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Thesis defences

PhD Oral Exam - Mohsen Parisay, Computer Science and Software Engineering

Computational Analysis of Eye-Strain on Digital Screens based on Eye Tracking Studies

DATE & TIME
Friday, December 10, 2021 (all day)
COST

This event is free

ORGANIZATION

School of Graduate Studies

CONTACT

Dolly Grewal

WHERE

Online

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

Computer vision syndrome (CVS) is composed of multiple eye vision problems due to the prolonged use of digital displays, including tablets and smartphones. These problems were shown to affect visual comfort as well as work productivity in both adults and teenagers. CVS causes eye and vision symptoms such as eye-strain, eye burn, dry eyes, double vision, and blurred vision. CVS, which causes severe vision and muscular problems due to repeated eye movements and excessive eye focus on computer screens, is a cause of work-related stress. In this thesis, we address this problem and present three general-purpose mathematical compound models for assessing eye-strain in eye-tracking applications, namely (1) Fixation-based Eye fatigue Load Index (FELiX), (2) Index of Difficulty for Eye-tracking Applications (IDEA), and (3) Eye-Strain Probation Model (ESPiM) based on eye-tracking parameters and subjective ratings to measure, predict, and compare the amount of fatigue or cognitive workload during target selection tasks for different user groups or interaction techniques. The ESPiM model is the outcome of both FELiX and IDEA, which benefit from direct subjective rating and, therefore, can be applied to assess the ESPiM model's efficacy. We present experiments and user studies that show that these models can measure potential eye-strain levels on individuals based on physical circumstances such as screen resolution and target positions per time.

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