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

• URL: http://arxiv.org/abs/2401.05367v1
• Date: Thu, 14 Dec 2023 19:16:11 GMT
• Title: Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings
• Authors: Seyed Amir Hossein Aqajari, Sina Labbaf, Phuc Hoang Tran, Brenda Nguyen, Milad Asgari Mehrabadi, Marco Levorato, Nikil Dutt, Amir M. Rahmani
• Abstract summary: We present a monitoring system that objectively tracks daily stress levels by utilizing both physiological and contextual data. We propose a three-tier Internet-of-Things-based system architecture to address the challenges.
• Score: 2.650926942973848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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