RescueNow: Real-time SOS and Predictive Womens Safety System

Abstract

This research paper presents the real-world implementation of \"RescueNow\", an AI-integrated women’s safety mobile application aimed at enhancing emergency responsiveness through smart technology. The system combines real-time SOS alerting, GPS-based location tracking, silent audio-video recording, and multi-channel emergency communication using services like Twilio. Designed with a user-centric approach, the app provides multiple trigger mechanisms including voice command, shake detection, and one-tap alerts, ensuring accessibility in high-stress situations. It also features community safety ratings and direct communication with law enforcement authorities. A notable aspect of this project is its machine learning module, which analyses patterns to predict potential crime-prone zones, allowing for proactive measures. Extensive testing in simulated and real-world scenarios validated the system’s effectiveness, reliability, and scalability. This paper documents the design, implementation, and evaluation of RescueNow, demonstrating how technology can be a powerful ally in building a safer environment for women.

Introduction

Overview:

Women’s safety remains a critical global issue, with high rates of gender-based violence and harassment reported worldwide. Existing mobile safety apps often face challenges like dependency on internet connectivity, limited automation, and weak integration with emergency services.

RescueNow is an AI-enabled mobile app designed to overcome these challenges by offering real-time safety features including GPS tracking, silent audio-video recording, multi-trigger SOS alerts, and cloud-based communication with authorities and contacts. It also uses machine learning to predict and warn users about high-risk areas.

Literature Review:

• Prior systems combined mobile apps, wearables, and GIS to track locations and send alerts but often relied on manual input or stable connectivity.

• Some apps used audio sensors or voice activation but faced issues like sensor accuracy and internet dependency.

• IoT wearables with sensors and GPS offered automation but had limitations in data accuracy and practical deployment.

• Many solutions lacked real-time community data integration, advanced automation, or offline functionality.

System Design:

• Mobile App: User interface with SOS button, voice/shake triggers, GPS tracking.

• Backend Server: Manages SOS alerts and user tracking.

• Firebase: Real-time data storage and syncing.

• Real-Time Communication (Twilio): Sends SMS alerts even without internet.

• Machine Learning Engine: Predicts crime hotspots using historical data.

• Law Enforcement Integration: Shares live user location and incidents with police.

App Features & User Interface:

• Welcoming, user-friendly design with secure login.

• Personalized account setup includes “Panic Word” for discreet SOS activation and medical info.

• Central SOS button with emergency service shortcuts (Police, Hospital, Fire).

• Confirmation popup to prevent false alarms.

• Quick Actions for discreet evidence capture and location sharing.

• “Defence Tips” section with self-defense video tutorials to empower users.

Comparative Analysis:

• RescueNow outperforms many existing women’s safety apps by offering:

  • Real-time SOS and emergency SMS alerts.

  • Silent audio-video recording.

  • Police integration.

  • AI-driven prediction of safe and unsafe areas.

  • Self-defense educational content.

• Unlike others, it combines emergency response with preventive awareness and offline resilience.

Conclusion

The implementation of RescueNow demonstrates how technology, when thoughtfully designed, can play a transformative role in enhancing women’s safety. By integrating real-time SOS alerts, location tracking, silent recording, and machine learning-driven risk prediction, the system provides a holistic solution to address emergency scenarios and daily safety concerns. The application’s intuitive user interface, combined with robust backend infrastructure, ensures seamless user experience and effective emergency communication. Real-world testing confirmed the app\'s reliability in various environments—urban, low connectivity, and offline scenarios. The ability to record incidents silently and notify contacts instantly empowers users to take control of their safety in distress situations. Furthermore, integration with law enforcement channels and community reporting mechanisms adds depth to the system, fostering both individual and collective safety. While the current version of RescueNow fulfills its primary objective effectively, there is ample scope for future enhancements. These include: • Multilingual support to cater to diverse users across regions • Integration with wearable devices like smart bands for discreet activation • Advanced AI crime prediction models trained on regional datasets • A centralized dashboard for authorities to view and respond to live incidents • Emergency status broadcasting through public safety networks or local hubs In conclusion, RescueNow is not just a mobile app—it is a step toward empowering women through smart, proactive, and community-supported safety technologies.

References

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Copyright

Copyright © 2025 Sandip Shankar Pawar, Riya Roy, Kalyani Vikas Sawant, Rishu Bhagat Singh, Dr. Mahesh Navale. 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.