Rule-based learning for eye movement type detection

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, error free recordings, and impend straightforward adaptation to new movements, such as microsaccades, since they are designed for certain eye movement detection. We propose a novel rule-based machine learning approach to create detectors on annotated or simulated data. It is capable of learning diverse types of eye movements as well as automatically detecting pupil detection errors in the raw gaze data. Additionally, our approach is capable of using any sampling rate, even with fluctuations. Our approach consists of learning several interdependent thresholds and previous type classifications and combines them into sets of detectors automatically. We evaluated our approach against the state-of-the-art algorithms on publicly available datasets. Our approach is integrated in the newest version of EyeTrace which can be downloaded at http://www.ti.uni-tuebingen.de/Eyetrace.1751.0.html.