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

机译:基于Laban运动分析的情绪表达式分类方法在设计创建中的应用

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摘要

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:情感估计是伴随着人类传感和AI技术的进步之一。我们已经提出了一种基于Laban运动分析的情感表达式的分类方法,这是舞者的典型理论。在这项研究中,我们应用了设计创建的分类方法,其通常在数字制造中执行。首先,我们清楚地通过使用评估网格方法在数字制造任务中唤起了哪些情绪,通过构建任务的核心影响模型,我们分析了情绪。接下来,我们通过使用索尼FES观看U.使用DataSet来执行实验来测量身体动作和情绪的数据集来测量身体动作和情绪的数据集。我们通过身体动作分类用户,估计了通过使用分类的数据集诱发的情绪。并实现了大约80%的情绪估计。即使在与制造任务相比,身体运动不那么大或激活,我们也可以估计情绪。结果表明了分类方法的一般有效性。

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