Jove
Visualize
Contact Us

Related Concept Videos

Narcolepsy01:07

Narcolepsy

117
Narcolepsy is a chronic sleep disorder characterized by pervasive, uncontrolled sleepiness and other sleep disturbances. One of its hallmark symptoms is an abrupt transition to REM sleep upon falling asleep, which causes symptoms typically associated with this phase to occur unexpectedly during wakefulness. These include the following symptoms, which typically last from a minute or two to half an hour.
117

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Air quality index prediction using machine learning regression models: A comparative analysis.

PloS one·2026
Same author

Multi-Scale Attention Fusion With Depthwise Separable Convolutions for Efficient Skin Cancer Detection.

Journal of cutaneous pathology·2025
Same author

Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images.

Scientific reports·2025
Same author

Fundus image classification using feature concatenation for early diagnosis of retinal disease.

Digital health·2025
Same author

Enhancing Typhoid Fever Diagnosis Based on Clinical Data Using a Lightweight Machine Learning Metamodel.

Diagnostics (Basel, Switzerland)·2025
Same author

Ventilator pressure prediction employing voting regressor with time series data of patient breaths.

Health informatics journal·2025
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jul 11, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

564

Detection of Drowsiness among Drivers Using Novel Deep Convolutional Neural Network Model.

Fiaz Majeed1, Umair Shafique1, Mejdl Safran2

  • 1Department of Information Technology, University of Gujrat, Gujrat 50700, Pakistan.

Sensors (Basel, Switzerland)
|November 14, 2023
PubMed
Summary

This study introduces a new deep neural network for detecting driver drowsiness using facial movements. The proposed convolutional neural network (CNN) model achieves high accuracy, enhancing road safety through improved yawn detection.

Keywords:
advanced driver assistance systemsdeep learningdrowsiness detectionneural networkroad safety

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K
Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.5K

Related Experiment Videos

Last Updated: Jul 11, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

564
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K
Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.5K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Transportation Safety

Background:

  • Driver drowsiness is a major cause of road accidents.
  • Existing drowsiness detection systems require improvement for accuracy.
  • Behavioral features like mouth and eye movement are key indicators of fatigue.

Purpose of the Study:

  • To propose a novel deep neural network architecture for driver drowsiness detection.
  • To enhance traffic safety by accurately identifying drowsy drivers.
  • To leverage facial behavioral features for improved detection accuracy.

Main Methods:

  • A convolutional neural network (CNN) architecture was developed for drowsiness detection.
  • The DLIB library was used to extract facial landmarks and calculate the mouth aspect ratio (MAR).
  • Data augmentation techniques were applied to address dataset limitations for 'yawning' and 'no_yawning' classes.

Main Results:

  • The proposed CNN model achieved an average accuracy of 96.69% on the drowsiness detection task.
  • Training and testing on both original and augmented datasets demonstrated the impact of data augmentation on performance.
  • The model showed superior performance compared to existing state-of-the-art approaches.

Conclusions:

  • The developed CNN model offers a highly accurate solution for driver drowsiness detection.
  • The approach effectively utilizes facial behavioral features (MAR) for fatigue monitoring.
  • This research contributes to developing more robust and reliable driver safety systems.