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A CNN-Based Wearable System for Driver Drowsiness Detection.

Yongkai Li¹, Shuai Zhang¹, Gancheng Zhu¹

¹Center for Psychological Sciences, Zhejiang University, Hangzhou 310028, China.

Sensors (Basel, Switzerland)

|April 13, 2023

Summary

This summary is machine-generated.

This study introduces a wearable glass prototype with a lightweight neural network to detect driver drowsiness by measuring eye blinks. This system offers a privacy-preserving and computationally efficient solution for road safety.

Keywords:

convolution neural network driving drowsiness detection glass lightweight

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Area of Science:

Human-Computer Interaction
Road Safety Technology
Biometric Sensing

Background:

Driver drowsiness is a significant risk to road safety.
Current in-cabin sensing technologies face challenges with privacy and environmental constraints.
Contactless sensing via cameras requires complex algorithms for user and environmental variations.

Purpose of the Study:

To develop a lightweight convolution neural network for detecting driver drowsiness.
To present a wearable glass prototype for contactless eye-blink monitoring.
To offer a computationally efficient solution for real-world driver alertness applications.

Main Methods:

A wearable glass prototype with integrated cameras using a hot mirror design was developed.
A lightweight convolution neural network was employed to analyze eye images for blink detection.
Performance was validated against an industrial gold standard EyeLink eye-tracker.

Main Results:

The wearable glass prototype demonstrated high effectiveness in detecting eye blinks.
Blink rate measurements from the prototype showed strong consistency with the EyeLink eye-tracker.
The system proved to be a viable solution for monitoring driver alertness.

Conclusions:

The developed wearable glass prototype and lightweight neural network provide an effective method for detecting driver drowsiness.
This approach offers a computationally efficient and privacy-conscious alternative to existing driver monitoring systems.
The technology has the potential to significantly enhance road safety by mitigating drowsiness-related accidents.