Abstract
Studying and understanding team performance is very important for sports, games, health and any applications that involve a team of users. It is affected by team behaviour or cognition. Usually a team with a good shared cognition can perform better and achieve the set goal faster. Having a good team with a good shared behaviour is even more crucial in health care environments, especially for laprascopic surgery applications. Analyzing team cognition is a new area of research. In this paper, we study the team cognition between two surgeons, who performed a laparascopic simulation operation, by analyzing their eye tracking data spatially and temporally. We used Cross Recurrence Analysis (CRA) and overlap analysis to find spatio-temporal features that can be used to distinguish between a good performer team and a bad performer team. Dual eye tracking data for twenty two dyad teams were recorded during the simulation and then the teams were divided into good performer and poor performer teams based on the time to finish the task. We then analyze the signals to find common features for good performer teams. The results of this research indicates that the good performer teams show a smaller delay as well as have a higher overlap in the eyegaze signals compared to poor performer teams.
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Hajari, N., He, W., Cheng, I., Basu, A., Zheng, B. (2018). Spatio-Temporal Eye Gaze Data Analysis to Better Understand Team Cognition. In: Basu, A., Berretti, S. (eds) Smart Multimedia. ICSM 2018. Lecture Notes in Computer Science(), vol 11010. Springer, Cham. https://doi.org/10.1007/978-3-030-04375-9_4
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DOI: https://doi.org/10.1007/978-3-030-04375-9_4
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