Safe Alert App

Abstract

Human safety in various environments is a growing concern, and ensuring timely alerts to guardians or authorities can play a vital role in preventing harm. This project introduces an intelligent application that integrates Real-Time Monitoring to analyze human emotions and detect potential threats based on environmental events. The system evaluates emotional states by analyzing voice inputs using Sentiment Analysis Machine Learning techniques. In addition, a Prediction Algorithm is employed to assess the network signal and predict disconnection or movement patterns. Upon detecting abnormal events or emotional distress, the system sends alert messages, including the exact location, to the registered guardian. Furthermore, with OpenCV algorithms, the system enhances its threat detection capabilities through visual analysis. The collected data, such as incident recordings, location, date, and time, are transmitted securely, which can later be used as evidence for police complaints or emergency responses. This multi-layered protection system ensures rapid response, thereby helping to protect individuals in real time.

Introduction

Overview

The Safe Alert App is a mobile application designed to enhance personal safety through real-time monitoring, emotion recognition, and AI-based threat detection. It aims to protect vulnerable individuals—especially women, children, students, and travelers—by analyzing emotional distress, detecting threats, and alerting guardians or authorities instantly.

Key Features & Functionality

1. Voice Recognition Module

   • Uses speech recognition (via Android Speech API) to convert voice input into text.

   • Analyzes emotional tone (sentiment analysis) to detect distress.

   • Triggers alerts if unusual behavior is detected.

2. Location Detection Module

   • Captures real-time location using GPS and converts it into a human-readable address.

   • Sends Google Maps links to guardians/emergency contacts.

3. SMS Sending Module

   • Sends automated or manual emergency messages with embedded text and location details.

   • Uses SmsManager and Intent to notify emergency contacts.

4. Evidence Database Module (Optional)

   • Stores recognized speech, timestamp, and location in a local SQLite database.

   • Helps with record-keeping and can be accessed later as evidence.

Advanced Capabilities

• AI & ML Integration:

  • Uses advanced models like BERT, Wav2Vec 2.0, YOLO, LSTM, and XGBoost for:

    • Sentiment/emotion detection.

    • Visual threat detection via OpenCV and YOLO.

    • Network signal prediction for message delivery in low-connectivity areas.

• Real-Time Alerts:

  • Automatically detects threats and sends alerts with timestamp, location, and evidence (text/audio/video).

• Multienvironment Use:

  • Designed for use in public transport, schools, streets, and private spaces.

Related Work & Literature Support

The system draws inspiration from similar research projects:

• Wearable safety devices, voice monitoring apps, gesture-based SOS, and AI-powered threat prediction.

• Studies emphasize the effectiveness of integrating real-time data, AI algorithms, and automated emergency responses.

Proposed System Architecture

The app is structured into independent but interconnected modules handling:

• Emotion detection

• Location tracking

• Alert transmission

• Data logging

Results & Observations

• The app successfully generates and logs multiple emergency alerts with accurate timestamps.

• Alerts are sent in real time, enabling immediate response.

• The system shows continuous monitoring capability with fast reaction to multiple distress events.

Conclusion

The Safe Alert App successfully achieves its goal of enhancing personal safety through real-time monitoring, emotion analysis, and automated emergency alerts. By integrating advanced machine learning models like BERT, Wav2Vec 2.0, YOLO, and LSTM, the system is capable of accurately detecting distress, identifying threats, and predicting network conditions to ensure timely and reliable alert delivery.Through continuous voice and environment monitoring, the app can detect emotional changes and unusual events. When a potential threat is identified, the app promptly sends an alert to a designated guardian, including crucial information such as time, location (when available), and incident recordings. Even in scenarios where GPS fails or network connectivity is weak, the system is designed to adapt and ensure alert delivery, maintaining a high level of reliability and responsiveness.The experimental results validate the app’s performance in real-world conditions, demonstrating high accuracy in emotion detection, effective threat identification, and fast alert transmission. While certain challenges like inconsistent location retrieval still exist, the system lays a solid foundation for real-time safety solutions.

References

[1] D.Vijendra Babu , 2022, Automatic Prediction and Identification of Smart Women Safety Wearable Device Using Dc-RFO-IoT [2] Md. Ashikuzzaman, 2024, DangerDet:A mobile application-based danger detection platform for women and children using deeplearning [3] Geeta Brijwani,2023, Artificial Intelligence: A Smart and Empowering Approach to Women\'s Safety [4] Dr.Sridhar Mandapati, 2015, A Mobile Based Women Safety Application (I Safe Apps) [5] D. G. Monisha, 2016, Women Safety Device and Application-FEMME [6] Dr.N. Tamilselvi, 2021, Sheltered in Safe Hands - A Study on the Usage and Effectiveness of ‘Kavalan’ SOS App among Women in Tamil Nadu [7] Manikanta Korrapati , 2024, AI-Enabled Predictive Analytics for Women\'s Safety: From Threat Detection to Incident Prevention [8] Mazlina Md Mustaffa,2022, Development of women safety applications [9] Sanjana Mukherjee, 2017, Advanced women security app: We’RSafe [10] Rahul Mange, 2018, Women Safety Android App

Copyright

Copyright © 2025 Dr. R. Priya, Chiranchievi S, Santhosh M, Udhaya Parameshwaran M, Tamizhbala S. 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.