Visual Fatigue Assessment Based on Multi-task Learning

2019 ◽  
Vol 63 (6) ◽  
pp. 60414-1-60414-8
Author(s):  
Danli Wang ◽  
Xueyu Wang ◽  
Yaguang Song ◽  
Qian Xing ◽  
Nan Zheng
Keyword(s):  
Rapid Development ◽  
Assessment Model ◽  
Learning Method ◽  
Stereoscopic Display ◽  
Visual Fatigue ◽  
Fatigue Assessment ◽  
Task Learning ◽  
Eeg Data ◽  
Display Technology ◽  
Electroencephalogram Eeg

Abstract In recent years, with the rapid development of stereoscopic display technology, its applications have become increasingly popular in many fields, and, meanwhile, the number of audiences is also growing. The problem of visual fatigue is becoming more and more prominent. Visual fatigue is mainly caused by vergence‐accommodation conflicts. An evaluation experiment was conducted, and the electroencephalogram (EEG) data of the subjects were collected when they were watching stereoscopic content, and then the stereoscopic fatigue state of the subjects during the viewing process was analyzed. As deep learning is proved to be an effective end-to-end learning method and multi-task learning can alleviate the problem of lacking annotated data, the authors establish a user visual fatigue assessment model based on EEG by using multi-task learning, which can effectively obtain the user’s visual fatigue status, so as to make the comfort designs to avoid the harm caused by user’s visual fatigue.

scholarly journals InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7251
Author(s):  
Hong Zeng ◽  
Jiaming Zhang ◽  
Wael Zakaria ◽  
Fabio Babiloni ◽  
Borghini Gianluca ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Support Vector ◽  
Learning Method ◽  
Learning Models ◽  
Eeg Signals ◽  
Eeg Data ◽  
State Detection ◽  
Fatigue Detection ◽  
Electroencephalogram Eeg ◽  
Machine Learning Models

Electroencephalogram (EEG) is an effective indicator for the detection of driver fatigue. Due to the significant differences in EEG signals across subjects, and difficulty in collecting sufficient EEG samples for analysis during driving, detecting fatigue across subjects through using EEG signals remains a challenge. EasyTL is a kind of transfer-learning model, which has demonstrated better performance in the field of image recognition, but not yet been applied in cross-subject EEG-based applications. In this paper, we propose an improved EasyTL-based classifier, the InstanceEasyTL, to perform EEG-based analysis for cross-subject fatigue mental-state detection. Experimental results show that InstanceEasyTL not only requires less EEG data, but also obtains better performance in accuracy and robustness than EasyTL, as well as existing machine-learning models such as Support Vector Machine (SVM), Transfer Component Analysis (TCA), Geodesic Flow Kernel (GFK), and Domain-adversarial Neural Networks (DANN), etc.

Stereoscopic visual fatigue assessment and modeling

2014 ◽  
Author(s):  
Danli Wang ◽  
Tingting Wang ◽  
Yue Gong
Keyword(s):  
Visual Fatigue ◽  
Fatigue Assessment

Reduced electroencephalogram complexity in postoperative delirium

2021 ◽  
Author(s):  
Sean Tanabe ◽  
Maggie Parker ◽  
Richard Lennertz ◽  
Robert A Pearce ◽  
Matthew I Banks ◽  
...  
Keyword(s):  
Postoperative Delirium ◽  
Primary Outcome ◽  
Rating Scale ◽  
Monocyte Chemoattractant Protein 1 ◽  
Monocyte Chemoattractant ◽  
Monocyte Chemoattractant Protein ◽  
Eeg Data ◽  
Before And After ◽  
Electroencephalogram Eeg ◽  
Multiple Comparison Correction

Abstract Delirium is associated with electroencephalogram (EEG) slowing and impairments in connectivity. We hypothesized that delirium would be accompanied by a reduction in the available cortical information (i.e. there is less information processing occurring), as measured by a surrogate, Lempil-Ziv Complexity (LZC), a measure of time-domain complexity. Two ongoing perioperative cohort studies (NCT03124303, NCT02926417) contributed EEG data from 91 patients before and after surgery; 89 participants were used in the analyses. After cleaning and filtering (0.1-50Hz), the perioperative change in LZC and LZC normalized (LZCn) to a phase-shuffled distribution were calculated. The primary outcome was the correlation of within-patient paired changes in delirium severity (Delirium Rating Scale-98 [DRS]) and LZC. Scalp-wide threshold free cluster enhancement was employed for multiple comparison correction. LZC negatively correlated with DRS in a scalp-wide manner (peak channel r 2=0.199, p<0.001). This whole brain effect remained for LZCn, though the correlations were weaker (peak channel r 2=0.076, p=0.010). Delirium diagnosis was similarly associated with decreases in LZC (peak channel p<0.001). For LZCn, the topological significance was constrained to the midline posterior regions (peak channel p=0.006). We found a negative correlation of LZC in the posterior and temporal regions with monocyte chemoattractant protein-1 (peak channel r 2=0.264, p<0.001, n=47) but not for LZCn. Complexity of the EEG signal fades proportionately to delirium severity implying reduced cortical information. Peripheral inflammation, as assessed by monocyte chemoattractant protein-1, does not entirely account for this effect, suggesting that additional pathogenic mechanisms are involved.

scholarly journals Accurate Age Estimation Using Multi-Task Siamese Network-Based Deep Metric Learning for Front Face Images

Symmetry ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 385 ◽  
Author(s):  
Yoosoo Jeong ◽  
Seungmin Lee ◽  
Daejin Park ◽  
Kil Park
Keyword(s):  
Age Estimation ◽  
Metric Learning ◽  
Front Face ◽  
Experimental Fact ◽  
Gender Classification ◽  
Learning Method ◽  
Siamese Network ◽  
Task Learning ◽  
Face Images ◽  
Deep Metric Learning

Recently, there have been many studies on the automatic extraction of facial information using machine learning. Age estimation from front face images is becoming important, with various applications. Our proposed work is based on the binary classifier, which only determines whether two input images are clustered in a similar class, and trains the convolutional neural networks (CNNs) model using the deep metric learning method based on the Siamese network. To converge the results of the training Siamese network, two classes, for which age differences are below a certain level of distance, are considered as the same class, so the ratio of positive database images is increased. The deep metric learning method trains the CNN model to measure similarity based on only age data, but we found that the accumulated gender data can also be used to compare ages. From this experimental fact, we adopted a multi-task learning approach to consider the gender data for more accurate age estimation. In the experiment, we evaluated our approach using MORPH and MegaAge-Asian datasets, and compared gender classification accuracy only using age data from the training images. In addition, from the gender classification, we found that our proposed architecture, which is trained with only age data, performs age comparison by using the self-generated gender feature. The accuracy enhancement by multi-task learning, for the simultaneous consideration of age and gender data, is discussed. Our approach results in the best accuracy among the methods based on deep metric learning on MORPH dataset. Additionally, our method is also the best results compared with the results of the state of art in terms of age estimation on MegaAge Asian and MORPH datasets.

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