Virtual and experimental physical comfort testers for earplugs
Bibliographic record
Abstract
Earplugs are commonly used to prevent noise-induced hearing loss, but their efficacy is often hindered by discomfort, impacting consistent and correct use. Comfort of earplugs can be comprehended through four dimensions: physical (related to biomechanical and thermal interactions with the earcanal), acoustical (linked to noise perception), functional (including usability and efficiency) and psychological (related to well-being and satisfaction). The evaluation of (dis)comfort involves intricate interactions among components of a triad formed by the user, the earplug, and the work environment. Recent research by the authors has identified key psychosocial and physical characteristics of the triad influencing earplug physical discomfort. This study examines specific physical characteristics of the coupling between the "user" and "earplug" components for disposable and reusable earplugs. Virtual and experimental comfort testers serve as modeling tools and test benches to enable this determination. Mechanical comfort testers of increased complexity designed to assess tribological characteristics of the earplug/earcanal system are introduced. The study starts with simple benches measuring radial forces, extraction forces and friction coefficients, progressing to more advanced tools assessing mechanical pressure in various earcanal shapes either with rigid walls or lined with skin. This work aims at providing earplugs comfort-driven design methods for manufacturers.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".