Eye movement simulation and detector creation to reduce laborious parameter adjustments
This work addresses a bottleneck in eye-tracking research by reducing laborious parameter adjustments for researchers and practitioners, though it is incremental in improving existing methods.
The paper tackled the problem of existing eye movement algorithms requiring constant sampling rates by proposing a novel simulator that models saccades and smooth pursuits probabilistically, enabling adaptation to any sampling rate and achieving competitive performance against state-of-the-art detectors.
Eye movements hold information about human perception, intention and cognitive state. Various algorithms have been proposed to identify and distinguish eye movements, particularly fixations, saccades, and smooth pursuits. A major drawback of existing algorithms is that they rely on accurate and constant sampling rates, impeding straightforward adaptation to new movements such as micro saccades. We propose a novel eye movement simulator that i) probabilistically simulates saccade movements as gamma distributions considering different peak velocities and ii) models smooth pursuit onsets with the sigmoid function. This simulator is combined with a machine learning approach to create detectors for general and specific velocity profiles. Additionally, our approach is capable of using any sampling rate, even with fluctuations. The machine learning approach consists of different binary patterns combined using conditional distributions. The simulation is evaluated against publicly available real data using a squared error, and the detectors are evaluated against state-of-the-art algorithms.