A multimodal visual fatigue assessment model based on back propagation neural network and XGBoost

被引：0
|
作者：
Jia, Lixiu [1 ]
Jia, Lixin [2 ]
Zhao, Jian [1 ]
Feng, Lihang [2 ]
Huang, Xiaohua [1 ]
机构：
[1] Nanjing Inst Technol, Sch Comp Engn, Nanjing, Peoples R China
[2] Nanjing Tech Univ, Coll Elect Engn & Control Sci, Nanjing, Peoples R China
来源：
DISPLAYS | 2024年 / 83卷
基金：
中国国家自然科学基金;
关键词：
Visual fatigue; Backpropagation neural network; XGBoost; EEG; ECG; Human factors; VALIDITY; COMFORT;
D O I：
10.1016/j.displa.2024.102702
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
An experiment was conducted using a subjective questionnaire, ophthalmological parameters, electroencephalogram (EEG) signals, electrocardiogram (ECG) signals, and eye-tracking parameters. The goal was to investigate the impact of display modes (2D, normal 3D, and enhanced 3D) on visual fatigue. The results of paired samples ttests for both subjective and objective parameters indicated a significant influence of the display mode on visual fatigue. A visual fatigue assessment model employing multimodal parameters and based on a backpropagation neural network (BPNN) and XGBoost was proposed. The results suggested that the model was effective in predicting visual fatigue states, which the F1 and Accuracy value were both 0.94.
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页数：7
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