An analysis of head and ear anthropometric data using 3D scans for head-wearable products
Bibliographic record
Abstract
The present study analysed the anthropometric characteristics of an integrated head-ear dataset to advance the ergonomic design of head-wearable products. By integrating 3D scanning, casting techniques, and template registration methods, scan data from 200 Korean participants were processed and 88 head and ear dimensions were measured. Among the head and ear dimensions, 95.5% showed larger average sizes in males than in females (mean difference = 0.3-25.1 mm; mean ratio = 1.02-1.37), with males exhibiting 67.0% greater variability (SD ratio = 1.01-1.36). Significant age-related changes were observed in 30.7% of the dimensions, with the majority of significant increases occurring in ear height-related dimensions, which rose by 5.0-18.0% from individuals in their 20s to their 50s, such as ear length (mean ratio: 30s = 1.03, 40s = 1.04, 50s = 1.09). These results can be of use for designing head-wearable products.
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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 | 0.001 | 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".