Project Definition:
Drowsy driving is a major cause of road accidents, leading to fatalities and property damage. This project aims to develop an AI-enhanced Anti-Sleep Glasses system that detects driver drowsiness using infrared (IR) sensors and machine learning (ML). The glasses will monitor eye blinks and head movements in real time and trigger alerts if signs of fatigue are detected. Additionally, the system will interface with a vehicle’s dashboard to provide audio-visual warnings, improving driver awareness and road safety.

Research Goals:
• Develop a wearable AI-based drowsiness detection system using infrared sensors.
• Train an ML model to distinguish normal blinks from drowsiness-induced eye closure.
• Integrate the system with a computer-based dashboard alert system to notify the driver.
• Optimize the hardware and software for real-time processing.

Methodology:
o Use Infrared LED + Photodiode to track eye movements.
o Integrate Accelerometer/Gyroscope (MPU6050) for head movement tracking.
o Utilize Arduino Pro Mini / ESP32 for data processing and Bluetooth connectivity.
o Implement a buzzer/vibration motor for immediate driver alerts.
o Develop a dashboard-connected system using Raspberry Pi or a small Linux-based PC.

AI & Machine Learning Model Development:
o Collect eye blink and head movement datasets from test users.
o Train a Convolutional Neural Network (CNN) to classify drowsy vs. normal blinks.
o Use a Long Short-Term Memory (LSTM) network to detect patterns of drowsiness over time.
o Deploy the AI model on an Edge Device (Raspberry Pi / ESP32) for real-time processing.

Real-Time Alert & Dashboard Integration:
o Connect the glasses system to a car’s dashboard via Bluetooth/WiFi.
o Display visual warnings on the dashboard (e.g., “Driver Drowsy – Take a Break”).
o Trigger audible alerts via the car’s sound system.
o Provide a haptic (vibration) warning in the steering wheel.

Testing & Optimization
o Test the detection accuracy in various lighting and driving conditions.
o Evaluate response time for real-time alerts.
o Optimize power consumption for longer battery life.