Probabilistic assessment of visual fatigue caused by stereoscopy using dynamic Bayesian networks
Zhongyun Yuan1, Kai Zhuo1, Qiang Zhang1
1MicroNano System Research Center, Key Laboratory of Advanced Transducers and Intelligent Control System of Ministry of Education and Shanxi Province, College of Information and Computer Engineering, Taiyuan University of Technology, Taiyuan, China.
Acta Ophthalmologica
|April 27, 2018
Summary
This study introduces a dynamic Bayesian network (DBN) to measure 3D visual fatigue, incorporating multiple features. The DBN model accurately captures the influence of past states on current visual fatigue in stereoscopic viewing.
Area of Science:
- Computer Vision
- Human-Computer Interaction
- Biomedical Engineering
Background:
- 3D displays can cause visual fatigue.
- Existing methods for measuring visual fatigue are limited.
- A dynamic probabilistic framework is needed for accurate fatigue assessment.
Purpose of the Study:
- To develop a dynamic Bayesian network (DBN) model for measuring 3D visual fatigue.
- To adapt a DBN structure-based probabilistic framework for inferring viewer fatigue.
- To systematically evaluate 3D visual fatigue using a DBN.
Main Methods:
- Implemented a DBN model focusing on interdependencies between factors and visual fatigue.
- Utilized multiple features: contextual, contactless, and contact physiological data.
- Incorporated dynamic factors representing previous time slices.
Main Results:
- DBN model showed visual fatigue at time t is influenced by dynamic factors from time t-1.
- The proposed DBN scheme is more comprehensive than static Bayesian networks and MOS.
- Dynamic factors significantly impact visual fatigue inference.
Conclusions:
- The DBN model offers more reliable and accurate inference of visual fatigue.
- Incorporated a wider range of features for improved fatigue assessment.
- Confirmed the significance of dynamic factors for inferring stereoscopic visual fatigue.
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