REAL-TIME DROWSINESS DETECTION AND DRIVER ALERT THROUGH MOBILE APPLICATION INTEGRATION
Abstract
Driver fatigue and drowsiness are major causes of road accidents worldwide. Traditional driver monitoring methods often fail to detect early signs of fatigue, leading to increased risk of collisions. This study proposes a real-time driver drowsiness detection system integrated with a mobile application to monitor driver alertness and provide immediate warnings when signs of fatigue are detected. The system utilizes computer vision techniques to analyze facial features such as eye closure rate, blinking frequency, and head movement through a smartphone camera. When drowsiness indicators exceed a predefined threshold, the system triggers an alert through sound notifications and vibration to awaken the driver. The study adopts an experimental research methodology involving the development and testing of a prototype mobile application capable of detecting driver fatigue in real time. The results demonstrate that integrating drowsiness detection algorithms with mobile applications provides an efficient and cost-effective approach to improving road safety. The system offers a practical solution that can be widely adopted due to the widespread availability of smartphones. The study concludes that mobile-based driver monitoring systems have the potential to significantly reduce accidents caused by fatigue.
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