Using Density To Identify Fixations In Gaze Data Optimization-Based Formulations And Algorithms
Eye tracking is an increasingly common technology with a variety of practical uses. Eye-tracking gaze data can be categorized into two main events: fixations, which represent attention, whereas saccades occur between fixation events. We propose a novel manner to identify fixations based on their density, which concerns both the fixation duration as well as its inter-point proximity. We develop two mixed-integer nonlinear programming formulations and corresponding algorithms to recover the densest fixations in a data set. Our approach is parameterized by a unique value that controls for the degree of desired density. We conclude by discussing computational results and insights on real data sets.
Andrew C. Trapp completed his PhD in Industrial Engineering from the University of Pittsburgh in 2011. He is presently an Assistant Professor of Operations and Industrial Engineering at Worcester Polytechnic Institute (WPI) in Worcester, MA. His research focus is on using advanced analytical techniques, in particular mathematical optimization, to find optimal decisions to problems arising from a diverse cross-section of sectors such as humanitarian operations, healthcare, data mining, and sustainability. He develops new theory, models, and computational solution approaches to tackle such problems. He has published in leading optimization journals such as Operations Research, European Journal of Operational Research, INFORMS Journal on Computing, Annals of Operations Research, IIE Transactions, and Discrete Optimization.