Cognitive load in tele-robotic surgery: a comparison of eye tracker designs

Eye gaze tracking and pupillometry are evolving areas within the field of tele-robotic surgery, particularly in the context of estimating cognitive load (CL). However, this is a recent field, and current solutions for gaze and pupil tracking in robotic surgery require assessment. Considering the necessity of stable pupillometry signals for reliable cognitive load estimation, we compare the accuracy of three eye trackers, including head and console-mounted designs.

We conducted a user study with the da Vinci Research Kit (dVRK), to compare the three designs. We collected eye tracking and dVRK video data while participants observed nine markers distributed over the dVRK screen. We compute and analyze pupil detection stability and gaze prediction accuracy for the three designs.

Head-worn devices present better stability and accuracy of gaze prediction and pupil detection compared to console-mounted systems. Tracking stability along the field of view varies between trackers, with gaze predictions detected at invalid zones of the image with high confidence.

While head-worn solutions show benefits in confidence and stability, our results demonstrate the need to improve eye tacker performance regarding pupil detection, stability, and gaze accuracy in tele-robotic scenarios.