Acta Psychol (Amst). 2024 Dec 7;251:104645. doi: 10.1016/j.actpsy.2024.104645. Online ahead of print.

ABSTRACT

Stress is one of the most pressing problems in society as it severely reduces the physical and mental wellbeing of people. It is therefore of great importance to accurately monitor stress levels, especially in work environments. However, contemporary stress assessments, such as questionnaires and physiological measurements, have practical limitations, mostly related to their subjective or contact-based nature. To assess stress objectively and conveniently, we developed an automated model that detects biomarkers in webcam-recorded facial behavior indicative of heightened stress levels, using computer vision, artificial intelligence, and machine learning techniques. Heart-rate induced skin pulsations and facial muscle activity were extracted from videos of 264 participants that performed an online mental capacity test under considerable time pressure. The model could successfully use these facial biomarkers to explain a significant proportion of individual differences in scores on a self-perceived stress scale. Next, we used the model to objectively score stress levels of 63 military candidates (pre-hiring) and 69 military personnel (post-hiring) that also performed the mental capacity test. Results showed that military personnel expressed facial behavior indicative of significantly higher stress levels than military candidates. This suggests that joining the military heightens overall stress levels. With this study we take the first steps towards a non-contact, automated, and objective measure of stress that is easily applicable in a variety of health and work contexts.

PMID:39647451 [https://pubmed.ncbi.nlm.nih.gov/39647451/?utm_source=Other&utm_medium=rss&utm_campaign=pubmed-2&utm_content=18Cx71VHHP3XUFm_UtwWKCRHiwab6gnVsoJL5Op9oVpDIYQURc&fc=20241127081157&ff=20241209020157&v=2.18.0.post9+e462414] | DOI:10.1016/j.actpsy.2024.104645 [https://doi.org/10.1016/j.actpsy.2024.104645]