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

arXiv: https://arxiv.org/abs/2401.05367 · HTML: https://mlsanity.com/paper/2401.05367/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 14 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [TILES-2018, a longitudinal physiologic and behavioral data set of hospital workers](https://mlsanity.com/paper/2003.08474/) (2020)
- [Personalized Stress Monitoring using Wearable Sensors in Everyday Settings](https://mlsanity.com/paper/2108.00144/) (2021)
- [Semi-Supervised Learning and Data Augmentation in Wearable-based Momentary Stress Detection in the Wild](https://mlsanity.com/paper/2202.12935/) (2022)
- [ADARP: A Multi Modal Dataset for Stress and Alcohol Relapse Quantification in Real Life Setting](https://mlsanity.com/paper/2206.14568/) (2022)

## Built on by (most cited first, 1 of 1)

- [Protecting Student Mental Health with a Context-Aware Machine Learning Framework for Stress Monitoring](https://mlsanity.com/paper/2508.01105/) (2025)

## Abstract (arXiv)

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.