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

You might also read

Related Articles

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

Sort by
Same author

DeepRespNet: a hybrid attention-recurrent framework for non-contact respiratory rate estimation.

Frontiers in physiology·2026
Same author

Effects of ramped GVS parameter combinations on vestibular perception and their application in a Virtual Reality flight simulator.

Ergonomics·2026
Same author

High-strength and high-modulus silicon monoxide for high-energy-density and fast-charging lithium-ion batteries.

Nature communications·2026
Same author

Enhanced multicancer screening assay through whole-genome methylation sequencing-based multimodal cell-free DNA analysis.

Experimental & molecular medicine·2026
Same author

SMARCA4 activation engages FOSL1 to drive enhancer reprogramming and tumorigenic phenotypes in SMARCA4-deficient LUAD cells.

Cell death discovery·2026
Same author

Landauer-consistent interpretation of carrier mobility in quasi-ballistic field-effect transistors: overcoming the limitations of the Y-function method.

Nanoscale horizons·2026
Same journal

Correction: Komatsu et al. Three-Dimensional Visualization and Detection of the Pulmonary Venous-Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening. <i>Bioengineering</i> 2026, <i>13</i>, 100.

Bioengineering (Basel, Switzerland)·2026
Same journal

Comparison of CO<sub>2</sub> Laser and Microdebrider in the Surgical Treatment of Pediatric Recurrent Respiratory Papillomatosis: A Retrospective Analysis.

Bioengineering (Basel, Switzerland)·2026
Same journal

Toward More Translational Tumor Models: Breast dECM-Based 3D Systems Capture Native Microenvironmental Cues.

Bioengineering (Basel, Switzerland)·2026
Same journal

Postural Stability Changes During the 4 Phases of the Half Squat: Kinematics Profile of the Center of Pressure and Center of Mass in High-Performance Weightlifters-A Pilot Study.

Bioengineering (Basel, Switzerland)·2026
Same journal

Definite Implant Position as Novel Readout for Effectiveness of Ridge Preservation Indicates to Beneficial Effect of Combined Treatment with Platelet-Rich Fibrin (PRF) and Xenogenic Biomaterial in Bone Regeneration.

Bioengineering (Basel, Switzerland)·2026
Same journal

Trueness and Precision of Intraoral Scanners for 3D-Printed Orthodontic Models with Attachments: An In Vitro Comparative Study.

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

Related Experiment Video

Updated: May 25, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K

Contactless Fatigue Level Diagnosis System Through Multimodal Sensor Data.

Younggun Lee1, Yongkyun Lee2, Sungho Kim3

  • 1Department of Electronics and Communication Engineering, Republic of Korea Air Force Academy, 635 Danjae-ro, Sangdang-gu, Cheongju 28187, Republic of Korea.

Bioengineering (Basel, Switzerland)
|February 26, 2025
PubMed
Summary
This summary is machine-generated.

This study presents a new contactless system using AI and multimodal sensors to accurately diagnose fatigue in high-risk jobs. It offers real-time monitoring to enhance safety and performance in critical professions.

Keywords:
AI-classifiercontactlessfatigue levelmultimodalnon-invasivepersonalizedpre-missiontask performance

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

2.5K
Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

6.7K

Related Experiment Videos

Last Updated: May 25, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K
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

2.5K
Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

6.7K

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Occupational Safety

Background:

  • Fatigue poses significant risks in high-risk professions like aviation, firefighting, and healthcare, potentially causing severe accidents.
  • Existing fatigue assessment methods (surveys, physiological measures) lack real-time capabilities and user convenience.
  • There is a critical need for advanced, non-invasive fatigue monitoring solutions.

Purpose of the Study:

  • To develop and validate a novel contactless fatigue level diagnosis system.
  • To integrate multimodal sensor data (video, thermal, audio) with AI for fatigue assessment.
  • To provide real-time, personalized fatigue monitoring for critical sectors.

Main Methods:

  • Utilized multimodal sensor data (video, thermal imaging, audio) for non-contact biometric data collection.
  • Developed an AI-driven classification model to diagnose fatigue levels on a 1-5 scale.
  • Incorporated adaptive retraining with user feedback to enhance personalized accuracy.

Main Results:

  • Achieved an average accuracy of 89% in diagnosing fatigue levels.
  • Demonstrated an 11 percentage point increase in classification accuracy through user feedback-based retraining.
  • Validated hardware robustness in diverse operational conditions, including temperature and electromagnetic interference.

Conclusions:

  • The developed contactless system offers a practical and efficient solution for precise fatigue monitoring.
  • Real-time feedback and adaptive retraining significantly improve diagnostic accuracy.
  • This innovation enhances operational safety and performance in critical industries through non-invasive fatigue management.