Smart Helmet for Real-Time Drowsiness and Alcohol Detection with IoT-Enabled Emergency Alerts

Road accidents because of drowsy or unconscious riders still pose a really big safety risk especially for two- wheelers as these have less physical shielding and the time to get emergency services is longer. This work proposes a smart helmet system that is a part of the Internet of Things paradigm which can quickly determine the drowsiness state of a rider in real time and implement corrective actions in time. A multi, core ESP32 microcontroller is used in this case to cope with the various jobs of sensing, communicating, and issuing the right control commands. The eyelids are detected by an eyeblink sensor based on infrared sensing to find if the eyelids are closed for too long and this prolonged eyelid closure is considered as an incident of drowsiness. When the eyeballs remain closed for more than a set limit of 1.75 seconds, the eyes are closed for
too long with an alarming sound and the bike's motors are slowed down via the motor control unit besides sending a message through the GPS, GSM interface as a help crime message along with the live location to the user with a specified contact.