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arXiv · 2206.14568

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

Ramesh Kumar Sah·Michael McDonell·Patricia Pendry·Sara Parent·Hassan Ghasemzadeh·Michael J Cleveland
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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.

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Ramesh Kumar Sah, Michael McDonell, Patricia Pendry, Sara Parent, Hassan Ghasemzadeh, Michael J Cleveland. 2022-06-14. ADARP: A Multi Modal Dataset for Stress and Alcohol Relapse Quantification in Real Life Setting. https://arxiv.org/abs/2206.14568

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