Daily monitoring of stress is a critical component of maintaining optimal
physical and mental health. Physiological signals and contextual information
have recently emerged as promising indicators for detecting instances of
heightened stress. Nonetheless, developing a real-time monitoring system that
utilizes both physiological and contextual data to anticipate stress levels in
everyday settings while also gathering stress labels from participants
represents a significant challenge. We present a monitoring system that
objectively tracks daily stress levels by utilizing both physiological and
contextual data in a daily-life environment. Additionally, we have integrated a
smart labeling approach to optimize the ecological momentary assessment (EMA)
collection, which is required for building machine learning models for stress
detection. We propose a three-tier Internet-of-Things-based system architecture
to address the challenges. We utilized a cross-validation technique to
accurately estimate the performance of our stress models. We achieved the
F1-score of 70\% with a Random Forest classifier using both PPG and contextual
data, which is considered an acceptable score in models built for everyday
settings. Whereas using PPG data alone, the highest F1-score achieved is
approximately 56\%, emphasizing the significance of incorporating both PPG and
contextual data in stress detection tasks.

每日监测的紧迫性与重要性日益凸显，本研究提出了一种利用生理信号和环境信息的实时监测系统来追踪和预测每日压力水平的方法，并通过集成智能标签方法来优化压力检测的机器学习模型。实验结果显示，利用脉搏波图和环境信息结合的随机森林分类器，F1 得分可达到 70%，较仅使用脉搏波图的 56% 得分更为显著。