从面部视频数据中检测压力的任务意识多个实例学习
Nele Sophie Brügge1, Alexandra Korda2, Heinz Handels3
1AI in Medical Image and Signal Processing, German Research Center for Artificial Intelligence, Ratzeburger Allee 160, Lübeck, 23562, Germany.
Journal of affective disorders
|February 22, 2026
概括
本研究介绍了一种新的基于视频的压力检测方法,使用top-k多重实例学习. 该方法通过分析混合行为来准确识别压力,为早期干预提供一种非侵入性的替代方案.
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:
- 压力是影响精神和身体健康的普遍问题.
- 早期发现压力对于及时干预和福祉至关重要.
- 当前的生理应激检测方法往往是侵入性的,并且无法扩展.
研究的目的:
- 提出一种非侵入性的,基于视频的压力检测方法.
- 利用多个实例学习来提高压力检测的准确性.
- 开发一种适用于连续和大规模压力监测的方法.
主要方法:
- 利用了top-k多个实例学习,具有时间特征网络和多头注意力.
- 集成了一个适用于主动和被动任务的调节机制.
- 包括top-k和bottom-k实例来使用有限的数据集和弱标签.
主要成果:
- 在定制和公共数据集 (StressID) 上获得了高准确度和F1分数.
- 在一个离开五个受试者的评估中超越了基线方法.
- 证明了可以解释的时间局部化压力指示行为.
结论:
- 拟议的基于视频的方法为压力检测提供了一种有效的,非侵入性的方法.
- 顶级k多个实例学习框架通过考虑各种行为模式来提高准确性.
- 这种方法对可扩展和持续的压力监测应用具有前景.
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