# Enhancing Performance and User Engagement in Everyday Stress Monitoring: A Context-Aware Active Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2407.08215 · HTML: https://mlsanity.com/paper/2407.08215/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 58 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection](https://mlsanity.com/paper/2402.10862/) (2024)
- [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)
- [ZotCare: A Flexible, Personalizable, and Affordable mHealth Service Provider](https://mlsanity.com/paper/2307.01905/) (2023)
- [Efflex: Efficient and Flexible Pipeline for Spatio-Temporal Trajectory Graph Modeling and Representation Learning](https://mlsanity.com/paper/2404.12400/) (2024)
- [Edge-centric Optimization of Multi-modal ML-driven eHealth Applications](https://mlsanity.com/paper/2208.02597/) (2022)
- [Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings](https://mlsanity.com/paper/2305.00111/) (2023)
- [VeTraSS: Vehicle Trajectory Similarity Search Through Graph Modeling and Representation Learning](https://mlsanity.com/paper/2404.08021/) (2024)

## Built on by (most cited first, 7 of 7)

- [HealthQ: Unveiling Questioning Capabilities of LLM Chains in Healthcare Conversations](https://mlsanity.com/paper/2409.19487/) (2024)
- [Towards Foundation-model-based Multiagent System to Accelerate AI for Social Impact](https://mlsanity.com/paper/2412.07880/) (2024)
- [ECG Unveiled: Analysis of Client Re-identification Risks in Real-World ECG Datasets](https://mlsanity.com/paper/2408.10228/) (2024)
- [Linkage Attacks Expose Identity Risks in Public ECG Data Sharing](https://mlsanity.com/paper/2508.15850/) (2025)
- [TransECG: Leveraging Transformers for Explainable ECG Re-identification Risk Analysis](https://mlsanity.com/paper/2503.13495/) (2025)
- [CARE-ECG: Causal Agent-based Reasoning for Explainable and Counterfactual ECG Interpretation](https://mlsanity.com/paper/2604.10420/) (2026)
- [PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning](https://mlsanity.com/paper/2605.07267/) (2026)

## Abstract (arXiv)

In today's fast-paced world, accurately monitoring stress levels is crucial. Sensor-based stress monitoring systems often need large datasets for training effective models. However, individual-specific models are necessary for personalized and interactive scenarios. Traditional methods like Ecological Momentary Assessments (EMAs) assess stress but struggle with efficient data collection without burdening users. The challenge is to timely send EMAs, especially during stress, balancing monitoring efficiency and user convenience. This paper introduces a novel context-aware active reinforcement learning (RL) algorithm for enhanced stress detection using Photoplethysmography (PPG) data from smartwatches and contextual data from smartphones. Our approach dynamically selects optimal times for deploying EMAs, utilizing the user's immediate context to maximize label accuracy and minimize intrusiveness. Initially, the study was executed in an offline environment to refine the label collection process, aiming to increase accuracy while reducing user burden. Later, we integrated a real-time label collection mechanism, transitioning to an online methodology. This shift resulted in an 11% improvement in stress detection efficiency. Incorporating contextual data improved model accuracy by 4%. Personalization studies indicated a 10% enhancement in AUC-ROC scores, demonstrating better stress level differentiation. This research marks a significant move towards personalized, context-driven real-time stress monitoring methods.