface for pressures between 30 and 90 kPa. Ten independent trials were collected per system, surface, and load at 30 min of static pressure. Novel Pliance® exhibited the lowest absolute error (4.51 ± 2.26 kPa) and drift rate (0.05 ± 0.06 kPa/min), which significantly differed from Tekscan I-Scan™ (absolute error: 7.16 ± 4.70 kPa; drift rate: 0.15 ± 0.11 kPa/min) and XSENSOR™ X3 (9.29 ± 4.90 kPa; 0.08 ± 0.07 kPa/min). Moreover, Novel Pliance® exhibited no absolute error difference per surface and load, and drift significantly lessened over time in nearly all conditions. This study presents the importance of pressure measurement system selection in biomechanical research to enhance data accuracy, reduce confounding variables, and improve the validity of ergonomic evaluations.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".