IoT-Based Smart Helmet for Real-Time Crash Detection, Prevention and Rider Safety Monitoring

Abstract

Motorcyclists remain among the most vulnerable road users, with high fatality rates often linked to delayed emergency response and unsafe riding practices. This project presents the design and development of an IoT-based smart helmet for real-time crash detection, accident prevention, and rider safety monitoring. The system integrates multiple sensors, including the MPU6050 accelerometer and gyroscope for crash detection, MQ- 3 alcohol sensor for intoxication monitoring, TCRT5000 IR sensor for drowsiness detection, ultrasonic sensor for obstacle awareness, and IR sensor for helmet- wearing verification. An ESP32 microcontroller serves as the central unit, coordinating sensor inputs and outputs, while GSM and GPS modules enable automatic emergency communication through SMS, calls, and location sharing. Testing results demonstrate that the helmet reliably detects unsafe conditions such as crashes exceeding a 2.5 g threshold, alcohol presence above set limits, prolonged eye closure, and nearby obstacles within one meter. The system initiates alerts via buzzer, Blynk mobile application, Gmail notifications, and GSM communication, with a 10- second countdown provided to cancel false crash triggers. Findings confirm that the smart helmet offers both preventive and responsive safety features, reducing accident risks and improving emergency response times. This work highlights the potential of IoT technology to enhance motorcycle safety by integrating real-time monitoring, rider condition assessment, and automated emergency alerts into a single wearable device. The prototype demonstrates a practical and scalable solution that can contribute to reducing motorcycle fatalities and advancing intelligent road safety systems.