Virtual ID based Vehicle Accident Detection and Patient Alerting System using IoT

Emergency response systems are made more effective by incorporating a multi-modal biometric authentication system with an internet-of-things-based accident detection system. Due to integrated sensors and machine learning algorithms, the suggested system is able to detect collision in real-time and identify face, fingerprint, and iris scans. The experimental testing showed an impressive biometric identification accuracy of 99.94% and accident detection accuracy of 99.87%, both of which were much higher than existing techniques. The hybrid structure can greatly minimize response times by allowing the identification of victims and sending emergency notifications within less than 850 milliseconds. Comparative evaluations with recent hybrid frameworks demonstrated superior performance in both accuracy and response latency. The findings showed that it was more precise, had a better recall, and minimized false positives compared to traditional biometric and accident detection techniques. The results validate the method’s potential to improve victim identification and speed up emergency response in case of an accident. Additionally, real-time cloud communication between the Raspberry Pi and NodeMCU ensures continuous data synchronization and remote alert dispatch.