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.
@article{arxiv.2206.14568,
title = {ADARP: A Multi Modal Dataset for Stress and Alcohol Relapse Quantification in Real Life Setting},
author = {Ramesh Kumar Sah and Michael McDonell and Patricia Pendry and Sara Parent and Hassan Ghasemzadeh and Michael J Cleveland},
journal= {arXiv preprint arXiv:2206.14568},
year = {2022}
}