P-31: Visual Fatigue Assessment and Modeling Based on ECG and EOG Caused by 2D and 3D Displays

Three‐dimensional (3D) displays become more and more popular in many fields, because they can provide amazing visual effects. However, visual fatigue as one of the critical factors has seriously impeded the wide range of applications of 3D technology. Electrocardiograph (ECG) and electrooculogram (E...

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Bibliographic Details
Published inSID International Symposium Digest of technical papers Vol. 47; no. 1; pp. 1237 - 1240
Main Authors Yang, Xinpan, Wang, Danli, Hu, Haichen, Yue, Kang
Format Journal Article
LanguageEnglish
Published Campbell Blackwell Publishing Ltd 01.05.2016
Wiley Subscription Services, Inc
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Summary:Three‐dimensional (3D) displays become more and more popular in many fields, because they can provide amazing visual effects. However, visual fatigue as one of the critical factors has seriously impeded the wide range of applications of 3D technology. Electrocardiograph (ECG) and electrooculogram (EOG) have been widely used for monitoring visual fatigue. In this paper, one more objective and effective visual fatigue evaluation model is proposed. Subjective scores (SS), heart rates (HR), blink frequency (BF), Sympathetic activity, Vagal activity, Sympathetic‐vagal ratio (a measure of autonomic balance), Average of NN intervals (NN‐MEAN) and Standard deviation of NN intervals (SDNN) of all subjects were collected to analyze the change of visual fatigue during the continuous viewing 3D/2D movie. The results showed that SS, HR, BF, Sympathetic activity and SDNN all increase with visual fatigue while NN‐MEAN and Vagal activity decrease. As shown by the result of subjective scoring, the visual fatigue has an overall trend of increasing when viewing both 2D and 3D videos and it is higher in 3D condition than in 2D. Based on the results above, two models were built to predict visual fatigue from above indicates during continuous viewing 2D and 3D video processes respectively. The performance of the models makes a good prediction of visual fatigue.
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ArticleID:SDTP10857
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SourceType-Scholarly Journals-1
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ISSN:0097-966X
2168-0159
DOI:10.1002/sdtp.10857