Subjective assessment on visual fatigue versus stereoscopic disparities

Liu, Yi; Guo, Xiang; Fan, Yubo; Meng, Xiangfeng; Wang, Jiahui

doi:10.1002/jsid.991

Cited by 10 publications

(13 citation statements)

References 24 publications

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“…Subjective and objective measurements are commonly used to evaluate visual fatigue. Liu et al [4] found that visual fatigue caused by negative parallax was larger than positive parallax using subjective assessment. Zheng et al [5] investigated the relationship between subjective symptoms of visual fatigue and visual functions.…”

Section: Introductionmentioning

confidence: 99%

21.1: Invited Paper: EEG‐based comparative measurement of visual fatigue caused by stereoscopic depth perception

Jia

Huang

Ding

et al. 2023

Symp Digest of Tech Papers

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In this study, we investigated the influence of stereoscopic depth on visual fatigue based on subjective and EEG measurement. 19 channels of EEG data of 17 subjects before and after watching 3D movie with different depth ranges were recorded, and the power of δ, θ, α, β, power spectral parameters (gravity frequency, power spectral entropy) were calculated and analyzed. The results showed that visual fatigue caused by 3D enhanced movies with larger negative disparity was more significantly severe than that of 3D normal movies.

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Section: Introductionmentioning

confidence: 99%

21.1: Invited Paper: EEG‐based comparative measurement of visual fatigue caused by stereoscopic depth perception

Jia

Huang

Ding

et al. 2023

Symp Digest of Tech Papers

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show abstract

“…Figure 4 visually demonstrates the perceptual misalignment of the image between the left and right eyes. Despite the existence of a single image, binocular parallax gives rise to horizontal or vertical misalignments in binocular perception [7,8]. In the context of horizontal parallax, two distinct types exist: convergence and divergence parallaxes, contingent upon the separation distance between binocular viewing points.…”

Section: Introductionmentioning

confidence: 99%

“…horizontal or vertical misalignments in binocular perception [7,8]. In the context of horizontal parallax, two distinct types exist: convergence and divergence parallaxes, contingent upon the separation distance between binocular viewing points.…”

Section: Introductionmentioning

confidence: 99%

“…This is achieved by introducing parallax at different display locations [1,5]. Consequently, when drivers engage with HUD information, they transition between distinct perceptions of visual depth, resulting in visual fatigue and diminished visual performance due to image parallax and variations in visual perception between the left and right eyes, resulting from parallax differences at different positions [8]. This effect is even more pronounced when the perceptual conflicts caused by the superimposition of virtual images on real objects are also considered [10,11].…”

Section: Introductionmentioning

confidence: 99%

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Parallax of Head-Up Displays and Visual Safety for Driving

Song,

Fan,

et al. 2023

Applied Sciences

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Head-up displays (HUDs), a novel form of virtual display, are characterized by their optical structure as a typical binocular virtual display system. This structure exhibits the effect of binocular parallax on visual perception, especially when diverse depth information is displayed on a screen, which makes the eyes switch between different parallax conditions, and easily affects visual tasks, and induces visual fatigue. Augmented reality HUDs (AR-HUDs) have a wider field of view and are more susceptible to parallax effects. In this study, to determine the acceptable parallax threshold in a two-dimensional virtual display system for HUDs, especially for adjacent positions, and to provide a reference for HUD design, visual comfort and task performance were experimentally evaluated by simulating the overall parallax effect and with step changes on the screen. Specifically, the effects of overall and stepped horizontal and vertical parallaxes on visual fatigue and task performance were evaluated under different conditions. The results showed that the overall horizontal and vertical parallaxes had no significant effect on visual fatigue and task performance. However, stepped horizontal parallax had a significant effect on task performance (p < 0.05), with a parallax value of 3.31 mrad between adjacent positions serving as an acceptable threshold for stepped horizontal parallax as a reference. Significant differences (p < 0.05) in the concentration and fluctuation ratios of the results caused by vertical stepped parallax were found, and an acceptable stepped parallax threshold of 2.24 mrad was obtained. Further, experiments revealed that stepped vertical parallax was more likely to lead to reading misalignments, halos, and distortions. In addition, an exponentially varying relationship between stepped parallax and the error rate of visual performance was observed, and a model was built to predict the degree of influence on visual performance caused by horizontal stepped parallax in virtual displays. This study provides a reference for parallax control between neighboring display icons in AR-HUDs.

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“…At present, there are three main ways to measure fatigue, and they mainly focus on mental fatigue. However, there is a lack of investigations on eye fatigue [3]. External cameras were used in the latest fatigue detection method to record the human face.…”

Section: Introductionmentioning

confidence: 99%

Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography

2022

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Nowadays, eye fatigue is becoming more common globally. However, there was no objective and effective method for eye fatigue detection except the sample survey questionnaire. An eye fatigue detection method by machine learning based on the Single-Channel Electrooculography-based System is proposed. Subjects are required to finish the industry-standard questionnaires of eye fatigue; the results are used as data labels. Then, we collect their electrooculography signals through a single-channel device. From the electrooculography signals, the five most relevant feature values of eye fatigue are extracted. A machine learning model that uses the five feature values as its input is designed for eye fatigue detection. Experimental results show that there is an objective link between electrooculography and eye fatigue. This method could be used in daily eye fatigue detection and it is promised in the future.

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Subjective assessment on visual fatigue versus stereoscopic disparities

Cited by 10 publications

References 24 publications

21.1: Invited Paper: EEG‐based comparative measurement of visual fatigue caused by stereoscopic depth perception

21.1: Invited Paper: EEG‐based comparative measurement of visual fatigue caused by stereoscopic depth perception

Parallax of Head-Up Displays and Visual Safety for Driving

Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography

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