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AI-BASED GESTURE RECOGNITION FOR EMERGENCY SITUATIONS USING SVM AND OpenCV

Year: 2025

Type: article

Abstract: In emergency situations, quick response and hands-free communication are critical for safety. This research introducesan AI-driven gesture recognition system designed to activate an SOS alert using simple hand gestures. The system employsMediaPipe for precise hand tracking, an SVM classifier for real-time gesture recognition, and an integrated SMS alert systemthat includes GPS location tracking. The proposed approach ensures accessibility for individuals in distress, particularly thosewith disabilities or in high-risk environments where traditional emergency triggers may be impractical. The experimental resultsshow a 95.19% accuracy in gesture classification, demonstrating the system’s effectiveness in real-world scenarios. Future workaims to incorporate deep learning-based recognition models and deploy the system on wearable and mobile platforms to enhanceusability and responsiveness.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.16410502

Authors: Shokeen, Ms. Aneesha, Mittal, Mr. Yash

Institution: Guru Gobind Singh Indraprastha University

Language: English

Topic: Hand Gesture Recognition Systems

Subfield: Human-Computer Interaction

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Good health and well-being

Open Access status: green

OpenAlex ID: W6893350317

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