Chronic stress can significantly affect physical and mental health. The advent of wearable technology allows for the tracking of physiological signals, potentially leading to innovative stress prediction and intervention methods. However, challenges such as label scarcity and data heterogeneity render stress prediction difficult in practice. To counter these issues, we have developed a multimodal personalized stress prediction system using wearable biosignal data. We employ self-supervised learning (SSL) to pre-train the models on each subject's data, allowing the models to learn the baseline dynamics of the participant's biosignals prior to fine-tuning the stress prediction task. We test our model on the Wearable Stress and Affect Detection (WESAD) dataset, demonstrating that our SSL models outperform non-SSL models while utilizing less than 5% of the annotations. These results suggest that our approach can personalize stress prediction to each user with minimal annotations. This paradigm has the potential to enable personalized prediction of a variety of recurring health events using complex multimodal data streams.

翻译：慢性压力会显著影响身心健康。可穿戴技术的出现使得追踪生理信号成为可能，有望带来创新的压力预测与干预方法。然而，标签稀缺和数据异质性等挑战使得压力预测在实践中困难重重。为解决这些问题，我们利用可穿戴生物信号数据开发了一种多模态个性化压力预测系统。我们采用自监督学习（SSL）对每个受试者的数据进行模型预训练，使模型在精调压力预测任务前能够学习参与者生物信号的基线动态。我们在可穿戴压力与情感检测（WESAD）数据集上测试了模型，结果表明，在仅使用不到5%标注数据的情况下，我们的SSL模型性能优于非SSL模型。这些发现表明，该方法能够在最少标注条件下实现针对每位用户的个性化压力预测。这一范式有望通过复杂的多模态数据流实现对多种复发性健康事件的个性化预测。