Visual fatigue evaluation based on depth in 3D videos

2013 ◽  
Author(s):  
Feng-jiao Wang ◽  
Xin-zhu Sang ◽  
Yangdong Liu ◽  
Guo-zhong Shi ◽  
Da-xiong Xu
Keyword(s):  
Visual Fatigue ◽  
Fatigue Evaluation

Visual fatigue evaluation and enhancement for 2D-plus-depth video

2010 ◽  
Author(s):  
Jaeseob Choi ◽  
Donghyun Kim ◽  
Bumsub Ham ◽  
Sunghwan Choi ◽  
Kwanghoon Sohn
Keyword(s):  
Visual Fatigue ◽  
Depth Video ◽  
Fatigue Evaluation

scholarly journals The Impact of Color Rendering on Visual Fatigue in Interior Zone of Tunnel

2021 ◽  
Vol 276 ◽  
pp. 02008
Author(s):  
Peng Liu ◽  
LiLi Dong ◽  
YingQi Jiang
Keyword(s):  
Traffic Safety ◽  
Visual Fatigue ◽  
Mesopic Vision ◽  
The Difference ◽  
Fatigue Evaluation ◽  
Critical Fusion Frequency ◽  
Color Rendering ◽  
Human Eyes ◽  
The Impact ◽  
Positive Effect

Judicious use of lamps is of profound significance to improve the internal traffic safety of tunnels. This study evaluated the effect of LED color on human visual fatigue under mesopic vision category. According to the difference of human eyes’ response to different wavelengths of light radiation, the mesopic vision spectral luminous efficiency curve is applied to the visual fatigue evaluation methods. Taking the critical fusion frequency as the physiological index, the detection experiment of human visual fatigue was carried out in the simulated tunnel environment. The results show that spectrum with high color rendering index has a positive effect on alleviating drivers’ visual fatigue, and is more suitable for tunnel interior lighting.

scholarly journals EEG-Based 3D Visual Fatigue Evaluation Using CNN

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1208 ◽  
Author(s):  
Kang Yue ◽  
Danli Wang
Keyword(s):  
Feature Extraction ◽  
Temporal Structure ◽  
Visual Fatigue ◽  
Eeg Signals ◽  
Visual Evaluation ◽  
Time Frequency ◽  
Multi Scale ◽  
Temporal Features ◽  
Depth Analysis ◽  
Fatigue Evaluation

Visual fatigue evaluation plays an important role in applications such as virtual reality since the visual fatigue symptoms always affect the user experience seriously. Existing visual evaluation methods require hand-crafted features for classification, and conduct feature extraction and classification in a separated manner. In this paper, we conduct a designed experiment to collect electroencephalogram (EEG) signals of various visual fatigue levels, and present a multi-scale convolutional neural network (CNN) architecture named MorletInceptionNet to detect visual fatigue using EEG as input, which exploits the spatial-temporal structure of multichannel EEG signals. Our MorletInceptionNet adopts a joint space-time-frequency features extraction scheme in which Morlet wavelet-like kernels are used for time-frequency raw feature extraction and inception architecture are further used to extract multi-scale temporal features. Then, the multi-scale temporal features are concatenated and fed to the fully connected layer for visual fatigue evaluation using classification. In experiment evaluation, we compare our method with five state-of-the-art methods, and the results demonstrate that our model achieve overally the best performance better performance for two widely used evaluation metrics, i.e., classification accuracy and kappa value. Furthermore, we use input-perturbation network-prediction correlation maps to conduct in-depth analysis into the reason why the proposed method outperforms other methods. The results suggest that our model is sensitive to the perturbation of β (14–30 Hz) and γ (30–40 Hz) bands. Furthermore, their spatial patterns are of high correlation with that of the corresponding power spectral densities which are used as evaluation features traditionally. This finding provides evidence of the hypothesis that the proposed model can learn the joint time-frequency-space features to distinguish fatigue levels automatically.

Symptom Validity Testing

2018 ◽  
Vol 23 (6) ◽  
pp. 14-15
Author(s):  
Lee H. Ensalada
Keyword(s):  
Memory Deficits ◽  
Symptom Validity ◽  
Sensory Cues ◽  
Validity Testing ◽  
Symptom Validity Testing ◽  
Tunnel Vision ◽  
Blurry Vision ◽  
The Common ◽  
Fatigue Evaluation ◽  
Choice Testing

Abstract Symptom validity testing (SVT), also known as forced-choice testing, is a means of assessing the validity of sensory and memory deficits, including tactile anesthesias, paresthesias, blindness, color blindness, tunnel vision, blurry vision, and deafness. The common feature among these symptoms is a claimed inability to perceive or remember a sensory signal. SVT comprises two elements: a specific ability is assessed by presenting a large number of items in a multiple-choice format, and then the examinee's performance is compared to the statistical likelihood of success based on chance alone. These tests usually present two alternatives; thus the probability of simply guessing the correct response (equivalent to having no ability at all) is 50%. Thus, scores significantly below chance performance indicate that the sensory cues must have been perceived, but the examinee chose not to report the correct answer—alternative explanations are not apparent. SVT also has the capacity to demonstrate that the examinee performed below the probabilities of chance. Scoring below a norm can be explained by fatigue, evaluation anxiety, inattention, or limited intelligence. Scoring below the probabilities of chance alone most likely indicates deliberate deceptions and is evidence of malingering because it provides strong evidence that the examinee received the sensory cues and denied the perception. Even so, malingering must be evaluated from the total clinical context.

Impairment Questions and Answers

1999 ◽  
Vol 4 (4) ◽  
pp. 4-4
Keyword(s):  
Symptom Validity ◽  
Validity Testing ◽  
Symptom Validity Testing ◽  
Negative Results ◽  
Tunnel Vision ◽  
Questions And Answers ◽  
Blurry Vision ◽  
Fatigue Evaluation ◽  
Choice Testing ◽  
Rule Out

Abstract Symptom validity testing, also known as forced-choice testing, is a way to assess the validity of sensory and memory deficits, including tactile anesthesias, paresthesias, blindness, color blindness, tunnel vision, blurry vision, and deafness—the common feature of which is a claimed inability to perceive or remember a sensory signal. Symptom validity testing comprises two elements: A specific ability is assessed by presenting a large number of items in a multiple-choice format, and then the examinee's performance is compared with the statistical likelihood of success based on chance alone. Scoring below a norm can be explained in many different ways (eg, fatigue, evaluation anxiety, limited intelligence, and so on), but scoring below the probabilities of chance alone most likely indicates deliberate deception. The positive predictive value of the symptom validity technique likely is quite high because there is no alternative explanation to deliberate distortion when performance is below the probability of chance. The sensitivity of this technique is not likely to be good because, as with a thermometer, positive findings indicate that a problem is present, but negative results do not rule out a problem. Although a compelling conclusion is that the examinee who scores below probabilities is deliberately motivated to perform poorly, malingering must be concluded from the total clinical context.

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