[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128303-en":3,"doc-seo-128303-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128303,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data - lessons learned","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.","EUR Research Information Portal  \nMachine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data  \nPublished in:  \nFrontiers in Psychiatry  \nPublication status and date:  \nPublished: 13/06/2025  \nDOI (link to publisher):  \n10.3389/fpsyt.2025.1548287  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nDocument License/Available under:  \nCC BY  \nCitation for the published version (APA):  \nCiharova, 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  \nlearned. Frontiers in Psychiatry, 16, Article 1548287. [https://doi.org/10.3389/fpsyt.2025.1548287](https://doi.org/10.3389/fpsyt.2025.1548287)  \nLink to publication on the EUR Research Information Portal  \nTerms and Conditions of Use  \nExcept 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:  \n• you may download, save and print a copy of this material for your personal use only;  \n• you may share the EUR portal link to this material.  \nIn 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.  \nTake-down policy  \nIf 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.  \nTYPE Original Research PUBLISHED 13 June 2025  \nDOI 10.3389/fpsyt.2025.1548287  \nOPEN ACCESS  \nEDITED BY  \nMichael Patrick Schaub,  \nUniversity of Zurich, Switzerland  \nREVIEWED BY  \nRüdiger Christoph Pryss,  \nJulius Maximilian University of Würzburg, Germany  \nColin K Drummond,  \nCase Western Reserve University, United States  \n*CORRESPONDENCE  \nMarketa Ciharova  \n [m.ciharova@vu.nl](m.ciharova@vu.nl)  \nRECEIVED 19 December 2024  \nACCEPTED 20 May 2025  \nPUBLISHED 13 June 2025  \nCITATION  \nCiharova M, Amarti K, van Breda W, Gevonden MJ, Ghassemi S, Kleiboer A, Vinkers CH, Sep MSC, Trofimova S,  \nCooper 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.  \nFront. Psychiatry 16:1548287 .  \ndoi: 10.3389/fpsyt.2025.1548287  \nCOPYRIGHT  \n© 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  \nCreative Commons Attribution License (CC BY) .  \nThe 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.  \nMachine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned  \nMarketa Ciharova 1*, Khadicha Amarti 1, Ward van Breda 2,3, Martin J. Gevonden 4, Sina Ghassemi 5, Annet Kleiboer 1, Chri","cbCaigQdDshUYC4j","https://ap.wps.com/l/cbCaigQdDshUYC4j","pdf",718659,4,1,14,"English","en",105,"# Background\n## Rationale for early detection of acute stress\n# Methodological scope\n## Multi-modal inputs: acoustic, verbal, visual, physiological data\n# Publication details\n## Open access and research metadata","[{\"question\":\"Why is early detection of acute stress important in this research?\",\"answer\":\"Early detection of elevated acute stress is necessary to reduce consequences linked to prolonged or recurrent stress exposure.\"},{\"question\":\"What types of data does the machine-learning approach use to estimate stress severity?\",\"answer\":\"The approach uses acoustic, verbal, visual, and physiological data to detect stress severity.\"},{\"question\":\"How is stress severity represented in the model output?\",\"answer\":\"Stress severity is expressed on a continuous scale rather than as discrete categories.\"}]","Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data - 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