在虚拟现实中增强情绪识别:多式联络数据集和时间情绪探测器
Chenxin Qu1, Xiaoping Che1, Yafei Yang1
1Beijing Jiaotong University, Beijing, China.
Frontiers in psychology
|December 10, 2025
概括
这项研究引入了一个新的数据集和模型,用于在虚拟现实 (VR) 中识别情绪. 多模态时间情绪探测器 (MMTED) 模型在使用VR体验的生理信号来识别情绪方面表现出高准确度.
科学领域:
- 心理生理学 心理生理学
- 人与计算机的交互
- 情感计算是一种情感计算.
背景情况:
- 情感识别对于各种应用至关重要,虚拟现实 (VR) 提供沉浸式环境.
- 现有的VR多式联机情感数据集有限,阻碍了精确情感识别的进展.
- 当前的多式联运方式与噪音,个体变化和概括性作斗争.
研究的目的:
- 为了解决VR多式联络情感数据集的稀缺性,并提高情感识别准确性.
- 构建一个新的VR数据集,并开发一个强大的多式联接式情绪识别模型.
- 在现有和新收集的数据集上评估拟议模型的性能.
主要方法:
- 根据PAD模型开发了一个VR实验环境,包含10个激发情感的视频剪辑.
- 收集了来自38名参与者的电皮活动,眼睛跟踪和问卷数据.
- 提出了多模时态情绪探测器 (MMTED) 模型,整合了基线校准和多模融合.
主要成果:
- 该MMTED模型实现了高的识别准确度:VREED数据集的85.52%,新数据集的89.27%,组合数据集的85.29%.
- 新收集的数据集扩展了现有的基于VR的多式联络情感资源.
- 证明了电皮和眼睛跟踪信号的多式融合对情绪识别的有效性.
结论:
- MMTED模型为VR环境中的情绪识别提供了强大的解决方案.
- 新的数据集和模型对推进基于VR的情感计算做出了重大贡献.
- 多模体生理信号处理和VR中的情绪建模仍然是一个有前途的研究领域.
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