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Eye-tracking Metrics Predicts Perceived Workload in Robotic Surgical Skills Training

Listed in Datasets

By Chuhao Wu1, Jackie Cha1, Jay Sulek2, Tian Zhou3, Chandru Sundaram2, Juan Wachs1, Denny Yu1

1. Purdue University 2. Indiana University 3. Boston Consulting Group

Eye-tracking data and NASA-TLX ratings during robotic surgical training.

Version 1.0 - published on 04 Oct 2019 doi:10.4231/0EVK-9P12 - cite this Content may change until committed to the archive on 04 Nov 2019

Licensed under CC0 1.0 Universal


Robotic surgery offers potential benefits of smaller incisions and shortened recovery time. Yet the technical complexity may increase surgeons’ workload. This study aims to assess the capability of eye-tracking metrics for monitoring mental workload in simulated robotic surgery tasks. Eight surgical trainees participated in 15 robotic skills simulation sessions. In each session, participants performed up to 12 simulated exercises. Performance was assessed by the robotic system. Participants completed the NASA-TLX survey after every completion of an exercise. Throughout all exercises, a wearable eye tracker, Tobii Pro Glasses 2.0, was used to sample pupil diameter and gaze point at 50Hz. Four main metrics were derived from the eye-tracking signals: pupil diameter, gaze entropy, fixation duration and PERCLOS. Related publication: "Eye-Tracking Metrics Predict Perceived Workload in Robotic Surgical Skills Training".

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