通过生理和视觉数据预测持续的情绪测量
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 8, 2023
概括
这项研究使用生理和视频数据预测情绪状态,达到高准确度. 这些模型可以为心理健康治疗个性化虚拟现实,提高用户参与度.
科学领域:
- 情感计算是一种情感计算.
- 人与计算机的互动.
- 机器学习用于心理健康
背景情况:
- 情感状态预测对于个性化数字体验至关重要.
- 以前的工作重点是物理数据,如皮电活动 (EDA) 和心电图 (ECG).
- 虚拟现实 (VR) 提供了认知补救的潜力,但需要适应性环境来防止用户丧.
研究的目的:
- 从生理和视频数据中预测兴奋和价值值值.
- 开发适应性VR环境,用于心理健康障碍的认知补救.
- 通过增强预处理和纳入新型特征选择和决策融合来改进预测模型.
主要方法:
- 使用生理学 (EDA,ECG) 和视频记录作为数据来源.
- 实施了先进的机器学习模型,与精细的预处理步骤相结合.
- 整合了新的特征选择和决策融合技术.
- 对RECOLA数据集的方法进行了评估.
主要成果:
- 通过使用生理数据,实现了高预测准确度,共相关系数 (CCC) 为唤起0.996,价值系数为0.998.
- 在RECOLA数据集上表现优于现有的最先进方法.
- 证明了结合多个数据源和先进机器学习的有效性.
结论:
- 先进的机器学习技术可以准确地从各种数据中预测情绪状态.
- 开发的模型显示了个性化VR环境的巨大潜力,用于心理健康应用.
- 这种方法可以通过调整VR体验以适应用户的情绪状态来增强认知补救,减少丧.
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