眼科手术中眼注视方向的稳定跟踪
Stable Tracking of Eye Gaze Direction During Ophthalmic Surgery
AI总结：
本文针对眼科手术机器人术前导航依赖手动操作的问题，提出一种结合机器学习与传统算法的眼部定位跟踪方法，无需面部关键点即可在多变光照下稳定估计注视方向，实验显示眼朝向估计误差0.58度、机械臂控制误差2.08度。
摘要：
眼科手术机器人通过减少人类外科医生的自然手部震颤，提供了卓越的稳定性和精确性，从而能够在受限的手术空间中执行精细操作。尽管在开发基于视觉和力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，这限制了一致性并增加了不确定性。手术中现有的眼注视估计技术，无论是传统的还是基于深度学习的，都面临诸多挑战，包括依赖额外传感器、手术环境中的遮挡问题以及需要进行面部检测。为了解决这些局限性，本研究提出了一种创新的眼部定位与跟踪方法，该方法将机器学习与传统算法相结合，消除了对关键点的要求，并在不同光照和阴影条件下保持稳定的虹膜检测和注视估计。大量真实世界实验结果表明，我们提出的方法在眼部朝向估计方面具有0.58度的平均估计误差，基于计算出的朝向进行机器人手臂运动的平均控制误差为2.08度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
