Regular RGB-Video-Based Eye Movement Assessment for Parkinson’s Disease
Bibliographic record
Abstract
Eye-tracking, as an accessible, non-invasive technology, offers valuable insights into the human motor and cognitive functions, and it is an essential tool in studying neurodegenerative diseases such as Parkinson’s disease (PD). While current eye movement assessment for PD diagnosis mainly relies on high-end, specialized eye-tracker equipment, this work demonstrates that advanced deep learning methods using RGB-video from regular cameras (with 60 fps sampling rate, 1920 × 1080 image resolution) can provide promising performance on PD eye movement assessment. Our contributions are twofold: First, we show the potential and feasibility of using readily accessible, regular RGB camera data for PD eye movement assessment, making it more attractive for wide applicability in practice. Second, we propose a novel PD classification model by exploring temporal eye movement patterns from regular RGB-video data, and it can achieve performance comparable to or even better than current standard methods reliant on commercial, specialized eye-tracking equipment. The results highlight the promise of regular RGB-video-based PD assessment and the potential for more accessible diagnostic tools in PD studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".