In-Ear Energy Harvesting: Harvester Design and Validation (Part II)
Bibliographic record
Abstract
The mechanical deformation of the ear canal induced by the temporomandibular joint movement constitutes a promising source of energy to power in-ear devices (hearing aids, communication earpieces, etc.). The large morphological variability of the human ear canal and its intrinsic dynamic characteristics—with displacement frequencies below 1.5 Hz with an average volume variation of 60 mm3—motivate the development of non-conventional dedicated energy harvesting methods. This paper demonstrates the concept and design of a modular hydraulic–piezoelectric self-actuated frequency up-conversion micromachine for energy harvesting. The mechanical energy is conveyed using a liquid-filled custom fitted earplug, which can be considered as a hydraulic pump. A hydraulic circuit composed of a pressure amplifier, two driven valves and two check valves allows to drive two micro-pistons. These micro-pistons actuate a bistable oscillator associated to a piezoelectric transducer allowing the low frequency mechanical excitation to be efficiently converted into electric energy through frequency-up conversion. The two integrated passively driven valves are based on tube buckling and allow the pistons to act alternatively on the oscillator to generate a backward and forward run for two jaw movements. A complete theoretical multiphysics model of the machine has been established for the design and evaluation of the potential of the proposed approach. Global analytical and refined FEM approaches have been combined to integrate the fluid and mechanical behaviors. Based on simulation and preliminary experimental data, the harvested energy is expected to be 8 µJ for one jaw closing, with a theoretical 40% end-to-end conversion efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".