Machine Learning Approaches to Automatic Stress Detection: A Review

Sami Elzeiny; Marwa Qaraqe

doi:10.1109/AICCSA.2018.8612825

Machine Learning Approaches to Automatic Stress Detection: A Review

Sami Elzeiny, Marwa Qaraqe

CSE Information & Computing Technology

Hamad bin Khalifa University

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

36 Citations (Scopus)

Abstract

People experience mental stress on a daily basis from a variety of different reasons, including environmental reasons (traffic, noise, or bad weather), social reasons (family issues, friends, and financial problems), or from events such as wedding planning or giving a presentation in front of large audience. A manageable amount of stress is healthy and can motivate a person; however, a large amount of continuous stress or a strong response to stress can be harmful. For this reason, the detection of mental stress, as well as its prediction, has become a significant area of research. In this paper, we review and summarize various approaches found in the literature for stress detection using machine learning and suggest directions for future research and interventions.

Original language	English
Title of host publication	2018 IEEE/ACS 15th International Conference on Computer Systems and Applications, AICCSA 2018
Publisher	IEEE Computer Society
ISBN (Electronic)	9781538691205
DOIs	https://doi.org/10.1109/AICCSA.2018.8612825
Publication status	Published - 2 Jul 2018
Event	15th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2018 - Aqaba, Jordan Duration: 28 Oct 2018 → 1 Nov 2018

Publication series

Name	Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
Volume	2018-November
ISSN (Print)	2161-5322
ISSN (Electronic)	2161-5330

Conference

Conference	15th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2018
Country/Territory	Jordan
City	Aqaba
Period	28/10/18 → 1/11/18

Keywords

biosignal processing
features extraction
machine learning
stress detection
wearable sensing

Access to Document

10.1109/AICCSA.2018.8612825

Cite this

Elzeiny, S., & Qaraqe, M. (2018). Machine Learning Approaches to Automatic Stress Detection: A Review. In 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications, AICCSA 2018 Article 8612825 (Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA; Vol. 2018-November). IEEE Computer Society. https://doi.org/10.1109/AICCSA.2018.8612825

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title = "Machine Learning Approaches to Automatic Stress Detection: A Review",

abstract = "People experience mental stress on a daily basis from a variety of different reasons, including environmental reasons (traffic, noise, or bad weather), social reasons (family issues, friends, and financial problems), or from events such as wedding planning or giving a presentation in front of large audience. A manageable amount of stress is healthy and can motivate a person; however, a large amount of continuous stress or a strong response to stress can be harmful. For this reason, the detection of mental stress, as well as its prediction, has become a significant area of research. In this paper, we review and summarize various approaches found in the literature for stress detection using machine learning and suggest directions for future research and interventions.",

keywords = "biosignal processing, features extraction, machine learning, stress detection, wearable sensing",

author = "Sami Elzeiny and Marwa Qaraqe",

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year = "2018",

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doi = "10.1109/AICCSA.2018.8612825",

language = "English",

series = "Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA",

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Elzeiny, S & Qaraqe, M 2018, Machine Learning Approaches to Automatic Stress Detection: A Review. in 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications, AICCSA 2018., 8612825, Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA, vol. 2018-November, IEEE Computer Society, 15th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2018, Aqaba, Jordan, 28/10/18. https://doi.org/10.1109/AICCSA.2018.8612825

Machine Learning Approaches to Automatic Stress Detection: A Review. / Elzeiny, Sami; Qaraqe, Marwa.
2018 IEEE/ACS 15th International Conference on Computer Systems and Applications, AICCSA 2018. IEEE Computer Society, 2018. 8612825 (Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA; Vol. 2018-November).

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

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N2 - People experience mental stress on a daily basis from a variety of different reasons, including environmental reasons (traffic, noise, or bad weather), social reasons (family issues, friends, and financial problems), or from events such as wedding planning or giving a presentation in front of large audience. A manageable amount of stress is healthy and can motivate a person; however, a large amount of continuous stress or a strong response to stress can be harmful. For this reason, the detection of mental stress, as well as its prediction, has become a significant area of research. In this paper, we review and summarize various approaches found in the literature for stress detection using machine learning and suggest directions for future research and interventions.

AB - People experience mental stress on a daily basis from a variety of different reasons, including environmental reasons (traffic, noise, or bad weather), social reasons (family issues, friends, and financial problems), or from events such as wedding planning or giving a presentation in front of large audience. A manageable amount of stress is healthy and can motivate a person; however, a large amount of continuous stress or a strong response to stress can be harmful. For this reason, the detection of mental stress, as well as its prediction, has become a significant area of research. In this paper, we review and summarize various approaches found in the literature for stress detection using machine learning and suggest directions for future research and interventions.

KW - biosignal processing

KW - features extraction

KW - machine learning

KW - stress detection

KW - wearable sensing

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M3 - Conference contribution

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ER -

Machine Learning Approaches to Automatic Stress Detection: A Review

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