Head motion-corrected eye gaze tracking with the da Vinci surgical system
Bibliographic record
Abstract
PURPOSE: To facilitate the integration of point of gaze (POG) as an input modality for robot-assisted surgery, we introduce a robust head movement compensation gaze tracking system for the da Vinci Surgical System. Previous surgical eye gaze trackers require multiple recalibrations and suffer from accuracy loss when users move from the calibrated position. We investigate whether eye corner detection can reduce gaze estimation error in a robotic surgery context. METHODS: A polynomial regressor is first used to estimate POG after an 8-point calibration, and then, using another regressor, the POG error from head movement is estimated from the shift in 2D eye corner location. Eye corners are computed by first detecting regions of interest using the You Only Look Once (YOLO) object detector trained on 1600 annotated eye images (open dataset included). Contours are then extracted from the bounding boxes and a derivative-based curvature detector refines the eye corner. RESULTS: (p = 0.079) for the right compared to the previous gold-standard POG error correction method. In addition, the eye corner pipeline showed a root-mean-squared error of 3.57 (SD = 1.92) pixels in detecting eye corners over 201 annotated frames. CONCLUSION: We introduce an effective method of using eye corners to correct for eye gaze estimation, enabling the practical acquisition of POG in robotic surgery.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".