Visual fatigue measurement model in stereoscopy based on Bayesian network

Yuan, Zhongyun; Kim, Jong Hak; Cho, Jun-Dong

doi:10.1117/1.oe.52.8.083110

Cited by 5 publications

(11 citation statements)

References 42 publications

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“…There are a number of cases for the inaccuracies using the scheme mentioned above (Yuan et al. ; Li et al. ): for example, environment factor, when human present in a discomfort (or not well acquainted) environment, an inaccurate interpretation of the facial expression would be caused, especially for the introverted persons.…”

Section: Background and Motivationmentioning

confidence: 99%

“…In our previous work Yuan et al. , there is a relating issue on stereoscopic visual fatigue based on the static Bayesian network (SBN). We ignored that fatigue recognition model must be evaluated over time.…”

Section: Background and Motivationmentioning

confidence: 99%

“…According to Yuan et al. ; the conditional probability of node Z (given the occurrence evidence) can be obtained by combining eqns and .

\begin{matrix} left P ()(Z = z_{t = 1}^{k} 1.2em1.2emtrue| e) = \frac{P ()(Z = z_{t = 1}^{k} 1.2em1.2emtrue| e_{t = 1}^{X, Y}) P ()(e_{t = 1}^{o} 1.2em1.2emtrue| Z = z_{t = 1}^{k})}{\sum_{i = 1}^{2} P ()(Z = z_{t = 1}^{i} 1.2em1.2emtrue| e_{t = 1}^{X, Y}) P ()(e_{t = 1}^{o} 1.2em1.2emtrue| Z = z_{t = 1}^{i})} \\ left k = 1, 2 \end{matrix}

…”

Section: Modelling Of Dbn‐based Measurement Implementationmentioning

confidence: 99%

“…(), the research ignores the human body condition and the testing environment condition, and in Yuan et al. ; the research ignores the dynamic factor. Therefore, with the same measuring objective for 3D fatigue on different situations, the results of the measurement may have significant deviations.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic assessment of visual fatigue caused by stereoscopy using dynamic Bayesian networks

Yuan

Zhuo

Zhang

et al. 2018

Acta Ophthalmologica

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Section: Background and Motivationmentioning

confidence: 99%

Section: Background and Motivationmentioning

confidence: 99%

“…According to Yuan et al. ; the conditional probability of node Z (given the occurrence evidence) can be obtained by combining eqns and .

\begin{matrix} left P ()(Z = z_{t = 1}^{k} 1.2em1.2emtrue| e) = \frac{P ()(Z = z_{t = 1}^{k} 1.2em1.2emtrue| e_{t = 1}^{X, Y}) P ()(e_{t = 1}^{o} 1.2em1.2emtrue| Z = z_{t = 1}^{k})}{\sum_{i = 1}^{2} P ()(Z = z_{t = 1}^{i} 1.2em1.2emtrue| e_{t = 1}^{X, Y}) P ()(e_{t = 1}^{o} 1.2em1.2emtrue| Z = z_{t = 1}^{i})} \\ left k = 1, 2 \end{matrix}

…”

Section: Modelling Of Dbn‐based Measurement Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Probabilistic assessment of visual fatigue caused by stereoscopy using dynamic Bayesian networks

Yuan

Zhuo

Zhang

et al. 2018

Acta Ophthalmologica

Self Cite

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“…With respect to multiple modality-based methods, eye fatigue has been measured using multiple sensors simultaneously [ 14 , 15 ]. Kim et al proposed a method for measuring eye fatigue on a 3D display using ECG sensors, the galvanic skin response (GSR), and the skin temperature (SKT) with SE [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Eye Fatigue Caused by 3D Displays Based on Multimodal Measurements

Bang

Heo

Choi

et al. 2014

Sensors

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With the development of 3D displays, user's eye fatigue has been an important issue when viewing these displays. There have been previous studies conducted on eye fatigue related to 3D display use, however, most of these have employed a limited number of modalities for measurements, such as electroencephalograms (EEGs), biomedical signals, and eye responses. In this paper, we propose a new assessment of eye fatigue related to 3D display use based on multimodal measurements. compared to previous works Our research is novel in the following four ways: first, to enhance the accuracy of assessment of eye fatigue, we measure EEG signals, eye blinking rate (BR), facial temperature (FT), and a subjective evaluation (SE) score before and after a user watches a 3D display; second, in order to accurately measure BR in a manner that is convenient for the user, we implement a remote gaze-tracking system using a high speed (mega-pixel) camera that measures eye blinks of both eyes; thirdly, changes in the FT are measured using a remote thermal camera, which can enhance the measurement of eye fatigue, and fourth, we perform various statistical analyses to evaluate the correlation between the EEG signal, eye BR, FT, and the SE score based on the T-test, correlation matrix, and effect size. Results show that the correlation of the SE with other data (FT, BR, and EEG) is the highest, while those of the FT, BR, and EEG with other data are second, third, and fourth highest, respectively.

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A visual discomfort recognition model based on the fusion of multisource information

et al. 2021

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An experiment was performed to explore the influence of image attributes (white level, black level, color saturation, and sharpness) on visual discomfort, with measurements of the subjective questionnaire, eye movement, and electrocardiogram. The objective and subjective results showed that these image attributes influenced visual discomfort significantly. A visual discomfort recognition model based on Dempster–Shafer evidence theory was proposed according to the experiment results. With multiple features and weighting coefficients, the model could recognize visual discomfort states well.

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Visual fatigue measurement model in stereoscopy based on Bayesian network

Cited by 5 publications

References 42 publications

Probabilistic assessment of visual fatigue caused by stereoscopy using dynamic Bayesian networks

Probabilistic assessment of visual fatigue caused by stereoscopy using dynamic Bayesian networks

Assessment of Eye Fatigue Caused by 3D Displays Based on Multimodal Measurements

A visual discomfort recognition model based on the fusion of multisource information

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