In-ear acoustic monitoring for swallow detection
Bibliographic record
Abstract
Sialorrhea, or involuntary drooling, affects up to 25% of patients with neurodegenerative diseases, causing significant discomfort. Current treatments are mostly invasive. Non-invasive treatment may be possible by providing patients with reminders to swallow based on their swallowing frequency. A promising approach involves detecting swallowing using in-ear devices. To enable such treatment, we are collecting a database of 13 non-verbal human-produced events such as blinking, coughing, grinding teeth, etc. These events are captured by various sensors inside the ear, including microphones, air pressure microphones, PPG sensors, and IMUs, in different acoustic environments, both quiet and noisy. Additionally, we use ultrasound videos of the tongue as a ground truth for spontaneous swallowing. We will use this database to train machine learning algorithms for detecting and classifying swallowing and other non-verbal events. This work will enable the development of health monitoring in-ear devices for vulnerable individuals.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".