Documentation for Raptor

Introduction

Raptor is an AI-powered smart helmet designed to enhance rider safety through real-time accident detection and emergency alert systems. The helmet integrates IoT sensors, AI-based crash detection, and automated emergency response mechanisms to ensure immediate assistance in case of an accident.

With the rising number of road accidents involving two-wheelers, many riders fail to receive timely medical attention, leading to severe consequences. Raptor aims to solve this by automatically detecting crashes and alerting emergency contacts with real-time location details.

Project Motivation

Road safety is a critical issue worldwide, especially for motorcyclists. While conventional helmets provide physical protection, they lack smart features to assist riders in emergencies. Raptor goes beyond traditional helmet designs by integrating AI-driven crash detection and real-time communication to save lives.

Key Features & Implementation

1. AI-Based Crash Detection System Accelerometer & Gyroscope Sensors detect sudden impact, abrupt
motion changes, and helmet tilt angles, determining if a crash has occurred.

→ AI algorithms analyze sensor data to distinguish between a normal fall and a severe accident.

→ The system prevents false alarms by using Impact Threshold models trained on real-world riding data.

2. Emergency Alert & Live Location Sharing Upon detecting an accident, Raptor automatically sends an SOS alert to emergency contacts.

→ The alert includes the rider’s live GPS location, allowing responders to reach the accident site quickly.

→ Uses ESP-32 module for real-time location tracking, even in low-connectivity areas.

3. Fall & Unconsciousness Detection If a rider falls but doesn’t move for a certain period, the system assumes unconsciousness and triggers emergency alerts.

→ Integrated pulse sensor & body temperature monitor can detect abnormal rider conditions.

4. Smart Helmet Lock System (Prevention Feature) The helmet has an ignition-locking mechanism, ensuring the bike won’t start unless the helmet is worn.

→ Uses RFID/NFC authentication for secure user verification before ignition.

5. Mobile App Integration for Alerts & Tracking The helmet connects to a mobile app via Bluetooth, allowing riders to:

→ View ride analytics & helmet status.

→ Receive battery & sensor health alerts.

→ Customize emergency contact details.

→ The app is designed with a clean, user-friendly UI, ensuring easy navigation and real-time monitoring.

6. Voice Assistance & Hands-Free Communication Built-in Bluetooth speakers & microphone allow for hands-free calls and navigation assistance.

→ AI voice commands enable riders to control features without distraction.

Resolution

Challenges & Solutions

→ Accurate Crash Detection: Implemented AI-driven pattern recognition to reduce false positives.

→ Ensuring Instant Emergency Response: Optimized ESP-32 module for fast and reliable connectivity.

→ User Comfort & Battery Life: Used a lightweight battery pack with optimized power consumption, ensuring long-lasting operation without adding extra weight.

Application

Expected Impact & Real-World Application.

→ Increases rider safety by ensuring quick medical response.

→ Prevents unauthorized ignition through helmet authentication.

→ Reduces accident fatalities by leveraging AI for faster emergency assistance.

Tech Stack

Tech Stack & Tools Used

→ Hardware: ESP32, MPU6050 (Accelerometer & Gyroscope), RFID/NFC Sensor, Pulse Sensor.

→ Software: Kotlin (for mobile app), C (for AI crash detection algorithms).

→ Database & Cloud: Firebase for real-time alerts & Supabase for emergency logs.