Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned

The complexity of input features needed for machine-learning detection of stress severity based on multimodal data requires large sample sizes with wide variability of stress reactions and inputs among participants. These are difficult to recruit for laboratory setting, due to high time and effort demands on the side of both researcher and participant. Resources needed may be decreased using automatization of experimental procedures, which may, however, lead to additional technological challenges, potentially causing other recruitment setbacks. Further investigation is necessary, with the emphasis on quality ground truth, i.e., gold standard (self-report) instruments, but also outside of laboratory experiments, mainly in general populations and mental health care patients.

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Publication Details

Journal
Frontiers in Psychiatry
Published
2025-06-13
DOI
https://doi.org/10.3389/fpsyt.2025.1548287
Primary Topic
Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Type
article
Field-Weighted Citation Impact
0.00

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article

Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned

Martin Gevonden, Ward van Breda, Milou S. C. Sep, Sina Ghassemi et al.
1 links
Frontiers in Psychiatry
Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
article

Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned

Martin Gevonden, Ward van Breda, Milou S. C. Sep, Sina Ghassemi, Markéta Čihařová, Pim Cuijpers, Annet M. Kleiboer, Christiaan H. Vinkers, Alexander C. Cooper, Xianhua Peng, Heleen M. Riper, Khadicha Amarti, Eirini Karyotaki, Mieke H. J. Schulte, Sophia Trofimova
article en

Abstract

The complexity of input features needed for machine-learning detection of stress severity based on multimodal data requires large sample sizes with wide variability of stress reactions and inputs among participants. These are difficult to recruit for laboratory setting, due to high time and effort demands on the side of both researcher and participant. Resources needed may be decreased using automatization of experimental procedures, which may, however, lead to additional technological challenges, potentially causing other recruitment setbacks. Further investigation is necessary, with the emphasis on quality ground truth, i.e., gold standard (self-report) instruments, but also outside of laboratory experiments, mainly in general populations and mental health care patients.

Frontiers in PsychiatryVol. 16
Netherlands Institute for Neuroscience (NL), Tilburg University (NL), Babeș-Bolyai University (RO), Dordrecht Hospital (ZA), GGZ inGeest (NL), Vrije Universiteit Amsterdam (NL), University of Amsterdam (NL), Erasmus University Rotterdam (NL)
Interreg North-West Europe, European Commission, Interreg
Quality Education
Openalex Percentile: Top 86%
Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
DOIOpen Access PDFOpenAlex
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