Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings
This work addresses the challenge of continuous stress detection for individuals in daily life, though it is incremental as it builds on existing methods by integrating contextual data and optimizing labeling.
The paper tackled real-time stress monitoring in everyday settings by developing a system that combines physiological (PPG) and contextual data with a smart labeling approach for EMA collection, achieving an F1-score of 70% with a Random Forest classifier, compared to 56% using PPG data alone.
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.