{"id":175404,"date":"2026-10-19T09:50:29","date_gmt":"2026-10-19T09:50:29","guid":{"rendered":"https:\/\/www.m3globalresearch.com\/blog\/?p=175404"},"modified":"2026-10-20T14:46:36","modified_gmt":"2026-10-20T14:46:36","slug":"ai-system-detects-daily-stress-by-analysing-routine-smartphone-behaviours","status":"publish","type":"post","link":"https:\/\/www.m3globalresearch.com\/blog\/ai-system-detects-daily-stress-by-analysing-routine-smartphone-behaviours\/","title":{"rendered":"AI System Detects Daily Stress by Analysing Routine Smartphone Behaviours"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"175404\" class=\"elementor elementor-175404\" data-elementor-settings=\"{&quot;ha_cmc_init_switcher&quot;:&quot;no&quot;}\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7b2c08d e-con e-atomic-element e-flexbox-base e-d693f31 \" data-id=\"7b2c08d\" data-element_type=\"e-flexbox\" data-e-type=\"e-flexbox\" data-interaction-id=\"7b2c08d\">\n    \t\t<div class=\"elementor-element elementor-element-b12c334 elementor-widget elementor-widget-text-editor\" data-id=\"b12c334\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Researchers have developed an artificial intelligence system that identifies daily stress by analysing repeated behavioural patterns on smartphones, rather than tracking which apps a person uses.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f241f77 elementor-widget elementor-widget-text-editor\" data-id=\"f241f77\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>A team at the Korea Advanced Institute of Science and Technology (KAIST) developed the technology, called SHIRBT-MMF. Previous smartphone-based stress research has primarily analysed factors such as screen time, app launches and typing behaviour. However, when apps or interfaces change, these data also change, reducing the long-term stability of stress-tracking models.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bcdb794 elementor-widget elementor-widget-text-editor\" data-id=\"bcdb794\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>The KAIST team focused instead on what a person does rather than which app they use. Whether someone switches from one messaging platform to another, the underlying activity remains \u201cmessenger chatting.\u201d The researchers defined these repeated behaviours as Smartphone Human Interaction Routine Behaviour Tasks (SHIRBTs) and used a large language model to assign understandable labels such as \u201cmessenger chatting,\u201d \u201cweb browsing\u201d and \u201cfinancial payment.\u201d<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c1081fb elementor-widget elementor-widget-text-editor\" data-id=\"c1081fb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>The system achieved more than 95% accuracy in consistently classifying the same behaviour across different apps. To protect privacy, it recorded only behavioural information such as whether text was entered and how often, without storing actual message content.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-05d7226 elementor-widget elementor-widget-text-editor\" data-id=\"05d7226\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>The team analysed real-life data from 26 university students and office workers aged in their 20s and 30s. Using personalised AI models trained on these behavioural patterns, the best-performing model achieved 75.9% accuracy for short-term participants and 73.8% for long-term participants in distinguishing between days with and without stress.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0b60924 elementor-widget elementor-widget-text-editor\" data-id=\"0b60924\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>The researchers noted that the study identified associations between behaviour and stress but did not demonstrate causality. Further research will expand to broader age groups and smartphone environments.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bb5aaf1 elementor-widget elementor-widget-text-editor\" data-id=\"bb5aaf1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Source: Lee H et al. Leveraging smartphone human interaction routine behavior task mining and modeling for daily stress monitoring. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (2025). DOI: 10.1145\/3770644<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\n<\/div>\n\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>AI system detects daily stress by analysing routine smartphone behaviours like messaging and payments, achieving 75% accuracy in a KAIST study.<\/p>\n","protected":false},"author":11,"featured_media":175446,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_eb_attr":"","footnotes":""},"categories":[3200],"tags":[],"class_list":["post-175404","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-latest-medical-news"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.9 (Yoast SEO v28.2) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>AI System Detects Daily Stress by Analysing Routine Smartphone Behaviours<\/title>\n<meta name=\"description\" content=\"Artificial intelligence detects 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