# Personalized Stress Monitoring using Wearable Sensors in Everyday Settings

arXiv: https://arxiv.org/abs/2108.00144 · HTML: https://mlsanity.com/paper/2108.00144/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 6 references that are in the map.
- First posted on arXiv 31 Jul 2021.

## Built on by (most cited first, 10 of 10)

- [Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data](https://mlsanity.com/paper/2401.06866/) (2024)
- [Enhancing Performance and User Engagement in Everyday Stress Monitoring: A Context-Aware Active Reinforcement Learning Approach](https://mlsanity.com/paper/2407.08215/) (2024)
- [Neural-Driven Image Editing](https://mlsanity.com/paper/2507.05397/) (2025)
- [Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings](https://mlsanity.com/paper/2305.00111/) (2023)
- [AMSER: Adaptive Multi-modal Sensing for Energy Efficient and Resilient eHealth Systems](https://mlsanity.com/paper/2112.08176/) (2021)
- [Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings](https://mlsanity.com/paper/2401.05367/) (2023)
- [Human Heterogeneity Invariant Stress Sensing](https://mlsanity.com/paper/2506.02256/) (2025)
- [Stress Monitoring in Healthcare: An Ensemble Machine Learning Framework Using Wearable Sensor Data](https://mlsanity.com/paper/2507.07589/) (2025)
- [Improving Engagement and Efficacy of mHealth Micro-Interventions for Stress Coping: an In-The-Wild Study](https://mlsanity.com/paper/2407.11612/) (2024)
- [Personalized Digital Health Modeling with Adaptive Support Users](https://mlsanity.com/paper/2605.02004/) (2026)

## Abstract (arXiv)

Since stress contributes to a broad range of mental and physical health problems, the objective assessment of stress is essential for behavioral and physiological studies. Although several studies have evaluated stress levels in controlled settings, objective stress assessment in everyday settings is still largely under-explored due to challenges arising from confounding contextual factors and limited adherence for self-reports. In this paper, we explore the objective prediction of stress levels in everyday settings based on heart rate (HR) and heart rate variability (HRV) captured via low-cost and easy-to-wear photoplethysmography (PPG) sensors that are widely available on newer smart wearable devices. We present a layered system architecture for personalized stress monitoring that supports a tunable collection of data samples for labeling, and present a method for selecting informative samples from the stream of real-time data for labeling. We captured the stress levels of fourteen volunteers through self-reported questionnaires over periods of between 1-3 months, and explored binary stress detection based on HR and HRV using Machine Learning Methods. We observe promising preliminary results given that the dataset is collected in the challenging environments of everyday settings. The binary stress detector is fairly accurate and can detect stressful vs non-stressful samples with a macro-F1 score of up to %76. Our study lays the groundwork for more sophisticated labeling strategies that generate context-aware, personalized models that will empower health professionals to provide personalized interventions.