In-Ear Energy Harvesting: Evaluation of the Power Capability of the Temporomandibular Joint
Bibliographic record
Abstract
Energy harvesting from human body motion is a promising source of power for future wearables and sensors. However, better knowledge on the power potentially available from each of the different types of human body motions is still required to develop efficient human-centered energy harvesting devices. This study investigates a method to evaluate the kinetic energy generated by the earcanal dynamic movements during the mastication of food. The wearable power sensing device, a hydraulic in-ear portable device, was developed for this study to measure the power associated with earcanal dynamic movements. The mean power was evaluated on six participants as they were eating. The results show that an average power of 26.2 mW is readily available in one earcanal from chewing an entire meal. This in-ear source of energy can ultimately be used to fully or partially power in-ear devices such as hearing aids, digital hearing protections, and emerging wearables technologies.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".