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
July 2022
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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.

Type
Conference paper
Publication
IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2022
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Ramesh Kumar Sah
Ramesh Kumar Sah
Research Assistant

I am a Computer Science PhD student in the Embedded Machine Intelligence Laboratory (EMIL) at Washington State University, Pullman.

Hassan Ghasemzadeh
Hassan Ghasemzadeh
Director

Hassan Ghasemzadeh is an Associate Professor of Biomedical Informatics at Arizona State University (ASU) and a Computer Science Adjunct Faculty at Washington State University (WSU).