A Cost-Effective Webcam Eye-Tracking Algorithm for Robust Classification of Fixations and Saccades
Bibliographic record
Abstract
Dementia, particularly Alzheimer's disease (AD), poses significant challenges to cognitive function. Utilizing eye-tracking devices for early AD detection has shown promise, but expensive lab-grade equipment limits accessibility. This paper extends previous work by proposing a robust eye movement classification system using a cost-effective webcam eye-tracker to measure fixations and saccades. The algorithm is evaluated against established methods on Tobii and webcam data, exhibits competitive accuracy, especially excelling in saccade identification. Beyond its efficacy in Alzheimer's research, the proposed algorithm demonstrates versatility and reliability, positioning it as a valuable tool for various applications requiring precise eye movement characterization. The study utilizes a dataset collected from 23 participants without known cognitive decline, comprising 27 samples. This paper evaluates established eye movement classification methods alongside the novel algorithm using both lab-grade Tobii eye-tracker and webcam eye-tracker data. The proposed method led to F1 = 0.93 in detecting fixations, comparable to a Random Forest method (F1 = 0.95). However, the new method also led to considerable improvements in saccade classification, with F1 = 0.69, compared to the next best method with F1 = 0.40.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".