Li Ningyun, Zhang Huijun, Feng Ling, Ding Yang, Li Haichuan
Department of Computer Science and Technology, Tsinghua University, Beijing, 100084 China.
China Huaneng Clean Energy Research Institute, Beijing, 102209 China.
Health Inf Sci Syst. 2024 Mar 6;12(1):16. doi: 10.1007/s13755-024-00280-z. eCollection 2024 Dec.
Stress is a common problem globally. Prediction of stress in advance could help people take effective measures to manage stress before bad consequences occur. Considering the chaotic features of human psychological states, in this study, we integrate deep learning and chaos theory to address the stress prediction problem.
Based on chaos theory, we embed one's seemingly disordered stress sequence into a high dimensional phase space so as to reveal the underlying dynamics and patterns of the stress system, and meanwhile are able to identify the stress predictable time range. We then conduct deep learning with a two-layer (dimension and temporal) attention mechanism to simulate the nonlinear state of the embedded stress sequence for stress prediction.
We validate the effectiveness of the proposed method on the public available Tesserae dataset. The experimental results show that the proposed method outperforms the pure deep learning method and Chaos method in both 2-label and 3-label stress prediction.
Integrating deep learning and chaos theory for stress prediction is effective, and can improve the prediction accuracy over 2% and 8% more than those of the deep learning and the Chaos method respectively. Implications and further possible improvements are also discussed at the end of the paper.
压力是全球范围内的一个常见问题。提前预测压力可以帮助人们在不良后果出现之前采取有效措施来应对压力。考虑到人类心理状态的混沌特征，在本研究中，我们将深度学习与混沌理论相结合来解决压力预测问题。
基于混沌理论，我们将看似无序的压力序列嵌入到高维相空间中，以揭示压力系统潜在的动力学和模式，同时能够识别压力可预测的时间范围。然后，我们使用具有两层（维度和时间）注意力机制的深度学习来模拟嵌入的压力序列的非线性状态，以进行压力预测。
我们在公开可用的Tesserae数据集上验证了所提出方法的有效性。实验结果表明，在二分类和三分类压力预测中，所提出的方法均优于单纯的深度学习方法和混沌方法。
将深度学习与混沌理论相结合进行压力预测是有效的，并且分别比深度学习方法和混沌方法的预测准确率提高了2%以上和8%以上。本文末尾还讨论了其意义和进一步可能的改进。
