Enhancing stress detection: a comprehensive approach through rPPG analysis and deep learning techniques

Fontes, L ORCID: https://orcid.org/0000-0003-0171-7436, Machado, P ORCID: https://orcid.org/0000-0003-1760-3871, Vinkemeier, D ORCID: https://orcid.org/0000-0001-8767-4355, Yahaya, S ORCID: https://orcid.org/0000-0002-0394-6112, Bird, JJ ORCID: https://orcid.org/0000-0002-9858-1231 and Ihianle, IK ORCID: https://orcid.org/0000-0001-7445-8573, 2024. Enhancing stress detection: a comprehensive approach through rPPG analysis and deep learning techniques. Sensors, 24 (4): 1096. ISSN 1424-8220

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Abstract

Stress has emerged as a major concern in modern society, significantly impacting human health and well-being. Statistical evidence underscores the extensive social influence of stress, especially in terms of work-related stress and associated healthcare costs. This work addresses the critical need for accurate stress detection, emphasising its far-reaching effects on health and social dynamics. Focusing on remote stress monitoring, this work proposes an efficient deep learning framework to discern stress from facial videos. In contrast to research on wearable devices, this paper investigates the application of *dl for stress detection based on *rppg. The methodology involves selecting suitable *dl models (*lstm, *gru, *1dcnn), optimising hyperparameters, and investigating augmentation techniques. 1D-CNNv1 model achieved the best performance compared to other approaches, particularly with augmentation techniques such as linear interpolation and white noise, achieving a stress classification accuracy of 95.83% while maintaining excellent computational efficiency. The experimental results demonstrate the use of *dl for stress detection based on *rppg, with the potential to make significant contributions to the international standard in the field.

Item Type:	Journal article
Publication Title:	Sensors
Creators:	Fontes, L., Machado, P., Vinkemeier, D., Yahaya, S., Bird, J.J. and Ihianle, I.K.
Publisher:	MDPI
Date:	7 February 2024
Volume:	24
Number:	4
ISSN:	1424-8220
Identifiers:	Number Type 10.3390/s24041096 DOI 1858692 Other
Rights:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	06 Feb 2024 15:54
Last Modified:	13 Mar 2024 11:51
URI:	https://irep.ntu.ac.uk/id/eprint/50814

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