Eye Tracking and Pupillometry for Cognitive Load Estimation in Tele-Robotic Surgery

Type: MA thesis

Status: finished

Date: August 15, 2023 - February 1, 2024

Supervisors: Manuela Meier, Mathias Unberath (JHU), Andreas Maier, Roger D. Soberanis-Mukul (JHU)

Inferring the cognitive load of a surgeon during robotic surgery is important to
ensure safe and effective outcomes for patients, as high cognitive load can can
lead to errors and impact performance in robot command. This information
about cognitive load can be used in training to improve user skill.

One approach to estimate the cognitive load is, to utilize eye gaze and pupillometry
measurements, which have already been demonstrated as a potential
solution to this problem. As is has been shown, that the pupil diameter is related
to the task difficulty [1–3].

In the scope of this work, eye gaze and pupillometry measurements and tool
information will be used to infer user skill and proficiency in robot command.
Therefore, the eyetracker must be calibrated to the da Vinci robot vision pipeline
with a SPAAM-type of calibration [4, 5], and tool tracking methods in robotic
surgery must be developed.



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’20, page 1–12, New York, NY, USA, 2020. Association for Computing Machinery.

[2] Andrew T. Duchowski, Krzysztof Krejtz, Izabela Krejtz, Cezary Biele, Anna
Niedzielska, Peter Kiefer, Martin Raubal, and Ioannis Giannopoulos. The
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and Izabela Krejtz. Eye tracking cognitive load using pupil diameter and
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[4] Kenneth R. Moser, Mohammed Safayet Arefin, and J. Edward Swan. Impact
of alignment point distance and posture on spaam calibration of optical seethrough
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[5] Mihran Tuceryan, Yakup Genc, and Nassir Navab. Single-Point Active
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