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

doi:10.1109/BSN56160.2022.9928495

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

Health Solutions, College of (CHS)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

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.

Original language	English (US)
Title of host publication	BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665487917
DOIs	https://doi.org/10.1109/BSN56160.2022.9928495
State	Published - 2022
Event	2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, BSN 2022 - Ioannina, Greece Duration: Sep 27 2022 → Sep 30 2022

Publication series

Name	BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings
Volume	2022-January

Conference

Conference	2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, BSN 2022
Country/Territory	Greece
City	Ioannina
Period	9/27/22 → 9/30/22

Keywords

alcohol use disorder
machine learning
mobile health
stress
wearable sensors

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Biomedical Engineering
Instrumentation

Access to Document

10.1109/BSN56160.2022.9928495

Cite this

Sah, R. K., McDonell, M., Pendry, P., Parent, S., Ghasemzadeh, H., & Cleveland, M. J. (2022). ADARP: A Multi Modal Dataset for Stress and Alcohol Relapse Quantification in Real Life Setting. In BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings (BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings; Vol. 2022-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BSN56160.2022.9928495

ADARP: A Multi Modal Dataset for Stress and Alcohol Relapse Quantification in Real Life Setting. / Sah, Ramesh Kumar; McDonell, Michael; Pendry, Patricia et al.
BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings; Vol. 2022-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sah, RK, McDonell, M, Pendry, P, Parent, S, Ghasemzadeh, H & Cleveland, MJ 2022, ADARP: A Multi Modal Dataset for Stress and Alcohol Relapse Quantification in Real Life Setting. in BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings. BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings, vol. 2022-January, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, BSN 2022, Ioannina, Greece, 9/27/22. https://doi.org/10.1109/BSN56160.2022.9928495

Sah RK, McDonell M, Pendry P, Parent S, Ghasemzadeh H, Cleveland MJ. ADARP: A Multi Modal Dataset for Stress and Alcohol Relapse Quantification in Real Life Setting. In BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. (BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings). doi: 10.1109/BSN56160.2022.9928495

Sah, Ramesh Kumar ; McDonell, Michael ; Pendry, Patricia et al. / ADARP : A Multi Modal Dataset for Stress and Alcohol Relapse Quantification in Real Life Setting. BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings).

@inproceedings{96e8e617a41944b8823621ee815b7a54,

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

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.",

keywords = "alcohol use disorder, machine learning, mobile health, stress, wearable sensors",

author = "Sah, {Ramesh Kumar} and Michael McDonell and Patricia Pendry and Sara Parent and Hassan Ghasemzadeh and Cleveland, {Michael J.}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, BSN 2022 ; Conference date: 27-09-2022 Through 30-09-2022",

year = "2022",

doi = "10.1109/BSN56160.2022.9928495",

language = "English (US)",

series = "BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings",

}

TY - GEN

T1 - ADARP

T2 - 2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, BSN 2022

AU - Sah, Ramesh Kumar

AU - McDonell, Michael

AU - Pendry, Patricia

AU - Parent, Sara

AU - Ghasemzadeh, Hassan

AU - Cleveland, Michael J.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - alcohol use disorder

KW - machine learning

KW - mobile health

KW - stress

KW - wearable sensors

UR - http://www.scopus.com/inward/record.url?scp=85152772017&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85152772017&partnerID=8YFLogxK

U2 - 10.1109/BSN56160.2022.9928495

DO - 10.1109/BSN56160.2022.9928495

M3 - Conference contribution

AN - SCOPUS:85152772017

T3 - BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings

BT - BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 27 September 2022 through 30 September 2022

ER -

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this