Stable Tracking of Eye Gaze Direction During Ophthalmic Surgery
Journal:
arXiv
Published Date:
Jul 1, 2025
Abstract
Ophthalmic surgical robots offer superior stability and precision by reducing
the natural hand tremors of human surgeons, enabling delicate operations in
confined surgical spaces. Despite the advancements in developing vision- and
force-based control methods for surgical robots, preoperative navigation
remains heavily reliant on manual operation, limiting the consistency and
increasing the uncertainty. Existing eye gaze estimation techniques in the
surgery, whether traditional or deep learning-based, face challenges including
dependence on additional sensors, occlusion issues in surgical environments,
and the requirement for facial detection. To address these limitations, this
study proposes an innovative eye localization and tracking method that combines
machine learning with traditional algorithms, eliminating the requirements of
landmarks and maintaining stable iris detection and gaze estimation under
varying lighting and shadow conditions. Extensive real-world experiment results
show that our proposed method has an average estimation error of 0.58 degrees
for eye orientation estimation and 2.08-degree average control error for the
robotic arm's movement based on the calculated orientation.
