[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118338-en":3,"doc-seo-118338-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},118338,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Drowsiness detection system during driving using IoT and Machine Learning","Growing interest in drowsiness detection systems through the integration of IoT and Machine Learning is accelerating in the automotive and transportation sector. The proposed solution aims to monitor and identify driver drowsiness to mitigate fatigue-related safety risks. Key adoption challenges include limited IoT network connectivity and the need for efficient resource use within machine-learning constraints. The study designs and implements an IoT-connected in-vehicle camera sensor, collects real-time eye-movement data, preprocesses it for features, and trains low-resource classification models for real-time drowsiness detection, comparing CV2, KNN, and Dlib performance for accuracy.","Electronic Thesesand Dissertations  \n2024  \nDrowsiness detection system during driving using IoT and Machine Learning.  \nSomo, Abubakar Mohamed  \nSchool of Computing and Engineering Sciences Strathmore University  \nRecommendedCitation  \nSomo, A. M. (2024) . Drowsiness detection system during driving using IoT and Machine Learning [Strathmore University] . [http://hdl.handle.net/11071/15649](http://hdl.handle.net/11071/15649)  \nFollow this andadditional works at:  [http://hdl.handle.net/11071/15649](http://hdl.handle.net/11071/15649)  \nDrowsiness Detection System During Driving Using IoT and  \nMachine Learning  \nAbubakar Mohamed Somo  \n111335  \nA Research Proposal submitted to the School of Computing Engineering, Strathmore University, for the Award of a master’s degree in information technology  \nSTRATHMORE UNIVERSITY  \nNAIROBI, KENYA  \nAugust 2023  \nDECLARATION  \nI declare that this work has not been submitted or previously submitted and approved in whole orin partial for the award of a degree by this university or any other university. To my knowledge and belief, this dissertation contains no material previously published or written by another person except where due reference is made in the thesis.  \nStudent  \nAbubakar Mohamed Somo  \n111335  \nSignature ……………………………. Date ……………………………………….  \nSupervisor  \nDr. Dickson Odhiambo  \nLecturer School of Computing and Engineering Sciences Faculty of Information Technology  \nSignature ……………………………. Date ……………………………………….  \n04 April, 2023  \nACKNOWLEDGEMENTS  \nI wish to thank God for giving me the strength and guidance to write this thesis proposal.  \nI would like to express my deepest gratitude to my supervisor, Dr. Dickson Owour, for their invaluable guidance, encouragement, and unwavering support throughout the journey of completing this thesis. Their expertise, patience, and constructive feedback have been instrumental in shaping the direction and quality of this work.  \nI am also indebted to Solomon Itotia, whose insightful suggestions and scholarly input have enriched the content of this thesis.  \nMy sincere appreciation goes to my family for their unconditional love, understanding, and encouragement. Their unwavering belief in my abilities has been a constant source of motivation.  \nI extend my heartfelt thanks to my friends and colleagues for their inspiration, camaraderie, and intellectual exchange, which have enriched my academic experience.  \nFinally, I am grateful to all the participants who generously contributed their time and insights to this research.  \nThis work would not have been possible without the support and encouragement of all those mentioned above. Thank you.  \nABSTRACT  \nThe interest in implementing drowsiness detection systems through the integration of IoT and Machine Learning, especially in the automotive and transportation sector is growing significantly. By utilizing this technology, it becomes possible to monitor and identify instances of driver drowsiness, addressing safety concerns related to fatigue related accidents. However, the widespread adoption and application of these drowsiness detection systems encounters some challenges such as poor telecommunication for network connectivity for IoT devices and ensuring efficient resource utilization within the constraints of Machine Learning. These are the main challenges faced by drowsiness detection systems during driving. This study designs and implements an efficient drowsiness detection system that utilizes Machine Learning and IoT technologies. The approach will involve the deployment of an IoT connected sensor, which is a camera within the vehicle’s environment. This sensor will collect real-time data on the driver’s eye movements. This raw data is then preprocessed to extract the relevant features and then processed information will be fed into the Machine Learning model. This model, which is optimized for low-resource environments will be able to perform real time drowsiness classification. Our model w","cbCais8pYGa1dNTu","https://ap.wps.com/l/cbCais8pYGa1dNTu","pdf",3642652,1,85,"English","en",105,"# Table of Contents\n## Declaration\n## Acknowledgements\n## Abstract\n## List of Figures\n## Abbreviation","[{\"question\":\"What problem does the proposed system address?\",\"answer\":\"It addresses driver drowsiness during driving to reduce safety concerns related to fatigue-related accidents.\"},{\"question\":\"How does the system collect data in real time?\",\"answer\":\"It uses an IoT-connected camera sensor inside the vehicle to collect real-time data on the driver’s eye movements.\"},{\"question\":\"Which machine learning approaches are compared in the study?\",\"answer\":\"The solution evaluates CV2, KNN, and Dlib algorithms independently to compare their performance and determine the most accurate one.\"}]","Drowsiness detection system during driving using IoT and Machine Learning | PDF",1785683159,214,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"drowsiness-detection-system-during-driving-using-iot-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@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/drowsiness-detection-system-during-driving-using-iot-and-machine-learning/118338/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the proposed system address?","Question",{"text":75,"@type":76},"It addresses driver drowsiness during driving to reduce safety concerns related to fatigue-related accidents.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the system collect data in real time?",{"text":80,"@type":76},"It uses an IoT-connected camera sensor inside the vehicle to collect real-time data on the driver’s eye movements.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning approaches are compared in the study?",{"text":84,"@type":76},"The solution evaluates CV2, KNN, and Dlib algorithms independently to compare their performance and determine the most accurate one.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]