AI-Based Women Safety App

Abstract

Abstract: Women\'s personal safety remains a pressing societal concern, particularly in urban and semi-urban environments where threats can emerge unpredictably. Conventional safety measures such as helpline numbers or manual alert systems are often too slow to respond effectively during emergencies. This paper presents an AI-Based Women Safety Application that integrates real-time GPS tracking, machine learning-driven crime hotspot detection, voice-activated SOS alerts, and an automated emergency response system into a unified mobile platform. The system employs a Random Forest classifier trained on historical crime data to predict risk zones and recommend safer alternative routes. A Convolutional Neural Network (CNN)-based voice recognition module enables hands-free SOS activation, while a rule-based alert engine immediately notifies pre-registered emergency contacts and nearby police stations. The application is implemented on Android using Firebase Realtime Database for live location sharing and Twilio API for automated SMS and call alerts. Experimental results demonstrate a crime hotspot prediction accuracy of 91.4%, voice-activation response time under 1.2 seconds, and SOS notification delivery latency of less than 3 seconds. The system provides a scalable, intelligent, and proactive safety infrastructure for women in distress.

Introduction

According to the National Crime Records Bureau (NCRB), over 428,000 crimes against women were reported in India in 2022, with actual numbers likely higher due to underreporting. Although government initiatives and community policing have improved safety in some areas, most existing safety measures remain reactive rather than preventive.

This paper proposes an AI-Based Women Safety Application that leverages smartphone technologies such as GPS, microphones, sensors, and internet connectivity to provide proactive protection. The system combines Artificial Intelligence (AI) and Machine Learning (ML) to predict unsafe areas, recommend safer routes, enable voice-activated emergency alerts, share real-time locations, and automatically notify emergency contacts and police authorities.

Key Objectives

The application addresses major limitations of existing women’s safety apps, including:

• Dependence on manual SOS activation.
• Lack of predictive crime-risk warnings.
• Limited emergency contact reach.
• Unreliable voice recognition in noisy environments.
• Absence of safe-route guidance.
• Poor scalability and integration with public safety systems.

Proposed System

The solution consists of four integrated modules:

1. Crime Hotspot Detection & Safe Route Recommendation
   • Uses historical crime data and a Random Forest model to predict risk levels in different areas.
   • Employs a modified Dijkstra’s algorithm to recommend safer travel routes by balancing distance and crime risk.
2. Voice-Activated SOS Trigger
   • Uses a CNN-based deep learning model trained on distress keywords in multiple Indian languages and English.
   • Automatically activates SOS when a distress phrase is detected with high confidence.
   • Achieved 92.8% validation accuracy after 40 training epochs.
3. Real-Time GPS Tracking & Location Sharing
   • Continuously tracks the user's location during emergencies.
   • Shares live location updates every 5 seconds with authorized contacts through a secure link.
4. Automated Emergency Notification & Police Integration
   • Sends emergency alerts via SMS and calls.
   • Integrates with police databases through secure APIs for faster response.

System Architecture

The application follows a three-tier architecture:

• Mobile Client Tier: Android app with UI, ML inference, location services, and emergency triggers.
• Cloud Backend Tier: Firebase services for authentication, data storage, live tracking, and notifications.
• External Services Tier: Twilio, Google Maps, OpenRouteService, and police integration APIs.

Conclusion

In This paper has presented a comprehensive AI-Based Women Safety Application that integrates machine learning-driven crime hotspot detection, CNN-based voice-activated SOS triggering, real-time GPS tracking, and an automated multi-channel emergency notification system into a unified, production-grade Android application. The system addresses critical limitations of existing women safety solutions by delivering both proactive risk intelligence and reactive emergency response capabilities. Experimental evaluation confirms the system\'s efficacy: the Random Forest crime predictor achieved a weighted-average F1-score of 0.92; the CNN voice recogniser reached 92.8% accuracy under realistic noise conditions with a 38ms latency; and end-to-end SOS notification delivery averaged 2.74 seconds. The safe route recommender reduced exposure to high-risk areas by 43.2% at a marginal distance cost of 11.7%, a trade-off accepted by 84% of study participants. The modular architecture ensures that each component can be independently updated, replaced, or extended without disrupting the overall system. Future directions including federated model updates, smartwatch biometric integration, and government emergency API connectivity present a clear pathway towards a national-scale women safety infrastructure powered by artificial intelligence.

References

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Copyright

Copyright © 2026 Pranav Darekar, Swaraj Dorage, Prathmesh Gaikwad, Harish Senthil, Prof. Geetanjali Bansod. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.