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

Contrast use and radiation exposure during transcatheter aortic valve implantation according to valve design.

Kardiologia polska·2026
Same author

Simultaneous transcatheter edge-to-edge repair (TEER) for severe mitral and tricuspid regurgitation is feasible, safe, and associated with good clinical outcome.

PloS one·2026
Same author

Electrophysiological Correlates for the Detection of Haptic Illusions.

IEEE transactions on haptics·2025
Same author

Long-Term Follow-Up After Direct-Flow Transcatheter Aortic Valve Implantation: A Single Center Experience.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions·2025
Same author

4D flow MRI-based grading of left ventricular diastolic dysfunction: a validation study against echocardiography.

European radiology·2025
Same author

Long-term structural valve deterioration after TAVI: insights from the EORP ESC Valve Durability TAVI Registry.

EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology·2025
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: Oct 12, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.2K

Mobile Eye-Tracking Data Analysis Using Object Detection via YOLO v4.

Niharika Kumari1, Verena Ruf1, Sergey Mukhametov1

  • 1Physics Education Research Group, Physics Department, TU Kaiserslautern, 67663 Kaiserslautern, Germany.

Sensors (Basel, Switzerland)
|November 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces object recognition for mobile eye tracking in labs. YOLOv4 with optical flow offers fast, accurate object detection, simplifying data analysis and enabling real-time responses.

Keywords:
Faster R-CNNYOLOeye movementseye trackingobject detectionphysics experiments

More Related Videos

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.8K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K

Related Experiment Videos

Last Updated: Oct 12, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.2K
A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.8K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K

Area of Science:

  • Educational Technology
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Remote eye tracking is crucial for analyzing learning processes in real-world settings.
  • Mobile eye trackers offer greater flexibility than stationary ones but face analysis challenges.
  • Manual analysis of mobile eye-tracking data is time-consuming and impractical for dynamic environments.

Purpose of the Study:

  • To explore using object recognition models for assigning mobile eye-tracking data to real objects.
  • To evaluate the efficiency and accuracy of different Convolutional Neural Networks (CNNs) for this task.
  • To simplify the analysis of mobile eye-tracking data in authentic student lab courses.

Main Methods:

  • Comparison of three CNN models: Faster Region-Based CNN, YOLOv3, and YOLOv4.
  • Integration with optical flow estimation for enhanced object detection.
  • Application within an authentic student laboratory course setting.

Main Results:

  • YOLOv4, combined with optical flow estimation, demonstrated the highest accuracy and fastest results for object detection.
  • Automatic assignment of gaze data to objects significantly simplifies data analysis.
  • This approach enables potential real-time system responses based on user gaze.

Conclusions:

  • Object recognition models, particularly YOLOv4, can effectively automate mobile eye-tracking data analysis in educational settings.
  • This automation addresses the limitations of manual analysis, making larger studies feasible.
  • Several challenges in applying object detection to mobile eye-tracking data were identified and discussed.