Abstract
Abstract: The system utilizes advanced computer vision algorithms to analyze real-time video streams, employing techniques such as facial landmark detection and eye movement tracking. By continuously monitoring these visual cues, the system can accurately detect signs of drowsiness, such as drooping eyelids or prolonged eye closures, prompting timely interventions to prevent accidents or errors caused by fatigue. In addition to detecting drowsiness, the system also evaluates the user's attention levels by analysing head orientation and facial expressions. By tracking head movements and assessing changes in facial expressions indicative of engagement or distraction, the system provides valuable insights into the user's cognitive state. This attention assessment component enables the system to adapt its feedback and intervention strategies based on the user's level of alertness and focus. To enhance user awareness and responsiveness, the system employs graphical visualization techniques to display real-time feedback on drowsiness and attention status. Visual indicators, such as color-coded alerts or dynamic graphs depicting attention trends, provide users with intuitive insights into their cognitive performance, empowering them to make informed decisions about their work habits or driving patterns. Moreover, the system utilizes auditory alerts to supplement visual feedback, ensuring that users receive timely notifications even in noisy or visually demanding environments. Whether it's a gentle reminder to take a short break or a more urgent warning about escalating drowsiness levels, auditory cues serve as an effective means of alerting users to potential risks and encouraging proactive intervention
Published Version
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