情感多感官书籍

Juliana Kreitlon, Luma Chen, L. Santos, Rodolpho Nascimento, Maria Rita Carrara, Gustavo Paiva Guedes
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引用次数: 5

摘要

情感计算设备已被用于电子学习、游戏和电影领域,以获取个体的情感状态。因此,像有趣、无聊、沮丧和兴奋这样的状态可以被检测到。基于这些状态,可以执行许多任务。例如,如果个人在电子学习系统中进行练习感到无聊,系统可以执行一些操作;例如,它可以提出更具挑战性的任务。受上述情景的启发,本研究提出了一个基于电子书阅读、多感官媒体(mulsemmedia)区域和情感计算区域的模型。这种模式被称为情感多感官图书,它有两个主要目标:(i)从用户那里获取情感状态;(ii)产生多感官媒介效应。主要的动机是,多媒体可以提高个人的体验质量,情感计算可以用来捕捉个人在阅读过程中的情感状态(例如,无聊)。然而,评估是否有可能检测到个人在阅读任务中的情感状态变化是很重要的。在这种情况下,这项工作评估是否有统计差异在同一个人阅读两种类型的文本。第一个是饮水机指南页,第二个是一首诗。使用Wilcoxon符号秩检验来评估个体在阅读每个文本时皮肤电反应的测量平均值的差异。分析表明,两篇文章的阅读之间存在统计学上的显著差异。
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Affective Multisensorial Books
Affective computing devices have been used in e-learning, game, and movie areas to acquire individuals' affective states. Thus, states like fun, boredom, frustration, and excitement can be detected. Based on these states, many tasks can be executed. For example, if an individual practicing an exercise in an e-learning system is bored, some action can be executed by the system; for example, it could present more challenging tasks. Inspired by the above scenario, the present work proposes the creation of a model based on e-books reading, multisensorial media (MulSeMedia) area, and affective computing area. This model is referred to as Affective Multisensorial Books, and it has two main goals: (i) acquire affective states from users; (ii) produce multisensorial media effects. The main motivation is related to the fact that MulSeMedia can improve Quality of Experience of individuals and affective computing can be used to capture individuals' affective states during reading (e.g., boredom). However, it is important to evaluate if it is possible to detect changes in affective states of individuals in the reading task. In this scenario, this work evaluates if there are statistical differences in the same individual reading two genres of texts. The first one is a drinking fountain guide page, and the second is a poem. The Wilcoxon signed-rank test was used to assess the difference in mean of measurements in Galvanic Skin Response of individuals when reading each of the texts. The analysis indicates a statistically significant difference between the reading of the two texts.
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