Paper Info
Title
Eye movement simulation and detector creation to reduce laborious parameter adjustments.
Abstract
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.
Year
Venue
Field
2018
arXiv: Neurons and Cognition
Smooth pursuit,Computer vision,Conditional probability distribution,Mean squared error,Eye movement,Sampling (statistics),Artificial intelligence,Gamma distribution,Saccade,Mathematics,Machine learning,Sigmoid function
DocType
Volume
Citations 
Journal
abs/1804.00970
1
PageRank 
References 
Authors
0.35
20
6
Name
Order
Citations
PageRank
Wolfgang Fuhl194.61
thiago santini2638.72
Thomas C. Kübler312412.57
Nora Castner410.69
Wolfgang Rosenstiel51462212.32
Enkelejda Kasneci620233.86