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BASIC EXPERIMENT FOR SWITCHING DIFFICULTY IN VIRTUAL ENVIRONMENT

机译:虚拟环境切换困难的基本实验

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Recently, there has been considerable interest in immersive virtual workspace. This environment makes us more concentrated. Applying this environment to individual work, worker can concentrate on their works harder. Efficiency of individual work largely depends on worker's mental states. Therefore, working in accordance with worker's mental states may improve work efficiency. In this study, we focus on physiological signals such as brain wave and breathing as a method of estimating worker's mental states, which are deeply related with ones including concentration and load.rnSo we propose task supporting method based on physiological signals in virtual environment. In this method, EEGs is used to quantify worker's mental states as an unique index of BA-Level. We also use breathing information related to worker's mental states. Worker's mental states are estimated from indexes of each physiological signals. This information is reflected to complexity and difficulty of work in virtual environment.rnAccording to the result of experiments, indexes deeply related to worker's mental states are derived from EEGs and breathing information. It was found that last 60 seconds of BA-Level and breathing frequency is related to worker's mental states. In this paper, we research the relation between difficulty of work and worker's mental states. Using this knowledge, switching method of difficulty is suggested.
机译:近来,对沉浸式虚拟工作空间引起了极大的兴趣。这种环境使我们更加集中。将这种环境应用于个人工作,工人可以更加专注于自己的工作。个人工作的效率在很大程度上取决于工人的精神状态。因此,按照工人的精神状态进行工作可以提高工作效率。在这项研究中,我们将注意力集中在脑电波和呼吸等生理信号上,作为估计工人精神状态的一种方法,这与包括集中精神和负荷在内的心理状态密切相关。因此,我们提出了一种在虚拟环境中基于生理信号的任务支持方法。在这种方法中,EEG用于量化工人的心理状态,作为BA级的唯一指标。我们还使用与工人的精神状态有关的呼吸信息。从每个生理信号的指标估计工人的精神状态。这些信息反映了虚拟环境中工作的复杂性和难度。根据实验结果,从脑电图和呼吸信息中得出了与工人心理状态密切相关的指标。发现最后60秒的BA级和呼吸频率与工人的精神状态有关。在本文中,我们研究了工作难度与工人心理状态之间的关系。利用这些知识,提出了难度切换方法。

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