Editorial: Ear-Centered Sensing: From Sensing Principles to Research and Clinical Devices
Bibliographic record
Abstract
From Sensing Principles to Research and Clinical DevicesThe human ears are an attractive location for bio-signal acquisition.Heart rate, respiratory rate, skin conductance, eye blink, and eye motion signals, as well as the electrical activity from muscles and the brain can be recorded from the ear.Moreover, the ears provide a discreet and natural anchoring point for placing the necessary wearable hardware, thereby reducing the visibility of integrated devices.In this Research Topic, we define ear-centered sensing as monitoring physiological signals with sensors located in the earcanal (intra-aural), in the pinna, or around the ear (circum-aural).Ear-centered sensing allows data recording over extended periods of time in everyday situations with little disturbance for the users.As the ear is an unconventional place for monitoring these physiological measures, it is necessary to gain a better understanding of the signals and to characterize the signals relative to the conventional measurement methods.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.020 | 0.023 |
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".