Mehfooz-E-Amm

Screenshots

Overview

Mehfooz-E-Amm is a comprehensive personal safety application designed to provide security in high-risk environments. It combines real-time location tracking with AI-driven accident detection to ensure immediate assistance during emergencies. The app empowers users with proactive alerts about danger zones and a one-tap SOS mechanism for rapid response.

24/7

Safety Monitoring

AI

Accident Detection

SOS

Instant Alert

Problem Statement

Personal safety in unpredictable environments is a growing concern. Citizens face:

Lack of immediate help during accidents or threats
Unawareness of high-crime or dangerous areas
Delayed communication with trusted contacts in emergencies
Inefficient emergency response coordination

Solution

An intelligent safety ecosystem that provides:

Live Location Sharing with encrypted tracking for trusted contacts
High-Risk Area Mapping using historical crime/accident data analysis
Automated Accident Detection using mobile sensors and AI models
Proactive Alerts when entering unsafe zones
One-Tap SOS to instantly notify authorities and family

System Architecture

Core Technologies

1

Mobile App

Native Android (Kotlin) with Google Maps integration

2

Backend API

FastAPI (Python) for high-performance request handling

3

AI Engine

ML models for accident detection (accelerometer/gyroscope data)

4

Real-Time Data

Firebase Realtime Database for live tracking

5

Mapping Services

Google Maps & OpenStreetMap APIs for navigation and zoning

Key Features

📍 Live Safety Tracking

Securely share real-time location with family/friends. Configurable privacy durations ensure user control.

⚠️ High-Risk Mapping

Visual heatmaps of dangerous areas generated from crime statistics and user reports. Alerts users upon entry.

🚗 AI Accident Detection

Analyzes sensor data (speed changes, impact force) to detect crashes and automatically triggers emergency protocols.

🆘 One-Tap SOS

Instant trigger widget accessible from lock screen to send location and distress signal to emergency contacts.

Advanced Implementation

AI-Powered Accident Detection

Sophisticated machine learning pipeline combining multiple sensor inputs:

Sensor Fusion: Real-time analysis of accelerometer, gyroscope, and GPS data at 100Hz sampling rate
Feature Extraction: Jerk analysis, impact force magnitude, sudden velocity changes, orientation shifts
ML Classification: Random Forest ensemble trained on 10,000+ labeled accident scenarios
Confidence Thresholding: 85%+ confidence triggers automated emergency protocols
False Positive Reduction: User confirmation window (30s) with shake-to-cancel gesture

Real-Time Communication Architecture

WebSocket Connections: Persistent bi-directional channels for sub-second location updates

Firebase Cloud Messaging: Push notifications with geofencing triggers and zone alerts

Location Batching: Optimized battery usage with adaptive polling (1-30s intervals based on movement)

Offline Queue: Local caching of SOS requests with automatic retry when connectivity restored

Security & Privacy

End-to-End Encryption: AES-256 for location data, RSA-2048 for key exchange

Permission Management: Granular controls for trusted contacts with time-limited access tokens

Data Anonymization: Heatmap generation uses aggregated, de-identified location clusters

Secure Storage: Android Keystore integration for credential protection