Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings

TL;DR

This paper presents a real-time, IoT-based stress monitoring system that combines physiological and contextual data, achieving a 70% F1-score in daily-life stress detection, with an innovative labeling approach for model training.

Contribution

It introduces a novel three-tier IoT architecture for stress monitoring that integrates physiological and contextual data, along with a smart EMA labeling method for improved machine learning model accuracy.

Findings

F1-score of 70% using combined PPG and contextual data

F1-score of approximately 56% using PPG data alone

Demonstrates the importance of multi-modal data in stress detection

Abstract

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.