Towards the detection of Alzheimer’s disease through eye movement changes using a hearable
Bibliographic record
Abstract
Hearables are wearable devices with in-ear microphones that utilize the occlusion effect to detect amplified low-frequency signals propagated by tissue and bone conduction, such as eardrum oscillations caused by eye movements [Greuters et al., 2018, PNAS, 115(6)]. Saccades, which are rapid and simultaneous movements of both eyes, provide valuable insight into a person's motor abilities and can be used to assess cognitive dysfunction. Research indicates that individuals with Alzheimer’s disease exhibit delayed, slow, and hypometric (i.e., undershooting) saccades, along with less fixation stability [Fletcher & Sharpe, 1986. An. Neuro. 20(4)]. In this project, 35 patients with Alzheimer’s disease or mild cognitive impairment, along with 35 matched control participants, will undergo various experiments, such as a picture description task, while wearing an eye tracking device and a hearable. The objective is to correlate recorded eardrum oscillations from the hearable and the amplitude and trajectory of horizontal and vertical saccades from the glasses. In addition, we aim to predict AD group inclusion based on data collected from the hearable. The long-term goal of this project is to develop a wearable, non-intrusive, and easy-to-use device capable of identifying Alzheimer’s disease and potentially predicting it earlier than the standard diagnosis timeframe.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".