In-Ear Energy Harvesting: Source Characterization and Mechanical Simulator (Part I)
Bibliographic record
Abstract
During daily activities, such as chewing, eating, speaking, and so forth, the human jaw moves, and the earcanal is deformed by its anatomic neighbor called the temporomandibular joint (TMJ). Given the frequency of those jaw joint activities, the earcanal dynamic movement is a promising source of energy in close proximity to the ear, and such energy can be harvested by using a mechanical–electrical transducer dubbed energy harvester. However, the optimal design of such micromachine requires the characterization of the TMJ’s range of motion, its mechanical action on the earcanal, and its mechanical power capability. For that purpose, this research presents two methods for analyzing the earcanal dynamic movements: first, an in situ approach based on the measurement of the pressure variation in a water-filled earplug fitted inside the ear canal, and second, an anatomic-driven mechanism in the form of a chewing test fixture capable of reproducing the TMJ kinematics with great precision. The pressure earplug system provides the earcanal global dynamics, which can be derived as an equivalent displaced volume, while the chewing test fixture provides the discrete displacement along the earcanal wall. Both approaches are complementary and contribute to a better analysis of the interaction between the TMJ and earcanal. Ultimately, knowledge of the maximum displacement area and the derived generated power within the earcanal will lead to the design of a micromachine, allowing for the further investigation of in-ear energy harvesting strategies.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".