With the development of wearable technologies, a new kind of healthcare data has become valuable as medical information. These data provide meaningful information regarding an individual's physiological and psychological states, such as activity level, mood, stress, and cognitive health. These biomarkers are named digital since they are collected from digital devices integrated with various sensors. In this study, we explore digital biomarkers related to stress modality by examining data collected from mobile phones and smartwatches. We utilize machine learning techniques on the Tesserae dataset, precisely Random Forest, to extract stress biomarkers. Using feature selection techniques, we utilize weather, activity, heart rate (HR), stress, sleep, and location (work-home) measurements from wearables to determine the most important stress-related biomarkers. We believe we contribute to interpreting stress biomarkers with a high range of features from different devices. In addition, we classify the $5$ different stress levels with the most important features, and our results show that we can achieve $85\%$ overall class accuracy by adjusting class imbalance and adding extra features related to personality characteristics. We perform similar and even better results in recognizing stress states with digital biomarkers in a daily-life scenario targeting a higher number of classes compared to the related studies.

翻译：随着可穿戴技术的发展，一类新型医疗数据已成为具有价值的医学信息。这些数据提供了关于个体生理与心理状态（如活动水平、情绪状态、压力水平及认知健康）的有意义信息。由于这些生物标志物是通过集成多种传感器的数字设备采集而来，故被称为数字生物标志物。本研究通过分析从智能手机和智能手表采集的数据，探索与压力模态相关的数字生物标志物。我们利用Tesserae数据集中的机器学习技术（具体采用随机森林算法）来提取压力生物标志物。通过特征选择技术，我们利用可穿戴设备获取的天气、活动、心率、压力、睡眠及位置（工作-家庭）等测量数据，以确定最重要的压力相关生物标志物。我们认为本研究在从不同设备中提取大范围特征来解释压力生物标志物方面做出了贡献。此外，我们利用最重要的特征对5个不同压力等级进行分类，结果表明，通过调整类别不平衡并增加与人格特征相关的额外特征，我们能够实现85%的总体类别准确率。在与相关研究的对比中，我们在面向更多类别的日常场景下，利用数字生物标志物识别压力状态时取得了相仿甚至更优的结果。