Piezoelectric Earcanal Bending Sensor
Bibliographic record
Abstract
The earcanal shape is unique for each human being and temporarily changes when the jaw moves due to eating, chewing, or speaking. The earcanal deformation can be studied by the geometrical analysis of a distorted earpiece custom-fitted inside the earcanal, but the distortion of the earpiece is complex in nature and complicated to analyze. An earcanal bending sensor consisting of a thin piezoelectric strip attached to a custom-fitted earpiece is presented in this paper. An analytical approach based on computing the geometrical parameters of distorted and undistorted earpieces is developed to: 1) estimate the average bending moment and the resulting stress applied to the custom-fitted earpiece while opening the jaw and 2) calculate the sensitivity of the piezoelectric earcanal bending sensor. The theoretical model is experimentally validated. The proposed approach can be applied to measure the bending of any curved body in general, and custom-fitted earpieces in particular. It, therefore, enables the designing of versatile in-ear sensors capable of tracking jaw activity and evaluating the energy capacity of earcanal deformation for in-ear energy harvesting purposes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".