Fixation Identification in Centroid versus Start-Point Modes Using Eye-Tracking Data

• Published in: Perceptual and motor skills Vol. 106; no. 3; pp. 710 - 724
• Main Authors: Falkmer, Torbjörn, Dahlman, Joakim, Dukic, Tania, Bjällmark, Anna, Larsson, Matilda
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.06.2008
• Subjects: Algorithms; Artificial Intelligence; Data Collection; Eye Movements - physiology; Fixation, Ocular - physiology; Humans; Medical engineering; MEDICIN; MEDICINE; Medicinsk teknik; Ocular Physiological Phenomena; Odds Ratio; Other technology; Psychology; Psykologi; Reading; Research Design; Saccades - physiology; SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; Socialvetenskap; Software; Task Performance and Analysis; TECHNOLOGY; TEKNIKVETENSKAP; Videotape Recording - statistics & numerical data; Visual Fields - physiology; Visual Perception - physiology; Övriga teknikvetenskaper
• ISSN: 0031-5125; 1558-688X; 1558-688X
• DOI: 10.2466/pms.106.3.710-724

Fixation-identification algorithms, needed for analyses of eye movements, may typically be separated into three categories, viz. (i) velocity-based algorithms, (ii) area-based algorithms, and (iii) dispersion-based algorithms. Dispersion-based algorithms are commonly used but this application introduces some difficulties, one being optimization. Basically, there are two modes to reach this goal of optimization, viz., the start-point mode and the centroid mode. The aim of the present study was to compare and evaluate these two dispersion-based algorithms. Manual inspections were made of 1,400 fixations in each mode. Odds ratios showed that by using the centroid mode for fixation detection, a valid fixation is 2.86 times more likely to be identified than by using the start-point mode. Moreover, the algorithm based on centroid mode dispersion showed a good interpretation speed, accuracy, robustness, and ease of implementation, as well as adequate parameter settings.