preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202401.2121.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 30 January 2024 / Approved: 30 January 2024 / Online: 30 January 2024 (15:21:52 CET)

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.

Stress detection; Physiological signals; remote photoplethysmography (rPPG); Deep Learning (DL); Long Short-Term Memory (LSTM); Gated Recurrent Units (GRU); 1D Convolutional Neural Network (1D-CNN)

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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