| Issue |
ITM Web Conf.
Volume 85, 2026
Intelligent Systems for a Sustainable Future (ISSF 2026)
|
|
|---|---|---|
| Article Number | 01007 | |
| Number of page(s) | 4 | |
| Section | AI for Healthcare, Agriculture, Smart Society & Computer Vision | |
| DOI | https://doi.org/10.1051/itmconf/20268501007 | |
| Published online | 09 April 2026 | |
Intelligent IoT-Based Accident Monitoring System Using AI-Driven Emotion Detection
1 Dept of Computer Science Engineering, St. Joseph's College of Engineering Chennai, India
2 Dept of Computer Science Engineering, St. Joseph's College of Engineering Chennai, India
3 Dept of CyberSecurity (Associate Professor), St. Joseph's College of Engineering Chennai, India
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Abstract
This paper proposes a comprehensive IoT and AI-based smart vehicle accident prevention and monitoring system. The system integrates multiple sensors including ultrasonic, MEMS accelerometer, vibration sensor, MQ-3 alcohol sensor, temperature sensor, and tyre pressure sensor to monitor various vehicle and driver parameters in real time. The core innovation lies in the integration of AI-powered driver emotion detection using Python image processing, which can identify risky emotional states such as anger, stress, or drowsiness. The data from the sensors and the camera are processed to generate alerts, trigger emergency responses, or even stop the vehicle when dangerous conditions are detected. The IoT framework enables remote monitoring and real-time updates, making the system more effective in emergency situations. By combining environmental monitoring, vehicle health diagnostics, accident impact detection, and human emotion analysis, the proposed system offers a unique multi-layered safety solution. This integration significantly reduces the chances of accidents caused by mechanical faults, drunk driving, poor vehicle maintenance, or driver emotions, thereby improving overall road safety.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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