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

ABSTRACT: 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 ob

SUBMITTER: M Khayyat M 

PROVIDER: S-EPMC11651562 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature