A Novel Approach for Fit Analysis of Protective Clothing Using Three-Dimensional Body Scanning
Bibliographic record
Abstract
The purpose of this study is to explore a proposed approach to quantitatively characterize a threedimensional (3-D) fit.A 3-D body scanning technique was applied to capture the contour of nude and clothed manikin.The mesh model formed from nude and clothed scan by Rapidform software was aligned, superimposed and sectioned.From the neck to cuff, total 72 horizontal sections with equal interval of 2 cm were developed.The air gap size and distribution of overall and local body surface were analyzed.The total air volume was also calculated.Fit analysis was conducted on several protective clothing.The effect of fabric properties on air gap distribution was explored.The results indicated that average air gap of the fit clothing is around 25~30 mm and the overall air gap distribution is similar.The air gap showed uneven distribution over the body and it related to the body geometry and fabric properties.Larger size of air gap in legs and abdomen was observed.The air gap in chest, pelvis and arms, however, is minimal.The air gap over convex area is smaller than that of concave area.Coverall made of stiff fabric provided large air gap size.The research finding provides a technical base for clothing engineer to understand the overall fit associated with protection, thermal and movement comfort.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".