Visual Fatigue Estimation by Eye Tracker with Regression Analysis. (24th January 2022)

• Record Type: Journal Article
• Authors: Lin, Hui-Ju; Chou, Li-Wei; Chang, Kang-Ming; Wang, Jing-Fong; Chen, Sih-Huei; Hendradi, Rimuljo
• Other Names: Singh Pradeep Kumar Academic Editor.
• Is Part Of: Journal of sensors. Volume 2022(2022)
• Publication Date: 2022-01-24
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2022/7642777
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : The traditional way to detect visual fatigue is to use the questionnaire or to use critical fusion frequency of high-frequency exchanges due to eye fatigue. The objective of this study was to explore whether eye movement behavior can be used as an objective tool to detect visual fatigue. Thirty-three participants were tested in this study. Their subjective visual fatigue survey, critical fusion frequency, and eye tracker of one minute gaze were measured before and after 20 minutes visual fatigue task. There were significant differences before and after visual fatigue task on survey and eye tracker-derived features. By multiple regression analysis with four eye tracker features, total fixation time duration of the inner circle, longest continuous duration of inner circle viewing time, maximum saccade distance, and focus radius, the regressionR square value was greater than 0.9 for all critical fusion frequency data and when subjective visual fatigue assessment was greater than 12 points. In conclusion, eye movement behavior can be used to detect visual fatigue more sensitively even than the traditional critical flicker fusion assessment. Eye tracker can also provide well regression model to fit traditional critical fusion frequency measurement and subjective visual fatigue survey.