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Visual Fatigue Assessment Based on Multi-task Learning

机译：基于多任务学习的视觉疲劳评估

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摘要

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. (C) 2019 Society for Imaging Science and Technology.
机译：近年来，随着立体显示技术的飞速发展，其应用在许多领域变得越来越流行，同时，观众的数量也在增加。视觉疲劳的问题变得越来越突出。视觉疲劳主要是由发散与适应冲突引起的。进行了评估实验，并且当他们观看立体内容时收集了他们的脑电图（EEG）数据，然后分析了观看过程中受试者的立体疲劳状态。由于深度学习被证明是一种有效的端到端学习方法，并且多任务学习可以缓解缺少注释数据的问题，因此作者通过使用多任务学习建立了基于脑电图的用户视觉疲劳评估模型，可以有效获取用户的视觉疲劳状态，从而做出舒适的设计，避免用户视觉疲劳造成的伤害。 （C）2019影像科学与技术学会。

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