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Related Experiment Video

Updated: Dec 13, 2025

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

A Portable Fuzzy Driver Drowsiness Estimation System.

Alimed Celecia¹, Karla Figueiredo², Marley Vellasco¹

¹Electrical Engineering Department, PUC-Rio, Rio de Janeiro 22451900, Brazil.

Sensors (Basel, Switzerland)

|July 29, 2020

Summary

This summary is machine-generated.

This study introduces a portable, low-cost driver drowsiness detection system using a Raspberry Pi. The system accurately identifies fatigue levels, enhancing road safety and preventing accidents.

Keywords:

Raspberry Pi drowsiness detection drowsiness measures embedded hardware eyes closing detection fuzzy inference system

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Area of Science:

Engineering
Computer Science
Transportation Safety

Background:

Automatic driver fatigue detection is crucial for preventing traffic accidents.
Existing portable systems face limitations in accuracy, adaptability, and real-time performance.
Computationally powerful embedded systems like Raspberry Pi offer potential for low-cost, real-time solutions.

Purpose of the Study:

To develop a portable, low-cost, accurate, and robust drowsiness recognition device.
To leverage a fuzzy inference system on a Raspberry Pi for real-time driver fatigue monitoring.
To combine complementary drowsiness indicators for improved detection accuracy.

Main Methods:

Utilized a Raspberry Pi for real-time processing.
Integrated a fuzzy inference system to combine drowsiness measures.
Derived complementary indicators from eye states (PERCLOS, ECD) and mouth state (AOT).
Classified drowsiness into three states: Low-Normal, Medium-Drowsy, and High-Severe.

Main Results:

Achieved a significant accuracy of 95.5% in drowsiness state recognition.
Demonstrated real-time response capability.
The system proved robust to varying conditions like illumination changes and visual occlusion.

Conclusions:

The developed fuzzy inference system on Raspberry Pi is a feasible and effective approach for real-time, low-cost driver drowsiness detection.
The system's high accuracy and portability offer a promising solution for enhancing road safety.
This technology has the potential to become a standard tool for preventing fatigue-related traffic accidents.