预测情绪状态的兴奋和价值值使用学习,预设计和深度视觉特征
Itaf Omar Joudeh1, Ana-Maria Cretu1, Stéphane Bouchard2
1Department of Computer Science and Engineering, University of Quebec in Outaouais, Gatineau, QC J8Y 3G5, Canada.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
这项研究使用面部记录的深度视觉特征来预测虚拟现实 (VR) 中心理健康治疗的情绪状态. 机器学习模型在预测兴奋和价值方面取得了很高的准确性,这对于个性化的VR疗法至关重要.
科学领域:
- 计算机科学 计算机科学
- 心理学 心理学 心理学
- 人与计算机的交互
背景情况:
- 预测情绪状态对于个性化心理健康治疗至关重要.
- 环复合模型使用兴奋和价值维度对情绪进行分类.
- 虚拟现实 (VR) 为认知补救练习提供了一个平台.
研究的目的:
- 在VR系统中选择和整合机器学习模型来预测情绪状态 (兴奋和价值).
- 通过根据预测的情绪状态定制治疗来增强心理健康障碍患者的认知补救疗法.
主要方法:
- 使用RECOLA数据库,其中包含音频,视频和生理记录.
- 使用在面部记录上训练的MobileNet-v2卷积神经网络 (CNN) 提取深度视觉特征.
- 采用可优化的集体回归和融合特征/预测,用于增强兴奋和价值预测.
主要成果:
- 实现了0.1140的根平均平方误差 (RMSE) 和0.8000的皮尔森相关系数 (PCC) 来进行兴奋预测.
- 在价值预测方面获得了0.0790的RMSE和0.7904的PCC.
- 使用半面数据作为VR集成概念证明的成功预测.
结论:
- 通过CNN提取的深度视觉特征对于预测兴奋和价值是有效的.
- 开发的机器学习方法显示了VR治疗应用中实时情绪状态识别的前景.
- 准确的情绪状态预测可以显著提高基于VR的心理健康干预措施的个性化和有效性.
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