ADARP: A Multi Modal Dataset for Stress and Alcohol Relapse Quantification in Real Life Setting
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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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Jun 2022.
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Citing papers per year: 2023: 3; 2024: 0; 2025: 1.
4 papers.
- 2023Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settingscited by 3
- 2023Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settingscited by 1
- 2023Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learningcited by 0
- 2025Evaluation of Stress Detection as Time Series Events -- A Novel Window-Based F1-Metriccited by 0
- 2025Evaluation of Stress Detection as Time Series Events -- A Novel Window-Based F1-Metric3 Sep 2025
- 2023Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings14 Dec 2023
- 2023Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning7 Nov 2023
- 2023Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings28 Apr 2023