基于PPG的增强压力识别:一种转移学习方法来处理内部与外部压力
Guodong Liang1, Han Chen1, Xiaofen Xing1
1South China University of Technology, Guangzhou, China, Guangzhou, Guangzhou, Guangdong, 511442, CHINA.
Physiological measurement
|November 25, 2025
概括
这项研究引入了一套新的数据集和深度学习模型,用于使用光聚缩图 (PPG) 信号自动识别压力. 这些方法有效地区分内部和外部压力,提高压力检测的准确性.
科学领域:
- 生理学 生理学 生理学
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 压力识别对于心理和身体健康监测至关重要.
- 现有的方法很难区分各种类型的压力.
- 摄影发电图 (PPG) 信号提供了一种非侵入性的生理监测方法.
研究的目的:
- 为内部和外部压力评估创建一个全面的数据集.
- 开发使用PPG信号的自动压力识别方法.
- 提高压力检测模型的准确性和稳定性.
主要方法:
- 建立了内部和外部压力数据集 (IESD),其中包括来自107名参与者的PPG信号.
- 使用域对抗神经网络 (DANN) 和最大平均差异 (MMD) 开发了一个转移学习模型.
- 采用深度特征提取来提高识别性能.
主要成果:
- 在内部和外部压力范式之间的心率变化 (HRV) 中发现了显著差异.
- 在识别同源应力方面取得了高精度 (例如,从TSST到ST的73.86%).
- 在不同的压力环境中表现出更好的识别性能和稳定性.
结论:
- 该IESD和拟议的深度学习模型增强了自动化压力检测能力.
- 该方法有效地区分了内部和外部压力.
- 深度学习显著提高了对未来应用的压力识别准确性.
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