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Application of Classification Method of Emotional Expression Type Based on Laban Movement Analysis to Design Creation

机译:基于拉班运动分析的情感表达类型分类方法在设计创作中的应用

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Emotion estimation is one of the most essential research areas along with the progress of human sensing and AI technologies. We have already proposed a classification method of emotional expression type based on Laban movement analysis, which is a typical theory for dancers. In this study, we applied the classification method to design creation, which is typically performed in digital fabrication. First, we made clear what kinds of emotions are evoked in digital fabrication tasks by using the evaluation grid method, and we analyzed the emotions by constructing a core affect model for the task. Next, we performed an experiment to measure the dataset of body motions and emotions by performing an experiment using SONY FES Watch U. By using the dataset, we classified users by body motions, estimated the evoked emotions by using the classified dataset. and realized emotion estimation at about 80%. We could estimate emotions even when the body motions were not so large or activated compared with the fabrication task. The results showed the general effectiveness of the classification method.
机译:随着人类感知和AI技术的进步,情感估计是最重要的研究领域之一。我们已经提出了基于拉班运动分析的情感表达类型的分类方法,这是舞者的一种典型理论。在这项研究中,我们将分类方法应用于设计创建,通常在数字制造中执行。首先,我们使用评估网格方法弄清了数字化制造任务中会引起哪些情感,并通过构建任务的核心情感模型来分析情感。接下来,我们通过使用SONY FES Watch U进行实验,进行了一项测量身体运动和情绪数据集的实验。通过使用该数据集,我们通过身体运动对用户进行分类,并使用分类后的数据集估算诱发的情绪。并实现了约80%的情绪估计。即使与制造任务相比,身体动作不那么大或没有激活,我们也可以估计情绪。结果表明了该分类方法的一般有效性。

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