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During arthroscopic surgeries, surgeons are faced with challenges likecognitive re-projection of the 2D screen output into the 3D operating site ornavigation through highly similar tissue. Training of these cognitive processestakes much time and effort for young surgeons, but is necessary and crucial fortheir education. In this study we want to show how to recognize states ofconfusion of young surgeons during an arthroscopic surgery, by looking at theireye and head movements and feeding them to a machine learning model. With anaccuracy of over 94\% and detection speed of 0.039 seconds, our model is a steptowards online diagnostic and training systems for the perceptual-cognitiveprocesses of surgeons during arthroscopic surgeries.

States of confusion: Eye and Head tracking reveal surgeons' confusion during arthroscopic surgery