An improved biometric stress monitoring solution for working employees using heart rate variability data and Capsule Network model

Biometric stress monitoring has become a critical area of research in understanding and managing health problems resulting from stress. One of the fields that emerged in this area is biometric stress monitoring, which provides continuous or real-time information about different anxiety levels among...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2024-12, Vol.19 (12), p.e0310776
Main Authors: M Khayyat, Mashael, Munshi, Raafat M, Alabduallah, Bayan, Lamoudan, Tarik, Ghith, Ehab, Kim, Tai-Hoon, A Abdelhamid, Abdelaziz
Format: Article
Language:English
Subjects:
Accuracy
Adult
Algorithms
Anxiety
Biology and Life Sciences
Biometrics
Biometry - methods
Comparative analysis
Computational linguistics
Computer and Information Sciences
Data analysis
Datasets
Electrocardiography
Employees
Health problems
Heart beat
Heart rate
Heart Rate - physiology
Hormones
Humans
Hypothalamus
Language processing
Machine learning
Male
Medicine and Health Sciences
Mental depression
Mental disorders
Mental health
Monitoring, Physiologic - methods
Natural language interfaces
Nervous system
Neural networks
Occupational stress
Physiology
Real time
Research and Analysis Methods
Social Sciences
Stress
Stress, Psychological - physiopathology
Students
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Biometric stress monitoring has become a critical area of research in understanding and managing health problems resulting from stress. One of the fields that emerged in this area is biometric stress monitoring, which provides continuous or real-time information about different anxiety levels among people by analyzing physiological signals and behavioral data. In this paper, we propose a new approach based on the CapsNets model for continuously monitoring psychophysiological stress. In the new model, streams of biometric data, including physiological signals and behavioral patterns, are taken up for analysis. In testing using the Swell multiclass dataset, it performed with an accuracy of 92.76%. Further testing of the WESAD dataset reveals an even better accuracy at 96.76%. The accuracy obtained for binary classification of stress and no stress class is applied to the Swell dataset, where this model obtained an outstanding accuracy of 98.52% in this study and on WESAD, 99.82%. Comparative analysis with other state-of-the-art models underlines the superior performance; it achieves better results than all of its competitors. The developed model is then rigorously subjected to 5-fold cross-validation, which proved very significant and proved that the proposed model could be effective and efficient in biometric stress monitoring.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0310776