Comprehensive Drowsiness Detection System Using OpenCV, Machine Learning, and Heart Rate Monitoring

The face, an essential part of the body, communicates a vast array of information. When a driver is fatigued, their facial expressions, along with the frequency of blinking and yawning, change notably compared to a normal state. This project focuses on creating a system that identifies a driver's fatigue indicators-such as yawning, blinking, and eye closure duration-using video footage, without relying on wearable devices. We introduce a computer vision-based method to detect driver drowsiness from a camera-recorded video. Our technique involves identifying the face and detecting the eyes in each video frame. We have developed a unique detection method for facial regions using 68 key points. These facial regions help evaluate the driver's state. By examining the characteristics of the eyes and mouth, we can issue a fatigue alert to the driver via an alarm sound. Moreover, once the fatigue continues, the self-driving mode will be activated, and the system will detect the lane to ensure that the car moves in the correct lane. Integrate a heart rate sensor to continuously monitor the driver's heart rate. If an irregular heart rate is detected, alerts will be sent to nearby hospitals. This system has the potential to greatly decrease the risk of accidents caused by drowsy driving, a leading factor in traffic incidents globally.