Stress is a pervasive issue that affects individuals’ physical and mental health. Consequently, daily monitoring of stress levels has become increasingly important for maintaining overall well-being. Recent advancements in technology have allowed for the integration of physiological signals and contextual information to detect instances of heightened stress. However, creating a real-time monitoring system that effectively utilizes both types of data and collects stress labels from participants poses a significant challenge.

The Monitoring System

In this study, the researchers developed a monitoring system that tracks daily stress levels by combining physiological data and contextual information in everyday environments. To enhance the accuracy of stress detection, they integrated a smart labeling approach called ecological momentary assessment (EMA) collection. EMA involves gathering real-time data from participants to build machine learning models for stress detection.

The Three-Tier Internet-of-Things Architecture

To address the challenges of integrating physiological and contextual data, the researchers proposed a three-tier Internet-of-Things (IoT)-based system architecture. This architecture allows for the seamless collection, processing, and analysis of data from various sources. By leveraging the power of IoT, the system can optimize stress monitoring in real-time.

Performance Evaluation

The researchers utilized a cross-validation technique to accurately estimate the performance of their stress models. They achieved an F1-score of 70% using a Random Forest classifier that incorporated both photoplethysmography (PPG) and contextual data. It is considered an acceptable score for models built for everyday settings. In comparison, using PPG data alone, the highest F1-score achieved was approximately 56%, highlighting the importance of incorporating both PPG and contextual information in stress detection tasks.

Expert Insights

This study highlights the potential of combining physiological signals and contextual information for accurate stress detection in everyday settings. By integrating IoT technology, the researchers were able to develop a monitoring system that tracks stress levels in real-time. The use of a smart labeling approach, EMA, further enhances the system’s performance.

The achieved F1-score of 70% demonstrates the effectiveness of the proposed system, especially when compared to using only physiological data. This suggests that contextual information plays a crucial role in accurately detecting stress levels. Future research could explore additional contextual factors that may influence stress and further improve the system’s performance.

Overall, this study contributes to the growing body of research on stress monitoring and highlights the potential of IoT and machine learning in addressing mental health challenges. As technology continues to advance, we can expect further enhancements in real-time stress monitoring, leading to improved interventions and support for individuals managing stress in their daily lives.

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