{"id":350,"date":"2025-06-20T10:51:52","date_gmt":"2025-06-20T08:51:52","guid":{"rendered":"https:\/\/research.dblue.it\/dialog\/?p=350"},"modified":"2025-09-24T21:38:53","modified_gmt":"2025-09-24T19:38:53","slug":"how-can-we-understand-atco-workload-without-interrupting-it","status":"publish","type":"post","link":"https:\/\/research.dblue.it\/dialog\/2025\/06\/20\/how-can-we-understand-atco-workload-without-interrupting-it\/","title":{"rendered":"How Can We Understand ATCO Workload Without Interrupting It?"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">One of the most compelling questions in human-centric AI is how to <\/span><b>detect signs of strain or overload\u2014without adding more complexity<\/b><span style=\"font-weight: 400;\"> to the operator\u2019s task. In Air Traffic Management, where timing and attention are critical, the answer may lie in combining <\/span><b>unobtrusive sensors<\/b><span style=\"font-weight: 400;\"> with <\/span><b>intelligent data processing<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">During the recent face-to-face DIALOG meeting in Oslo, <\/span><b>Zia Uddin (SINTEF)<\/b><span style=\"font-weight: 400;\"> shared key developments from <\/span><b>Work Package 2<\/b><span style=\"font-weight: 400;\">, which focuses on the <\/span><b>real-time assessment of air traffic controllers\u2019 (ATCOs) attention and workload<\/b><span style=\"font-weight: 400;\">. This work underpins the <\/span><b>Teammate Awareness Service<\/b><span style=\"font-weight: 400;\">, one of DIALOG\u2019s three core AI components.<\/span><\/p>\n<h4><b>Sensing Workload Without Disrupting the Task<\/b><\/h4>\n<p><span style=\"font-weight: 400;\">A variety of <\/span><b>minimally invasive technologies<\/b><span style=\"font-weight: 400;\"> are being explored to track cognitive state in real time:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Gaze and behavior monitoring<\/b><span style=\"font-weight: 400;\">: Eye-tracking and task-related behavior offer real-time insights into where a controller\u2019s attention is focused\u2014and how it shifts across activities.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Speech analysis<\/b><span style=\"font-weight: 400;\">: Subtle changes in voice\u2014such as pace, pitch, or hesitation\u2014can act as passive indicators of cognitive load.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Physiological signals<\/b><span style=\"font-weight: 400;\">: Heart rate variability and electrodermal activity reflect internal states, offering additional clues without requiring active input.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Facial expressions<\/b><span style=\"font-weight: 400;\">: Emotion-related cues from facial analysis help complement the understanding of mental effort and stress.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">These data sources are <\/span><b>non-intrusive by design<\/b><span style=\"font-weight: 400;\">\u2014a key requirement in high-stakes environments like ATM.<\/span><\/p>\n<h4><b>Why Multimodality Matters<\/b><\/h4>\n<p><span style=\"font-weight: 400;\">The power of this approach lies in <\/span><b>data fusion<\/b><span style=\"font-weight: 400;\">. Isolated signals are informative, but it\u2019s when they are combined\u2014across visual, auditory, and physiological channels\u2014that a fuller, more accurate picture of mental workload emerges. In fact, <\/span><b>low synchrony between modalities often signals high workload<\/b><span style=\"font-weight: 400;\">. Machine learning models help detect these patterns and transform them into actionable insights.<\/span><\/p>\n<h4><b>From Monitoring to Support<\/b><\/h4>\n<p><span style=\"font-weight: 400;\">The ultimate goal is not just to monitor, but to <\/span><b>enable adaptive support<\/b><span style=\"font-weight: 400;\">: AI agents that can recognize when a controller is becoming cognitively saturated and adjust their assistance accordingly.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This work directly supports DIALOG\u2019s broader ambition: creating <\/span><b>AI teammates<\/b><span style=\"font-weight: 400;\"> that are context-aware, non-intrusive, and truly collaborative\u2014ensuring that support never becomes an additional burden.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\ud83e\udde0 <\/span><b>DIALOG<\/b><span style=\"font-weight: 400;\"> is a SESAR Exploratory Research project reimagining how humans and AI collaborate in air traffic control.<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><span style=\"font-weight: 400;\"> \ud83d\udcac Want to learn more about our technical progress? Explore the<a href=\"https:\/\/research.dblue.it\/dialog\/news\/\"> project updates\u00a0<\/a><\/span><a href=\"https:\/\/dialogproject.eu\"><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/a><span style=\"font-weight: 400;\"> \ud83d\udce1 <a href=\"https:\/\/docs.google.com\/forms\/d\/e\/1FAIpQLSeG9KVZcqP2Op5wGGhrVVRnRVTgxPPZWyqGZ-K6RRB521d9tQ\/viewform?usp=pp_url\">Follow us<\/a> to stay updated on the future of ATM.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>One of the most compelling questions in human-centric AI is  [&#8230;]<\/p>\n","protected":false},"author":19,"featured_media":392,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[],"class_list":["post-350","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - 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