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Driver Drowsiness Monitoring System (DMS)

Overview

The Driver Drowsiness Monitoring System (DMS) is an IoT-based solution designed to enhance road safety by detecting driver drowsiness in real-time. The system uses hardware components such as the ESP32 microcontroller, ESP32-CAM module, and GPS-NEO-6M module, alongside cloud services like Firebase and ThingSpeak for data storage and visualization. The system captures driver face images and vehicle location to detect drowsiness, and it provides real-time monitoring via a web dashboard.

This project was completed as part of the PUSL2022 - Introduction to IoT module at the University of Plymouth.

Key Components

  • ESP32 Microcontroller: Central control unit for coordinating modules.
  • ESP32 CAM Module: Captures driver face images for drowsiness detection.
  • GPS-NEO-6M Module: Tracks vehicle location and speed.
  • Firebase Realtime Database: Logs vehicle data and drowsiness events.
  • ThingSpeak: Cloud platform for real-time data visualization.
  • Web Interface: Dashboard for visualizing real-time data.

System Architecture

  1. Hardware:

    • ESP32 Microcontroller
    • ESP32 CAM Module
    • GPS-NEO-6M Module

  2. Software:

    • Firebase for backend management
    • ThingSpeak for real-time monitoring
    • Web-based dashboard with HTML, CSS, and JavaScript

  3. Data Flow:

    • Data is captured from sensors (GPS, camera) by the ESP32.
    • Drowsiness detection is performed locally on the ESP32-CAM images.
    • Data is logged to Firebase and visualized in ThingSpeak.

Database Design

  • Schema:
    • drowsiness_events: Stores event details.
    • Child Nodes: Each event contains:
      • latitude: Vehicle’s latitude.
      • longitude: Vehicle’s longitude.
      • speed: Vehicle’s speed.
      • timestamp: Event time.

Deployment

  • Hybrid Hosting: Combines on-premises servers and cloud services (Firebase, ThingSpeak) for real-time data storage and visualization.
  • Infrastructure: Includes servers, virtualization for resource optimization, and seamless networking.

Testing

  • Unit Testing: Validates individual components (ESP32, sensors, backend).
  • Integration Testing: Verifies interactions between hardware, cloud services, and dashboard.
  • System Testing: Ensures end-to-end functionality.

Dependencies

  • Libraries:
    • OpenCV, dlib for facial detection and drowsiness monitoring.
    • Firebase Admin SDK, ThingSpeak API for cloud interaction.
    • Arduino libraries for Wi-Fi, HTTPClient, and sensor control.
  • External Files:
    • Model files, alarm sounds, HTML for dashboard, Firebase credentials.

Communication Protocols

  • Wi-Fi & HTTP: Enables communication between hardware and the system.
  • Real-time Data Streaming: Streams sensor data for immediate action.

Contributors

  • Sinel Nemsara
  • Thejan Rajapaksha
  • Yohan Nanayakkara
  • Sachitha Eshan
  • Charith Bandara
  • Devin Fernando

Conclusion

The DMS effectively integrates hardware and cloud technologies to monitor driver behavior, detect drowsiness, and improve road safety through real-time monitoring and data visualization.