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

Aug 5, 2026

Early detection of elevated acute stress is critical to reduce harm from prolonged or recurrent stress exposure. This work develops machine-learning methods to detect stress severity on a continuous scale by integrating multi-modal signals spanning acoustic, verbal, visual, and physiological data. The study situates its approach within open-access publication practices and provides peer-reviewed research metadata, aiming to improve real-world stress monitoring and contribute lessons learned from model development and deployment.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Background

Rationale for early detection of acute stress

Methodological scope

Multi-modal inputs: acoustic, verbal, visual, physiological data

Publication details

Open access and research metadata

EUR Research Information Portal Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data Published in: Frontiers in Psychiatry Publication status and date: Published: 13/06/2025 DOI (link to publisher): 10.3389/fpsyt.2025.1548287 Document Version Publisher's PDF, also known as Version of record Document License/Available under: CC BY Citation for the published version (APA): Ciharova, M. , Amarti, K. , van Breda, W. , Gevonden, M. J. , Ghassemi, S. , Kleiboer, A. , Vinkers, C. H. , Sep, M. S. C. , Trofimova, S. , Cooper, A. C. , Peng, X. , Schulte, M. , Karyotaki, E. , Cuijpers, P. , & Riper, H. (2025) . Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned. Frontiers in Psychiatry, 16, Article 1548287. [https://doi.org/10.3389/fpsyt.2025.1548287](https://doi.org/10.3389/fpsyt.2025.1548287) Link to publication on the EUR Research Information Portal Terms and Conditions of Use Except as permitted by the applicable copyright law, you may not reproduce or make this material available to any third party without the prior written permission from the copyright holder(s) . Copyright law allows the following uses of this material without prior permission: • you may download, save and print a copy of this material for your personal use only; • you may share the EUR portal link to this material. In case the material is published with an open access license (e.g. a Creative Commons (CC) license), other uses may be allowed. Please check the terms and conditions of the specific license. Take-down policy If you believe that this material infringes your copyright and/or any other intellectual property rights, you may request its removal by contacting us at the following email address: [openaccess.library@eur.nl. Please](openaccess.library@eur.nl. Please) provide us with all the relevant information, including the reasons why you believe any of your rights have been infringed. In case of a legitimate complaint, we will make the material inaccessible and/or remove it from the website. TYPE Original Research PUBLISHED 13 June 2025 DOI 10.3389/fpsyt.2025.1548287 OPEN ACCESS EDITED BY Michael Patrick Schaub, University of Zurich, Switzerland REVIEWED BY Rüdiger Christoph Pryss, Julius Maximilian University of Würzburg, Germany Colin K Drummond, Case Western Reserve University, United States *CORRESPONDENCE Marketa Ciharova [m.ciharova@vu.nl](m.ciharova@vu.nl) RECEIVED 19 December 2024 ACCEPTED 20 May 2025 PUBLISHED 13 June 2025 CITATION Ciharova M, Amarti K, van Breda W, Gevonden MJ, Ghassemi S, Kleiboer A, Vinkers CH, Sep MSC, Troﬁmova S, Cooper AC, Peng X, Schulte M, Karyotaki E, Cuijpers P and Riper H (2025) Machinelearning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned. Front. Psychiatry 16:1548287 . doi: 10.3389/fpsyt.2025.1548287 COPYRIGHT © 2025 Ciharova, Amarti, van Breda, Gevonden, Ghassemi, Kleiboer, Vinkers, Sep, Troﬁmova, Cooper, Peng, Schulte, Karyotaki, Cuijpers and Riper. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned Marketa Ciharova 1*, Khadicha Amarti 1, Ward van Breda 2,3, Martin J. Gevonden 4, Sina Ghassemi 5, Annet Kleiboer 1, Chri

FAQs

Why is early detection of acute stress important in this research?

Early detection of elevated acute stress is necessary to reduce consequences linked to prolonged or recurrent stress exposure.

What types of data does the machine-learning approach use to estimate stress severity?

The approach uses acoustic, verbal, visual, and physiological data to detect stress severity.

How is stress severity represented in the model output?

Stress severity is expressed on a continuous scale rather than as discrete categories.

Aug 5, 2026

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