[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126299-en":3,"doc-seo-126299-105":30,"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":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126299,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136",8,"Research & Report","Stress can be detected during emotion-evoking smartphone use - a pilot study using machine learning","The study addresses the need for precise stress detection to enable timely interventions, noting that both objective and subjective stress measurements have validity limitations. It evaluates whether stress can be predicted from facial expressions corresponding to six basic emotions and relaxation during emotion-evoking smartphone use. Secondary video data from 69 participants are analyzed with regression machine learning models. Results show that facial emotion indicators relate to stress scores, with XGBoost providing lower prediction error than Random Forest. The findings support non-invasive video recordings as complements to standard stress markers.","TYPE Original Research PUBLISHED 30 April 2025  \nDOI 10.3389/fdgth.2025.1578917  \nEDITED BY  \nPanagiotis Tzirakis,  \nHume AI, United States  \nREVIEWED BY  \nParimita Roy,  \nThapar Institute of Engineering & Technology, India  \nYating Huang,  \nEast China Normal University, China  \n*CORRESPONDENCE  \nLydia Helene Rupp  \n [lydia.rupp@fau.de](lydia.rupp@fau.de)  \nRECEIVED 18 February 2025  \nACCEPTED 17 April 2025  \nPUBLISHED 30 April 2025  \nCITATION  \nRupp LH, Kumar A, Sadeghi M, SchindlerGmelch L, Keinert M, Eskofier BM and Berking M (2025) Stress can be detected during emotion-evoking smartphone use: a pilot study using machine learning.  \nFront. Digit. Health 7:1578917 .  \ndoi: 10.3389/fdgth.2025.1578917  \nCOPYRIGHT  \n© 2025 Rupp, Kumar, Sadeghi, SchindlerGmelch, Keinert, Eskofier and Berking. This isan 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.  \nStress can be detected during emotion-evoking smartphone use: a pilot study using machine learning  \nLydia Helene Rupp1*, Akash Kumar2, Misha Sadeghi2,  \nLena Schindler-Gmelch1, Marie Keinert1, Bjoern M. Eskofier2,3 and Matthias Berking1  \n1Lehrstuhl für Klinische Psychologie und Psychotherapie, Friedrich-Alexander-Universität ErlangenNürnberg, Erlangen, Germany, 2Machine Learning and Data Analytics Lab, Faculty of Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany, 3Translational Digital Health Group, Institute of AI for Health, Helmholtz Zentrum München-German Research Center for Environmental Health, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany  \nIntroduction: The detrimental consequences of stress highlight the need for precise stress detection, as this offers a window for timely intervention. However, both objective and subjective measurements suffer from validity limitations. Contactless sensing technologies using machine learning methods present a potential alternative and could be used to estimate stress from externally visible physiological changes, such as emotional facial expressions. Although previous studies were able to classify stress from emotional expressions with accuracies of up to 88.32%, most works employed aclassification approach and relied on data from contexts where stress was induced. Therefore, the primary aim of the present study was to clarify whether stress can be detected from facial expressions of six basic emotions (anxiety, anger, disgust, sadness, joy, love) and relaxation using a prediction approach.  \nMethod: To attain this goal, we analyzed video recordings of facial emotional expressions collected from n = 69 participants in a secondary analysis of adataset from an interventional study. We aimed to explore associations with stress (assessed by the PSS-10 and a one-item stress measure) .  \nResults: Comparing two regression machine learning models [Random Forest (RF) and XGBoost], we found that facial emotional expressions were promising indicators of stress scores, with model fit being best when data from all six emotional facial expressions was used to train the model (one-item stress measure: MSE (XGB) = 2.31, MAE (XGB) = 1.32, MSE (RF) = 3.86, MAE (RF) = 1.69; PSS-10: MSE (XGB) = 25.65, MAE (XGB) = 4.16, MSE (RF) = 26.32, MAE (RF) = 4 . 14) . XGBoost showed to be more reliable for prediction, with lower error for both training and test data.  \nDiscussion: The findings provide further evidence that non-invasive video recordings can complement standard objective and subjective markers of stress.  \nKEYWORDS  \nstress, emotion, machine learning, emotion expression, automated stress recognition  \nFrontiers in Digital","cbCaidwfpitqNjto","https://ap.wps.com/l/cbCaidwfpitqNjto","pdf",189015,1,10,"English","en",105,"# Introduction\n## Stress assessment limitations\n## Rationale for machine-learning-based sensing\n# Method\n## Data source and participants\n## Stress measures and prediction approach\n# Results\n## Regression model comparison\n## Performance across emotional expressions\n# Discussion","[{\"question\":\"What was the main goal of this pilot study?\",\"answer\":\"To clarify whether stress can be detected from facial expressions of six basic emotions and relaxation using a prediction approach.\"},{\"question\":\"How were stress levels assessed in the study?\",\"answer\":\"Stress was assessed using PSS-10 and a one-item stress measure.\"},{\"question\":\"Which machine learning model performed better for predicting stress scores?\",\"answer\":\"XGBoost showed more reliable prediction performance, with lower errors than Random Forest on both training and test data.\"}]","Stress can be detected during emotion-evoking smartphone use - a pilot study using machine learning | PDF",1785904328,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"stress-can-be-detected-during-emotion-evoking-smartphone-use-a-pilot-study-using-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/stress-can-be-detected-during-emotion-evoking-smartphone-use-a-pilot-study-using-machine-learning/126299/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What was the main goal of this pilot study?","Question",{"text":76,"@type":77},"To clarify whether stress can be detected from facial expressions of six basic emotions and relaxation using a prediction approach.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were stress levels assessed in the study?",{"text":81,"@type":77},"Stress was assessed using PSS-10 and a one-item stress measure.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning model performed better for predicting stress scores?",{"text":85,"@type":77},"XGBoost showed more reliable prediction performance, with lower errors than Random Forest on both training and test data.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":21,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]