Earcanal anthropometry analysis for the design of realistic artificial ears
Bibliographic record
Abstract
How in-ear devices fit in the ear strongly influences the acoustical and mechanical (dis)comforts induced to the wearer. As important variations in the ear geometry exist based on gender, age and ethnicity, several studies collected ear anthropometric data as a basis for designing ear-mounted products. However, most of these studies focused on the ergonomic design of earbuds, and are thus limited to the geometry of the pinna, and concha (where earbuds fit). Few studies explored geometrical earcanal data for the design of intra-auricular hearing protectors that fit up to the earcanal second bend. The design of earplugs that fit to the widest range of earcanals requires realistic acoustical test fixtures representative of the population. This study uses statistical analysis and artificial intelligence based algorithms to cluster 32 Canadian workers pairs of earmolds scans as a function of earcanal curvilinear axis length, entrance, first and second bend cross sections area and circumference, but also earcanal tortuosity and cross sections aspect ratios. Dimensions relevant to cluster earcanals will be collected on a hundred of ears in a future study to design artificial ears which capture the inter-individual variability in mechanical and acoustical objective indicators related to the most important earplugs (dis)comfort attributes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".