IoT Accident Detection and Rescue

Abstract

IoT Accident Detection and Rescue is a smart emergency response system that detects road accidents in real time and immediately initiates rescue communication to reduce response time and improve victim safety. The solution combines an Android mobile application built using Java/XML with an IoT edge device based on ESP32. The Android app provides secure user authentication, emergency contact management, and cloud synchronization using Firebase, ensuring that contact data remains available and updated across sessions. On the hardware side, the ESP32 integrates an accelerometer and camera to monitor sudden impact patterns and capture event context. A threshold-based accident detection algorithm triggers an emergency workflow when abnormal motion is detected, automatically sending an SMS alert containing the user’s live location to saved emergency contacts. The system also includes an in-app guidance video section to help users and bystanders follow basic accident response steps. Current development focuses on reducing false alerts through sensor data optimization, improving GPS accuracy for better location sharing, and integrating an AI assistant to answer user queries during emergencies. Upcoming enhancements include accident event history and logs, optional push notifications and admin monitoring, and complete end-to-end testing for deployment readiness. Overall, this project delivers a practical, low-cost, and scalable accident detection and rescue framework using IoT and mobile cloud technologies.

Introduction

Road accidents are critical emergencies where delays in assistance can be life-threatening. Traditional emergency response systems rely on manual human action, which often fails when victims are unconscious, stressed, or unable to communicate their location. The IoT Accident Detection and Rescue system addresses this issue by automating accident detection and emergency notification to ensure faster and more reliable first response.

The system integrates an Android application with an ESP32-based IoT device equipped with an accelerometer and camera. The accelerometer continuously monitors motion and detects crash-like patterns using threshold-based logic, while the camera captures contextual evidence during suspected accidents. Once an accident is detected, the Android app retrieves the user’s live location and sends SMS alerts with a Google Maps link to pre-saved emergency contacts stored securely in Firebase. The app also includes guidance videos to help victims or bystanders take immediate action before professional help arrives.

The methodology emphasizes edge-level detection, reliable communication, and cloud-based data synchronization to ensure readiness during emergencies. Ongoing improvements focus on reducing false alerts, enhancing location accuracy, and integrating an AI assistant for emergency guidance. Future features include accident history logs, push notifications, and administrative monitoring for fleet or institutional use.

Results show that the system effectively detects high-impact events and delivers timely alerts with actionable location data. While false triggers and location variability remain challenges, proper sensor tuning and data filtering significantly improve reliability. Overall, the project demonstrates a practical, end-to-end IoT safety solution capable of reducing response time and improving survival chances in road accidents.

Conclusion

The IoT Accident Detection and Rescue successfully demonstrates an end-to-end emergency response workflow where an ESP32-based sensor unit detects crash-like impacts and an Android app immediately alerts saved contacts with a live location link. The system proves that a low-cost IoT + mobile architecture can reduce dependency on manual emergency calling and improve the chances of timely rescue. Testing confirms that detection and alert delivery are functional and reliable in typical conditions, while also showing that performance depends heavily on proper threshold tuning, network availability, and GPS accuracy.

References

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Copyright

Copyright © 2026 Om Pokale, Digvijay Nakhate, Neel Khamait, Avneesh Patil, Mr. Shivansh Shukla . 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.