Detection of Narcoleptic and Veer tendencies using Machine Learning and Sensors

Detection of Narcoleptic and Veer tendencies using Machine Learning and Sensors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Detection of Narcoleptic and Veer tendencies using Machine Learning and Sensors Saurav K. Dubey, RAKESH K. PRASAD, Dilip K. Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4552913/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In recent years, road safety has been a matter of great concern world-wide with increasing number of accidents annually. Narcolepsy and Veer tendencies are a major source for the cause of accidents in today's world. The Drowsiness and Fatigue levels affect the mindset of the drivers which causes accidents. The proposed system uses a hybrid network of computer vision and physical sensor equipped on a prototype. Comparison is done between Yolo and Haar model. The system uses Haar Cascade Classifier to detect the eye iris state and the sensors work as a confirmatory test to validate the results. CHT (Circular Hough Transform) is used to detect the circular shape of the eye and to detect the eye iris state detection. The algorithm localizes tracks and analyses both the driver face and eyes to measure the eye iris visual state. The sensor ADXXL 335 measures the veer tendencies using gravitation acceleration (g) experienced by the prototype during traversing from one direction to the other. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4552913","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":315724419,"identity":"c378c1c0-2fe1-4bec-a62d-49ee0e8af7a6","order_by":0,"name":"Saurav K. 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