Jove
Visualize
Contact Us
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 Concept Videos

Sedatives and Hypnotics: Overview01:23

Sedatives and Hypnotics: Overview

Sedatives are drugs that alleviate anxiety, while hypnotics induce sleep. Both classes of medication suppress neuronal activity, leading to a calming effect for sedatives and facilitating sleep for hypnotics.
Sedative-hypnotics are categorized into barbiturates, benzodiazepines (BZDs), and non-benzodiazepines or Z-drugs. These drugs work by suppressing central nervous system activity, and this suppression is dose-dependent. Older sedative medications, like barbiturates, follow a linear curve in...
Sedatives and Hypnotics Drugs: Miscellaneous Agents01:17

Sedatives and Hypnotics Drugs: Miscellaneous Agents

Sedatives and hypnotics encompass a wide range of substances, each with its unique mechanism of action, uses, and potential adverse effects.
Melatonin congeners like ramelteon (Rozerem) and tasimelteon (Hetlioz) selectively bind to melatonin receptors (MT1 and MT2) and thus mimic the actions of melatonin, a hormone that regulates sleep-wake cycles. Tasimelteon is primarily used for non-24-hour sleep-wake disorder, common in blind patients. They are also used to treat conditions like insomnia...
Stages of Sleep01:22

Stages of Sleep

Sleep progresses through distinct stages, each characterized by specific brain wave patterns and physiological responses ranging from wakefulness to stages of non-rapid eye movement, known as non-REM, to rapid eye movement, referred to as REM. Understanding these stages helps in recognizing how sleep supports various bodily and cognitive functions.
Before sleep begins, in wakefulness, the brain exhibits primarily beta waves, which are high in frequency and low in amplitude, indicating alertness...
Sleepwalking and Sleep Talking01:17

Sleepwalking and Sleep Talking

Somnambulism, commonly known as sleepwalking, involves individuals engaging in activities ranging from simple walking to more complex behaviors such as driving. Sleepwalking typically occurs during the slow-wave sleep stages 3 and 4 early in the night when the person is not dreaming, contradicting the myth that sleepwalkers are acting out their dreams.
Factors that increase the likelihood of sleepwalking include sleep deprivation and alcohol consumption. Contrary to common beliefs, it is safe...
REM Sleep Behavior Disorder01:15

REM Sleep Behavior Disorder

REM Sleep Behavior Disorder (RBD) is a sleep disorder characterized by the absence of muscle paralysis that normally occurs during the REM phase of sleep. This absence allows individuals to physically act out their dreams, which are often vivid and disturbing. Common behaviors exhibited during episodes include kicking, punching, and yelling. These actions can be dangerous, potentially leading to injuries for the person with RBD or their bed partner.
RBD is significantly associated with...
Narcolepsy01:07

Narcolepsy

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.

You might also read

Related Articles

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

Sort by
Same author

How Good Is the Machine at the Imitation Game? On Stylistic Characteristics of AI-Generated Images.

Journal of imaging·2025
Same author

Toward Convenient and Accurate IMU-Based Gait Analysis.

Sensors (Basel, Switzerland)·2025
Same author

Parallax Inference for Robust Temporal Monocular Depth Estimation in Unstructured Environments.

Sensors (Basel, Switzerland)·2022
Same author

Scaling up SoccerNet with multi-view spatial localization and re-identification.

Scientific data·2022
Same author

Asynchronous Semantic Background Subtraction.

Journal of imaging·2021
Same author

Survey and Synthesis of State of the Art in Driver Monitoring.

Sensors (Basel, Switzerland)·2021
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jul 2, 2026

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects
11:12

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

Published on: September 18, 2012

Multi-Timescale Drowsiness Characterization Based on a Video of a Driver's Face.

Quentin Massoz1, Jacques G Verly2, Marc Van Droogenbroeck3

  • 1Department of Electrical Engineering and Computer Science, Faculty of Applied Science, University of Liège, B-4000 Liège, Belgium. quentin.massoz@uliege.be.

Sensors (Basel, Switzerland)
|August 29, 2018
PubMed
Summary
This summary is machine-generated.

This study presents a multi-timescale system to detect driver drowsiness using facial cues. It achieves high accuracy across different timescales, improving safety in transportation.

Keywords:
convolutional neural networkdriver monitoringdrowsinesseye closure dynamicsmulti-timescalepsychomotor vigilance taskreaction time

More Related Videos

Polygraphic Recording Procedure for Measuring Sleep in Mice
08:45

Polygraphic Recording Procedure for Measuring Sleep in Mice

Published on: January 26, 2016

A Chronic Sleep Fragmentation Model using Vibrating Orbital Rotor to Induce Cognitive Deficit and Anxiety-Like Behavior in Young Wild-Type Mice
06:23

A Chronic Sleep Fragmentation Model using Vibrating Orbital Rotor to Induce Cognitive Deficit and Anxiety-Like Behavior in Young Wild-Type Mice

Published on: September 22, 2020

Related Experiment Videos

Last Updated: Jul 2, 2026

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects
11:12

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

Published on: September 18, 2012

Polygraphic Recording Procedure for Measuring Sleep in Mice
08:45

Polygraphic Recording Procedure for Measuring Sleep in Mice

Published on: January 26, 2016

A Chronic Sleep Fragmentation Model using Vibrating Orbital Rotor to Induce Cognitive Deficit and Anxiety-Like Behavior in Young Wild-Type Mice
06:23

A Chronic Sleep Fragmentation Model using Vibrating Orbital Rotor to Induce Cognitive Deficit and Anxiety-Like Behavior in Young Wild-Type Mice

Published on: September 22, 2020

Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Transportation Safety

Background:

  • Drowsiness significantly contributes to fatal transportation accidents.
  • Real-time drowsiness detection systems are crucial for driver safety.
  • Current camera-based methods face a trade-off between detection accuracy and responsiveness.

Purpose of the Study:

  • To develop a multi-timescale system for accurate and responsive drowsiness characterization.
  • To address the accuracy-responsiveness trade-off in drowsiness detection.
  • To enhance driver warning systems for preventing accidents.

Main Methods:

  • Developed a multi-timescale drowsiness characterization system with four binary classifiers.
  • Classifiers operated at distinct timescales: 5 s, 15 s, 30 s, and 60 s.
  • Introduced a novel multi-timescale ground truth based on Psychomotor Vigilance Tasks (PVTs) reaction times.

Main Results:

  • Achieved global accuracies of 70%, 85%, 89%, and 94% for the four classifiers.
  • Demonstrated strong performance across varying timescales, balancing accuracy and responsiveness.
  • Validated the system on 29 subjects using leave-one-subject-out cross-validation.

Conclusions:

  • The multi-timescale system effectively characterizes drowsiness with adaptable accuracy-responsiveness trade-offs.
  • This approach enhances the potential for timely and accurate driver drowsiness warnings.
  • The system shows promise for improving safety in transportation and other critical domains.