AI-Driven Child Safety System: Real-Time Fear Detection and Emergency Response using Enhanced Deep Learning Algorithms

Child safety in critical situations requires real-time monitoring and rapid intervention. This study introduces an AI-powered system that utilizes Convolutional Neural Networks (CNN) and k-Nearest Neighbors (KNN) to detect fear in children by analyzing facial expressions and behavioral patterns. A custom dataset of annotated child facial expressions was used to train the model, achieving 92.4% accuracy, outperforming traditional methods. Upon detecting distress, the system captures environmental images, records GPS coordinates, and instantly alerts parents and law enforcement through a secure web platform. A panic button ensures immediate emergency communication, while real-time safety instructions provide guidance during natural disasters. Comparative analysis of CNN and KNN models through confusion matrix evaluation confirms the system’s reliability. This AI-integrated approach enables faster intervention, reduces risks, and strengthens emergency response mechanisms, offering a comprehensive, technology-driven solution to child safety challenges.