Drowsiness detection system during driving using IoT and Machine Learning

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dc.contributor.authorSomo, A. M.
dc.date.accessioned2024-12-11T13:55:16Z
dc.date.available2024-12-11T13:55:16Z
dc.date.issued2024
dc.descriptionFull - text thesis
dc.description.abstractThe 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 will employ CV2, KNN and Dlib algorithms independently. The purpose of implementing these distinct machine learning algorithms is to conduct a comprehensive assessment and comparison of their performance. By doing so, we will be able to determine which algorithm yields the best results in terms of accuracy, thus enabling us to make an informed decision. The implemented solution will aim to enhance transparency and consistency in the acquisition of drowsiness related data. This initiative will make things easier for drivers and demonstrate how we can use IoT and Machine Learning technologies to solve problems around detecting drowsiness. By using both hardware and software, the system will show how we can use IoT concepts to solve common problems in drowsiness detection. The hardware we're using includes a computer camera as the sensors, and we'll also use the OpenCV framework libraries to train the machine learning model. The collected data associated with the drowsiness levels will then be transmitted to a central server for real time analysis. The data will undergo thorough processing and assessment to identify patterns of drowsiness instances. Furthermore, a User-friendly python interface will be developed to provide clients with visual insights into the detected drowsiness instances. Keywords – Internet of things (IoT)
dc.identifier.citationSomo, A. M. (2024). Drowsiness detection system during driving using IoT and Machine Learning [Strathmore University]. http://hdl.handle.net/11071/15649
dc.identifier.urihttp://hdl.handle.net/11071/15649
dc.language.isoen
dc.publisherStrathmore University
dc.titleDrowsiness detection system during driving using IoT and Machine Learning
dc.typeThesis
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MSIT Theses and Dissertations (2024)