Improving the Temporal Accuracy of Eye Gaze Tracking for the da Vinci Surgical System through Automatic Detection of Decalibration Events and Recalibration

Abstract

Robust and accurate eye gaze tracking can advance medical telerobotics by providing complementary data for surgical training, interactive instrument control, and augmented human–robot interactions. However, current gaze tracking solutions for systems such as the da Vinci Surgical System (dVSS) are limited to complex hardware installations. Additionally, existing methods do not account for operator head movement inside the surgeon console, invalidating the original calibration. This work provides an initial solution to these challenges that can seamlessly integrate into console devices beyond the dVSS. Our approach relies on simple and unobtrusive wearable eye tracking glasses and provides calibration routines that can contend with operator-head movements. An external camera measures movement of the glasses through trackers mounted on the glasses to detect invalidation of the prior calibration from head movement and slippage. Movements beyond a threshold of 5 cm or 9ˆ∘ prompt another calibration sequence. In a study where users moved freely in the surgeon console after an initial calibration procedure, we show that our system tracks the eye tracking glasses to initiate recalibration procedures. Recalibration can reduce the mean tracking error up to 89% compared to the current prevailing approach which relies on the initial calibration only. This work is an important first step towards incorporating user movement into gaze-based applications for the dVSS.