Human Security System with Location Awareness

Ensuring personal safety has become a critical concern as incidents of harassment, abuse, and health emergencies continue to rise. Studies show that both men and women face physical and psychological threats, yet many incidents go unreported due to fear, lack of immediate assistance, or inadequate security mechanisms. To address these challenges, this research proposes an Android-based Human Security System with real-time emergency response and location awareness. The system integrates multiple security features: Panic Button – Instantly sends alert messages and places an emergency call to pre-configured contacts. Location Awareness System – Uses GPS tracking to monitor and share the user’s real-time location. Machine Learning-based Recommendation System – Employs collaborative filtering algorithms to provide safety insights based on historical user reviews and risk assessments. By leveraging mobile technology, GPS, and machine learning, this system offers a proactive, data-driven approach to security. The combination of real-time location tracking, emergency alerts, and AI-powered recommendations enhances user safety while enabling swift responses in distress situations. This research explores the system’s architecture, functionality, and efficiency, evaluating its advantages over existing emergency response applications. Keywords: Machine Learning, Panic Button, Alert Message, Emergency Response, Location Tracking, Recommendation System, Collaborative Filtering, Personal Security.