标题： 眼科手术中眼动方向的稳定跟踪 显示英文标题

标题： Stable Tracking of Eye Gaze Direction During Ophthalmic Surgery

摘要： 眼科手术机器人通过减少外科医生的自然手部震颤，提供更稳定的性能和精确度，使在有限手术空间内的精细操作成为可能。 尽管在开发基于视觉和力反馈的手术机器人控制方法方面取得了进展，术前导航仍然严重依赖手动操作，限制了一致性并增加了不确定性。 现有的手术中眼动估计技术，无论是传统方法还是基于深度学习的方法，都面临包括依赖附加传感器、手术环境中的遮挡问题以及需要面部检测等挑战。 为了解决这些限制，本研究提出了一种创新的眼部定位和跟踪方法，结合了机器学习与传统算法，消除了对特征点的需求，并在不同光照和阴影条件下保持稳定的虹膜检测和视线估计。 大量的真实世界实验结果表明，我们提出的方法在眼位估计方面的平均估计误差为0.58度，基于计算出的方位角，机械臂运动的平均控制误差为2.08度。 显示英文摘要

摘要： 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.