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

Understanding the role of burnout on nurses' quit intentions in the context of healthcare organizations of Hungary.

Scientific reports·2026
Same author

The Effect of Artificial Insemination and Multiple Ovulation Embryo Transfer on Production, Health Status, and Survival of Holstein-Friesian Cows.

Veterinary sciences·2026
Same author

Disrupting pegRNA intramolecular complementarity via PBS and spacer sequence alterations can enhance prime editing efficiency.

Nucleic acids research·2026
Same author

DenseLes: slice-wise dense network for multiple sclerosis lesion segmentation and classification.

Frontiers in neurology·2026
Same author

Assessment of pain and functional outcomes after lower limb amputation: a scoping review.

BMJ open·2026
Same author

Optimizing the mirror illusion during mirror therapy: evidence from unimpaired individuals.

Frontiers in psychology·2025

Related Experiment Video

Updated: Sep 21, 2025

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

4.2K

Deep-SAGA: a deep-learning-based system for automatic gaze annotation from eye-tracking data.

Oliver Deane1, Eszter Toth2, Sang-Hoon Yeo3

  • 1School of Sport, Exercise and Rehabilitation Sciences, The University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.

Behavior Research Methods
|June 1, 2022
PubMed
Summary
This summary is machine-generated.

Researchers can now automatically analyze real-world gaze data using a novel system combining eye-tracking and deep learning (Mask R-CNN). This automated method is faster and more versatile than manual annotation for in-the-wild navigation studies.

Keywords:
Deep learningGaze trackingMasked region-based convolutional neural networkObject detectionPortable eye-tracker

More Related Videos

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
07:48

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Published on: April 4, 2025

604
Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
07:09

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

10.8K

Related Experiment Videos

Last Updated: Sep 21, 2025

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

4.2K
Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
07:48

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Published on: April 4, 2025

604
Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
07:09

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

10.8K

Area of Science:

  • Human-Computer Interaction
  • Computer Vision
  • Cognitive Science

Background:

  • Portable eye-tracking enables naturalistic navigation research.
  • Manual annotation of eye-tracking data is time-consuming and lacks versatility for complex scenes.
  • Automated methods are needed for efficient analysis of in-the-wild gaze data.

Purpose of the Study:

  • To develop an automated system for identifying gaze focus in real-world navigation.
  • To combine head-mounted camera footage with eye-tracking data for object and location annotation.
  • To provide researchers with a versatile tool for analyzing dynamic gaze behavior.

Main Methods:

  • Utilized Masked Region-based Convolutional Neural Network (Mask R-CNN) for object detection in video footage.
  • Synchronized head-mounted camera recordings with eye-tracking device data.
  • Developed a system to automatically annotate gaze focus without manual intervention.

Main Results:

  • The automated system demonstrated high agreement with manual annotations.
  • The system processed data significantly faster than manual coding.
  • Case study confirmed the system's practicality in exploring attentional bias.

Conclusions:

  • The proposed system offers a robust and efficient method for analyzing real-world gaze data.
  • Automated annotation significantly accelerates data processing for navigation studies.
  • This technology facilitates deeper insights into gaze behavior in natural environments.