Real-Time Two-Wheeler Accident Detection with Automated Emergency Notification and Post-Impact Voice Logging Using IoT

Abstract

Two-wheeler riders are disproportionately vulnerable to severe road accident injuries due to the absence of automated emergency response mechanisms. This paper presents a real-time accident detection and emergency notification system designed specifically for motorcycles, leveraging Internet of Things (IoT) technologies. The proposed system employs an MPU6050 inertial measurement unit interfaced with an ESP32 microcontroller to continuously monitor linear acceleration and angular motion at 100 Hz. A time-based threshold filtering algorithm distinguishes genuine collision events and rollovers from common false-trigger scenarios such as potholes, sharp turns, and hard braking — by requiring hazardous sensor readings to persist beyond calibrated temporal gates (? 100ms for impacts at > 7g; ? 500ms for tilt angles exceeding ±75°). Upon accident confirmation, the system autonomously retrieves GPS coordinates via a NEO-6M module and dispatches location-embedded emergency SMS alerts through a SIM800L GSM module, while simultaneously uploading real-time data to the Blynk IoT cloud platform. A distinguishing feature of the system is its post-impact voice logging capability, wherein a MAX9814 microphone records 30–60 seconds of ambient audio, stored in WAV format on a MicroSD card to support forensic investigation and victim assessment. Experimental evaluation across ten simulated accident scenarios demonstrated reliable collision detection with a low false-alarm rate and average SMS delivery times of 8–12 seconds. The system\'s internet-independent architecture, cost-effective hardware, and scalable design make it a practical solution for improving emergency response times and enhancing safety across the global two-wheeler population.

Introduction

The text describes the development of a smart accident detection and emergency alert system designed specifically for two-wheeler riders, who are highly vulnerable in road accidents. Traditional accident reporting methods are often delayed or unreliable, prompting the need for an automated solution.

The proposed system uses sensors and IoT technology to detect collisions accurately. An MPU6050 sensor measures acceleration and motion, while an ESP32 processes the data using adaptive thresholding and time-based filtering to distinguish real accidents from normal riding disturbances like potholes or sharp turns. When an accident is confirmed, a GSM module sends emergency alerts with GPS location details, and a microphone records post-accident audio for further analysis. The system also supports cloud connectivity for real-time monitoring.

The methodology includes continuous sensor monitoring, calculation of resultant force, validation over time to avoid false alarms, and additional features like rollover detection and a manual panic button. Once an accident is detected, the system retrieves GPS data, sends alerts, uploads data to the cloud, and records audio evidence.

Results show high accuracy in detecting real accidents while minimizing false triggers. The system performs reliably across various scenarios, with quick alert delivery and accurate location tracking.

Overall, the solution is cost-effective, scalable, and significantly improves emergency response time and rider safety. Future improvements include integrating machine learning for better detection, enhancing audio quality, adding vehicle data integration, and enabling communication in low-network areas.

Conclusion

This paper presents a comprehensive IoT-based accident detection and emergency response system for two-wheelers that integrates impact sensing, GPS tracking, and a novel post-impact voice logging capability. By utilizing an ESP32 and MPU6050 sensor with a time-based threshold filtering algorithm, the system effectively distinguishes genuine collisions and rollovers from common riding irregularities like potholes or sharp turns. Upon detecting an accident, the device ensures communication redundancy by dispatching location-embedded alerts via GSM-SMS while simultaneously capturing ambient audio to assist in forensic investigation and victim assessment. Ultimately, this low-cost and internet-independent solution offers a practical, scalable framework designed to minimize emergency response times and enhance the safety of the global two-wheeler population.

References

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Copyright

Copyright © 2026 Tushar Bhadane, Arnav Abhigya, Neel Chotalia, Nandit Aghera. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.