Abstract

We report on an application of three multivariate time series classification methods, Hive-Cote 2.0, MiniRocket, and Mr-Petsc, to gaze and eyelid movement data to classify expertise. Our methods can be used to noninvasively monitor performance and identify experts using low-grade equipment. The test case was Tetris, which is a video game in which players arrange falling blocks to clear horizontal lines with increasing points and difficulty as the game advances. In addition to being able to classify the expert players, we can attribute the patterns within a time series that led to a prediction with Mr-Petsc. This allows us to describe eye behavior that is associated with expertise. This method can be used in any performance classification that involves screen-based activity that is accompanied by eye movement recordings, for example with a webcam.