通过整合拉班运动分析,身体表达的情感理解
Chenyan Wu1, Dolzodmaa Davaasuren1, Tal Shafir2
1Data Science and Artificial Intelligence Area, College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA 16802, USA.
Patterns (New York, N.Y.)
|October 25, 2023
概括
这项研究引入了一套新的数据集和方法,以改善从身体运动中自动识别情绪. 利用人类运动元件可以提高身体表达的情感理解 (BEEU),从而更好地分析心理健康.
科学领域:
- 计算机科学 计算机科学
- 心理学 心理学 心理学
- 情感计算是一种情感计算.
背景情况:
- 身体表达的情感理解 (BEEU) 旨在从人类运动中识别情感.
- 心理学研究证实,特定的运动元件能够传达情绪.
- 整合运动元件可以增强自动化情绪识别系统.
研究的目的:
- 通过结合人类运动元素,为BEEU开发一种新的方法.
- 为研究引入人类运动要素的精确数据集.
- 通过使用双源培训策略来增强BEEU模型.
主要方法:
- 介绍BoME (人体运动元件),这是一个关于人类运动元件的新型数据集.
- 基线深度学习模型的应用,以从运动数据中估计运动元素.
- 开发一个双源解决方案,以运动元和情感标签训练BEEU模型.
主要成果:
- 深度学习模型有效地从BoME数据集中学习人类运动的表示.
- 拟议的双源解决方案显著改善了BEEU在BoLD基准上的表现.
- 实验结果表明,整合动力元素用于情绪识别的好处.
结论:
- 人类的运动元素对于准确的身体表达的情感理解至关重要.
- BoME数据集和双源方法为BEEU研究提供了一个有希望的方向.
- 这项工作对推进情感理解和心理健康分析工具有意义.
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