{"id":17196,"date":"2026-07-21T15:46:45","date_gmt":"2026-07-21T15:46:45","guid":{"rendered":"https:\/\/www.cyber40.it\/sentinella-predictive-safety-platform-for-heavy-duty-vehicle-operators\/"},"modified":"2026-07-21T15:54:32","modified_gmt":"2026-07-21T15:54:32","slug":"sentinella-predictive-safety-platform-for-heavy-duty-vehicle-operators","status":"publish","type":"post","link":"https:\/\/www.cyber40.it\/en\/sentinella-predictive-safety-platform-for-heavy-duty-vehicle-operators\/","title":{"rendered":"SENTINELLA \u2013 Predictive Safety Platform for Heavy-Duty Vehicle Operators"},"content":{"rendered":"\n

Safety in heavy-duty vehicle operation is one of the main challenges in the logistics and industrial sectors<\/strong>, where complex operating conditions require high levels of attention and consistent performance.<\/p>\n\n

However, driving risks do not depend solely on the driver\u2019s behavior, but also on physiological factors that are often not visible. Conditions such as sudden changes in blood sugar levels<\/strong>, for example, can significantly affect reaction times, increasing the risk of accidents. <\/p>\n\n

SENTINELLA<\/strong> was created in response to this need; it is a project coordinated by Netcaring S.r.l.<\/a><\/strong>, in collaboration with ESA<\/a><\/strong> and MATER S.c.a.r.l.<\/a><\/strong>, is developing an integrated platform for monitoring heavy-duty vehicle operators and <\/strong>the secure management of the collected data.<\/p>\n\n

\"\"<\/figure>\n\n

The system analyzes driving behavior, attention patterns, and physiological parameters\u2014with a particular focus on signals related to glycemic risk\u2014to support a more comprehensive assessment of the driver’s condition.<\/p>\n\n

The goal is to move from a reactive security model, based on incident analysis, to a predictive approach capable of identifying risky situations before <\/strong>critical conditions arise.<\/p>\n\n

Innovation and key technologies<\/strong><\/h2>\n\n

\u2022 Intelligent monitoring of physiological and behavioral parameters\u2022 Integration of sensors for blood glucose level detection\u2022 Edge computing for local data processing and real-time response\u2022 Predictive models for the early identification of risk conditions\u2022 Cybersecurity architectures with authentication and data protection via JWT<\/p>\n\n

Scope of application<\/strong><\/h2>\n\n

\u2022 Construction companies with fleets of heavy-duty vehicles\u2022 Logistics and freight transport operators\u2022 Workplace safety systems and monitoring of operational staff<\/p>\n\n

Co-funded by Cyber 4.0<\/strong>, SENTINELLA introduces a predictive security model that integrates behavioral and physiological data to improve operator safety, ensure reliable information management, and enhance fleet operational efficiency.<\/p>\n","protected":false},"excerpt":{"rendered":"

Safety in heavy-duty vehicle operation is one of the main challenges in the logistics and industrial sectors, where complex operating conditions require high levels of attention and consistent performance. However, driving risks do not depend solely on the driver\u2019s behavior,…<\/p>\n","protected":false},"author":17,"featured_media":17192,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"iawp_total_views":0,"footnotes":""},"categories":[611],"tags":[632,616,672,720,659],"class_list":["post-17196","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-en","tag-ai-en","tag-cybersecurity-en","tag-innovation","tag-logistics","tag-sme"],"acf":[],"yoast_head":"\nSENTINELLA \u2013 Predictive 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