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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Published in | SID International Symposium Digest of technical papers Vol. 47; no. 1; pp. 1237 - 1240 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Campbell
Blackwell Publishing Ltd
01.05.2016
Wiley Subscription Services, Inc |
Subjects | |
Online Access | Get full text |
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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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Bibliography: | istex:791FF9F555EC1CC3451D46205650760DF7AA41D3 ark:/67375/WNG-RQGQKHKV-F ArticleID:SDTP10857 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
ISSN: | 0097-966X 2168-0159 |
DOI: | 10.1002/sdtp.10857 |