基于面部电肌图的情绪识别,用于虚拟现实应用,使用在姿势表情上训练的机器学习分类器进行虚拟现实识别.
Jung-Hwan Kim1, Ho-Seung Cha2,3, Chang-Hwan Im1,3
1Department of Artificial Intelligence, Hanyang University, Seoul, 04763 Republic of Korea.
Biomedical engineering letters
|July 8, 2025
概括
这项研究引入了一种更快的方法,用于在虚拟现实 (VR) 中识别情绪,使用面部电肌图 (fEMG) 和姿势表情,绕过漫长的校准以获得更容易的VR体验.
科学领域:
- 人与计算机的交互
- 情感计算是一种情感计算.
- 虚拟现实 虚拟现实 虚拟现实
背景情况:
- 在VR中,基于相机的情感识别受到头部显示器 (HMD) 的限制.
- 面部电肌图 (fEMG) 是VR情感识别的可行替代方案.
- 目前的fEMG方法需要广泛的校准,这阻碍了用户采用.
研究的目的:
- 在VR中开发基于fEMG的情感识别的简化校准方法.
- 为了在虚拟现实环境中实现高效和用户友好的情绪检测.
主要方法:
- 训练了一个机器学习分类器,使用面部表情的fEMG数据.
- 在20名参与者中验证了模型,使用姿态表达式进行校准和视频进行测试.
- 专注于分类高价值和低价值的情绪状态.
主要成果:
- 在分类价值状态方面获得了88.20%的宏观F1得分.
- 证明了构成表达式校准方法的有效性和效率.
- 成功建立了第一个基于fEMG的情绪识别模型,使用面部表情.
结论:
- 拟议的方法大大简化了VR情感识别的fEMG校准.
- 这种方法提高了在沉浸式环境中情绪检测的实用性和效率.
- 在VR中为用户友好的界面铺平了道路.
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