Power capacity from earcanal dynamic motion
Bibliographic record
Abstract
In-ear devices, such as a hearing aids, electronic earplugs, and wearables, need electrical power to operate. Batteries are the current solution, but unfortunately they also create other problems. For example, several hundred million users, mostly elderly, must change their hearing aid batteries on a weekly basis, which represents not only significant financial costs but a negative environmental impact. A promising alternative involves harvesting energy by converting the dynamic jaw movements into electrical energy via the earcanal. The extent that jaw movements distort the earcanal is still unknown, making it difficult to design the appropriate energy harvesting system for the earplug. Moreover, the finite element methods are barely capable to model the behavior of the earcanal distortion because of the complexity of mechanisms that deform the earcanal. However, this paper presents an alternative method, based on analytical considerations, to understand in-ear mechanical quasi-static deformations using earcanal point clouds. This model quantifies the bending and compressive movements of the earcanal. It can therefore be used to select an appropriate deformation mode for harvesting energy from the earcanal’s dynamic motion. The value of this approach was illustrated by calculating the obtainable mechanical energy from 12 human subjects. On average, the bending energy in a human earcanal was found to be three times greater than the radial compression energy. This key finding will need to be considered in the design of future in-ear energy harvesting devices. Such an energy harvesting device has the potential to revolutionize the market for in-ear wearable devices and hearing aids by complementing or replacing battery technology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".