Stable Tracking of Eye Gaze Direction During Ophthalmic Surgery

TL;DR

This paper introduces a novel eye gaze tracking method for ophthalmic surgery robots that combines machine learning with traditional algorithms, achieving stable and accurate gaze estimation without relying on additional sensors or facial landmarks.

Contribution

The study presents an innovative eye localization and tracking technique that operates reliably under varying lighting conditions, improving surgical robot control accuracy.

Findings

Average eye orientation estimation error of 0.58 degrees

Average robotic arm control error of 2.08 degrees

Robust gaze estimation under occlusion and shadow conditions

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.