International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 8 Issue 2
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An IOT-integrated Accident Detection and Emergency Alert System using Deep Learning and Gesture Recognition

Author(s) Prof. K B Padmasree, Mr. S Sathish Kumar, Mr. S Sriram, Mr. S R Vasudevan
Country India
Abstract road accidents are a leading cause of death, and in order to lower the number of fatalities, immediate alertness and action are necessary. in order to provide real-time monitoring and emergency support, the research proposes an iot-enabled ai-based accident detection and women safety monitoring system that combines deep learning, computer vision, and iot communication technologies. the algorithm analysis motion patterns to automatically identify accidents and employs a yolo-based object detection model to detect crashes. in order to recognize emergency signals and allow people to ask for assistance in emergency situations a gesture recognition module that uses hand sign detection is integrated. when an accident or gesture is detected, the system captures footage of the incident, uses internet of things modules to obtain gps-based location information, and automatically notifies emergency services via sms or email. an web interface is developed for recording and monitoring instances. via efficient automated emergency response systems, research findings demonstrate high reliability and accuracy under a wide range of situations involving significantly reducing response times and enhancing road safety.
Keywords road safety, computer vision, yolo, gesture recognition, deep learning, iot, accident detection, and emergency alert systems
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-03-30
DOI https://doi.org/10.36948/ijfmr.2026.v08i02.72842
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