Integrated Smart Helmet System with Drowsiness Detection, Accident Monitoring and Alcohol Detection

Abstract

This idea has a smart helmet system that combines different technologies to make riding safer. The helmet uses IoT to keep track of where the person is, see if they are sleepy, see if they are drinking, and warn them about accidents. It does all of this via machine learning. An alcohol sensor checks to see if the motorist has been drinking, and GPS keeps track of where the accidents are. A MEMS sensor finds the beginning point, while a vibration sensor keeps an eye on vibrations during an accident. You can also turn on the buzzer alarm by clicking a link on the page. The camera on the laptop also watches the driver's face for symptoms of tiredness and sounds an alarm if it sees any. The goal of the program is to make the road safer for everyone by adding features like being able to find out whether someone is tired, drunk, or in an accident, as well as tracking the rider's whereabouts. The ML technique will let the system look at data from several sensors in real time, which will let it find possible dangers like driver weariness or impairment from drinking alcohol early on. The system can also deliver real-time alerts and cautions to riders, which lets them rapidly and effectively respond to possible threats. This makes driving safer and less likely to cause accidents. This project attempts to make motorcycles safer by adding new technologies to a full smart helmet system.