Improving Synchronization of Eye Fixation and Saccade Measurements with Speech Recognition for Cognitive Assessment
Bibliographic record
Abstract
The escalating prevalence of cognitive disorders, notably Alzheimer's disease (AD), necessitates the development of accessible and cost-effective diagnostic tools for early detection. Changes in both eye movements and speech patterns have been associated with cognitive decline, and combining eye-tracking and speech analysis technologies may have advantages in detecting cognitive decline. While traditional lab-grade eye tracking systems are effective, their widespread adoption is hindered by cost and accessibility. Recent advancements have explored the feasibility of low-cost webcam-based systems, yet challenges persist in accurately classifying eye movements due to noise and lower precision. Our study evaluates a proposed system for cognitive assessment that combines fixation and saccade measurements from webcam-based eye-tracking data with synchronized speech data obtained during cognitive tasks. We extend a previously proposed algorithm to seamlessly combine synchronized eye tracking and speech data streams for comprehensive analysis. The presented results demonstrate promising accuracy for the proposed methods in identifying fixations, saccades, and oral identification respective speech, with minor variations compared to manual annotations. Specifically, the comparison between predicted and actual onset times for fixations and saccades reveals minimal discrepancies, suggesting the algorithm has needed performance. Moreover, the assessment of oral identification onset relative to fixations provides valuable insights into cognitive processing and response times for subject to name the object that they have just fixated on. Our study contributes to advancing AD research and offers potential for developing innovative diagnostic tools.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".