Machine Learning Based Solutions for Real-Time Stress Monitoring

Rajdeep Kumar Nath, Himanshu Thapliyal, Allison Caban-Holt, Saraju P. Mohanty

Research output: Contribution to specialist publicationArticle

43 Scopus citations

Abstract

Stress may be defined as the reaction of the body to regulate itself to changes within the environment through mental, physical, or emotional responses. Recurrent episodes of acute stress can disturb the physical and mental stability of a person. This subsequently can have a negative effect on work performance and in the long term can increase the risk of physiological disorders like hypertension and psychological illness such as anxiety disorder. Psychological stress is a growing concern for the worldwide population across all age groups. A reliable, cost-efficient, acute stress detection system could enable its users to better monitor and manage their stress to mitigate its long-term negative effects. In this article, we will review and discuss the literature that has used machine learning based approaches for stress detection. We will also review the existing solutions in the literature that have leveraged the concept of edge computing in providing a potential solution in real-time monitoring of stress.

Original languageEnglish
Pages34-41
Number of pages8
Volume9
No5
Specialist publicationIEEE Consumer Electronics Magazine
DOIs
StatePublished - Sep 1 2020

Bibliographical note

Publisher Copyright:
© 2012 IEEE.

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Science Applications
  • Electrical and Electronic Engineering

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