Use of magnetic resonance image registration to estimate displacement in the human earcanal due to the insertion of in-ear devices
Bibliographic record
Abstract
In-ear devices are used in a wide range of applications for which the device's usability and/or efficiency is strongly related to comfort aspects that are influenced by the mechanical interaction between the device and the walls of the earcanal. Although the displacement of the earcanal walls due to the insertion of the device is an important characteristic of this interaction, existing studies on this subject are very limited. This paper proposes a method to estimate this displacement in vivo using a registration technique on magnetic resonance images. The amplitude, the location and the direction of the earcanal wall displacement are computed for four types of earplugs used by one participant. These displacements give indications on how each earplug deforms the earcanal for one specific earcanal geometry and one specific earplug insertion. Although the displacement due to a specific earplug family cannot be generalized using the results of this paper, the latter help to understand where, how much, and how each studied earplug deforms the earcanal of the participant. This method is revealed as a promising tool to investigate further acoustical and physical comfort aspects of in-ear devices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".