在唤起情绪的智能手机使用过程中可以检测到压力:使用机器学习的试点研究
Lydia Helene Rupp1, Akash Kumar2, Misha Sadeghi2
1Lehrstuhl für Klinische Psychologie und Psychotherapie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Frontiers in digital health
|May 15, 2025
概括
面部表情可以使用机器学习来预测压力水平. 这种无接触方法为传统的压力检测提供了一个有希望的替代方案,补充了现有的措施.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 生物医学工程 生物医学工程
背景情况:
- 压力检测对于及时干预至关重要,但目前的方法有局限性.
- 使用机器学习的非接触式传感为压力估计提供了潜在的替代方案.
- 之前的研究在从面部表情分类压力方面取得了很高的准确性,但经常使用诱导压力和分类方法.
研究的目的:
- 通过预测方法,调查是否可以从六种基本情绪的面部表情中检测压力和放松.
- 探索面部情绪表达和压力水平之间的关联.
- 为了比较随机森林和XGBoost回归模型的压力预测性能.
主要方法:
- 对69名参与者的面部情绪表情视频录制的二次分析.
- 通过感知压力量表 (PSS-10) 和一个单项压力指标来评估压力.
- 应用随机森林 (RF) 和XGBoost回归模型,从面部表情数据中预测压力得分.
主要成果:
- 面部情绪表达被发现是压力得分的有希望的指标.
- 当所有六种情绪表达的数据用于训练时,模型的性能是最佳的.
- 与RF相比,XGBoost在训练和测试数据方面表现出更高的可靠性和更低的预测误差.
结论:
- 面部表情的非侵入性视频录制可以有效地补充标准的客观和主观压力标记.
- 机器学习模型,特别是XGBoost,显示出从面部线索准确预测压力的潜力.
- 这项研究支持使用非接触式传感来检测和干预压力.
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