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Record W1986583221 · doi:10.1063/1.4792307

Ear canal dynamic motion as a source of power for in-ear devices

2013· article· en· W1986583221 on OpenAlexaff
Aidin Delnavaz, Jérémie Voix

Bibliographic record

VenueJournal of Applied Physics · 2013
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsEar canalRoot canalPower (physics)Temporomandibular jointDeformation (meteorology)AcousticsJoint (building)Middle earInner earComputer scienceMaterials sciencePhysicsEngineeringAnatomyOrthodonticsStructural engineeringMedicine

Abstract

fetched live from OpenAlex

Ear canal deformation caused by temporomandibular joint (jaw joint) activity, also known as “ear canal dynamic motion,” is introduced in this paper as a candidate source of power to possibly recharge hearing aid batteries. The geometrical deformation of the ear canal is quantified in 3D by laser scanning of different custom ear moulds. An experimental setup is proposed to measure the amount of power potentially available from this source. The results show that 9 mW of power is available from a 15 mm3 dynamic change in the ear canal volume. Finally, the dynamic motion and power capability of the ear canal are investigated in a group of 12 subjects.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.202
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2013
Admission routes1
Has abstractyes

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