Bayesian Identification of Fixations, Saccades, and Smooth Pursuits (1511.07732v1)

Abstract: Smooth pursuit eye movements provide meaningful insights and information on subject's behavior and health and may, in particular situations, disturb the performance of typical fixation/saccade classification algorithms. Thus, an automatic and efficient algorithm to identify these eye movements is paramount for eye-tracking research involving dynamic stimuli. In this paper, we propose the Bayesian Decision Theory Identification (I-BDT) algorithm, a novel algorithm for ternary classification of eye movements that is able to reliably separate fixations, saccades, and smooth pursuits in an online fashion, even for low-resolution eye trackers. The proposed algorithm is evaluated on four datasets with distinct mixtures of eye movements, including fixations, saccades, as well as straight and circular smooth pursuits; data was collected with a sample rate of 30 Hz from six subjects, totaling 24 evaluation datasets. The algorithm exhibits high and consistent performance across all datasets and movements relative to a manual annotation by a domain expert (recall: \mu = 91.42%, \sigma = 9.52%; precision: \mu = 95.60%, \sigma = 5.29%; specificity \mu = 95.41%, \sigma = 7.02%) and displays a significant improvement when compared to I-VDT, an state-of-the-art algorithm (recall: \mu = 87.67%, \sigma = 14.73%; precision: \mu = 89.57%, \sigma = 8.05%; specificity \mu = 92.10%, \sigma = 11.21%). For algorithm implementation and annotated datasets, please contact the first author.