# Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings

arXiv: https://arxiv.org/abs/2305.00111 · HTML: https://mlsanity.com/paper/2305.00111/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 28 Apr 2023.

## Builds on (most cited first, 7 of 7)

- [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)
- [Detection of Maternal and Fetal Stress from the Electrocardiogram with Self-Supervised Representation Learning](https://mlsanity.com/paper/2011.02000/) (2020)
- [Semi-Supervised Learning and Data Augmentation in Wearable-based Momentary Stress Detection in the Wild](https://mlsanity.com/paper/2202.12935/) (2022)
- [Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits](https://mlsanity.com/paper/1410.5816/) (2014)
- [ADARP: A Multi Modal Dataset for Stress and Alcohol Relapse Quantification in Real Life Setting](https://mlsanity.com/paper/2206.14568/) (2022)
- [A Contextual Bandit Approach for Stream-Based Active Learning](https://mlsanity.com/paper/1701.06725/) (2017)

## Built on by (most cited first, 3 of 3)

- [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)
- [Enhancing Performance and User Engagement in Everyday Stress Monitoring: A Context-Aware Active Reinforcement Learning Approach](https://mlsanity.com/paper/2407.08215/) (2024)
- [Sense Less, Infer More: Agentic Multimodal Transformers for Edge Medical Intelligence](https://mlsanity.com/paper/2604.10404/) (2026)

## Abstract (arXiv)

Most existing sensor-based monitoring frameworks presume that a large available labeled dataset is processed to train accurate detection models. However, in settings where personalization is necessary at deployment time to fine-tune the model, a person-specific dataset needs to be collected online by interacting with the users. Optimizing the collection of labels in such phase is instrumental to impose a tolerable burden on the users while maximizing personal improvement. In this paper, we consider a fine-grain stress detection problem based on wearable sensors targeting everyday settings, and propose a novel context-aware active learning strategy capable of jointly maximizing the meaningfulness of the signal samples we request the user to label and the response rate. We develop a multilayered sensor-edge-cloud platform to periodically capture physiological signals and process them in real-time, as well as to collect labels and retrain the detection model. We collect a large dataset and show that the context-aware active learning technique we propose achieves a desirable detection performance using 88% and 32% fewer queries from users compared to a randomized strategy and a traditional active learning strategy, respectively.