This study introduces a real-time, non-invasive system designed to monitor and detect driver drowsiness as well as sleep states. The method works by tracking eye-related behavior, such as blink frequency and eyelid closure duration, along with environmental conditions like surrounding light levels. Using these inputs, the system can reliably distinguish whether a driver is alert, drowsy, or asleep, even under actual driving situations. A decision-making module processes the collected data and issues timely warnings to prevent lapses that could lead to accidents. To validate its effectiveness, the system was built as a complete hardware model and tested in different scenarios, showing high levels of accuracy and consistency. The results confirm that the solution is practical, scalable, and easy to use, making it suitable for real-world applications where safety depends on maintaining human alertness.
SujathaKumari et al. (2025) studied this question.