通过空间和道注意力机制进行压力识别和分类的增强信息网络
Rui Guo1, Beni Widarman Yus Kelana1, Eman Safar Almetere2
1Azman Hashim International Business School, Universiti Teknologi Malaysia, Skudai Johor 81300, Malaysia.
International journal of neural systems
|December 30, 2025
概括
本研究介绍了一种增强的深度学习方法,用于使用空间和道注意力机制来识别职业压力. 该模型准确地识别了微妙的压力模式,在压力分类中表现优于现有的方法.
科学领域:
- 职业健康 职业健康 职业健康 职业健康
- 心理压力检测 心理压力检测
- 深度学习应用程序深度学习应用程序
背景情况:
- 与工作相关的压力日益增加,需要先进的方法来检测心理压力.
- 目前的注意力增强模型在捕捉微妙的压力指标方面存在局限性.
研究的目的:
- 为压力识别和分类提出一个增强的Informer深度学习模型.
- 整合空间和道注意力机制 (SAM/CAM) 以提高性能.
主要方法:
- 开发了一个增强的Informer模型,包含了定制的SAM和CAM.
- SAM优先考虑时间敏感的生理数据,而CAM重视与压力相关的特征.
- 在公共数据集上对压力识别模型进行了评估.
主要成果:
- 拟议的方法在准确性,回忆力和F1分数方面明显优于现有的方法.
- 废除研究证实了SAM和CAM的有效性.
- 该模型准确地捕捉了与压力状态相关的微妙变化.
结论:
- 该研究提出了一种有效的深度学习方法,用于自动检测心理压力.
- 这项工作为使用深度学习的更广泛的健康监测应用提供了基础.
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