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Eye movements hold information about human perception, intention andcognitive state. Various algorithms have been proposed to identify anddistinguish eye movements, particularly fixations, saccades, and smoothpursuits. A major drawback of existing algorithms is that they rely on accurateand constant sampling rates, impeding straightforward adaptation to newmovements such as micro saccades. We propose a novel eye movement simulatorthat i) probabilistically simulates saccade movements as gamma distributionsconsidering different peak velocities and ii) models smooth pursuit onsets withthe sigmoid function. This simulator is combined with a machine learningapproach to create detectors for general and specific velocity profiles.Additionally, our approach is capable of using any sampling rate, even withfluctuations. The machine learning approach consists of different binarypatterns combined using conditional distributions. The simulation is evaluatedagainst publicly available real data using a squared error, and the detectorsare evaluated against state-of-the-art algorithms.

Eye movement simulation and detector creation to reduce laborious parameter adjustments