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

TL;DR

This paper introduces ADARP, a new multi-modal dataset capturing physiological and self-report data in real-world settings for stress and alcohol relapse analysis, enabling improved research in health monitoring.

Contribution

The paper presents a novel real-world dataset, ADARP, with physiological and self-report data for stress and alcohol relapse, along with analysis and stress classification results.

Findings

Significant correlation between physiological data and self-reported outcomes.

Successful stress classification using the dataset.

Public availability of the ADARP dataset for research.

Abstract

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.