Driver Drowsiness and Distraction Monitor
Real-time facial landmark analyzer tracking Eye Aspect Ratio (EAR) and head pose to sound drowsiness alarms.
Project Overview
Uses OpenCV and MediaPipe facial mesh running on edge devices. Measures blink duration, yawning frequency, and phone-distraction head tilts, triggering immediate auditory alerts to prevent vehicular accidents. ### Practical Student Engineering Solution Fully functional hardware demonstration prototype equipped with dedicated microcontrollers, precision sensors, actuators, and an interactive cloud/mobile telemetry dashboard. ### Key Learning Outcomes - Embedded C / MicroPython firmware architecture and sensor interfacing - IoT telemetry protocols (MQTT / HTTP / BLE / LoRaWAN) - Power regulation, hardware debugging, and PCB design principles - Comprehensive technical documentation aligned with IEEE academic standards
Modern commercial and industrial infrastructure requires automated, real-time sensing and telemetry. Conventional manual monitoring suffers from high latency, human error, and lack of predictive fault visibility for Driver Drowsiness and Distraction Monitor.
A turnkey engineering prototype integrating high-precision sensor modules, dedicated microcontroller unit, and cloud telemetry protocols to automate measurement, trigger alert thresholds, and provide remote dashboard control.