# ADARP: A Multi Modal Dataset for Stress and Alcohol Relapse Quantification in Real Life Setting

arXiv: https://arxiv.org/abs/2206.14568 · HTML: https://mlsanity.com/paper/2206.14568/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2022.

## Built on by (most cited first, 4 of 4)

- [Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings](https://mlsanity.com/paper/2305.00111/) (2023)
- [Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings](https://mlsanity.com/paper/2401.05367/) (2023)
- [Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning](https://mlsanity.com/paper/2311.04215/) (2023)
- [Evaluation of Stress Detection as Time Series Events -- A Novel Window-Based F1-Metric](https://mlsanity.com/paper/2509.03240/) (2025)

## Abstract (arXiv)

Stress detection and classification from wearable sensor data is an emerging area of research with significant implications for individuals' physical and mental health. In this work, we introduce a new dataset, ADARP, which contains physiological data and self-report outcomes collected in real-world ambulatory settings involving individuals diagnosed with alcohol use disorders. We describe the user study, present details of the dataset, establish the significant correlation between physiological data and self-reported outcomes, demonstrate stress classification, and make our dataset public to facilitate research.