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 Experiment Video

Updated: Sep 19, 2025

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

Smartphone eye-tracking with deep learning: Data quality and field testing.

Gancheng Zhu1, Zehao Huang1, Xiaoting Duan1

  • 1Center for Psychological Sciences, Zhejiang University, 148 Tianmushan Rd., Hangzhou, 310028, China.

Behavior Research Methods
|June 18, 2025
PubMed
Summary
This summary is machine-generated.

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

Inhibitory effects of salvianolic acid B on CCl(4)-induced hepatic fibrosis through regulating NF-κB/IκBα signaling.

Journal of ethnopharmacology·2012
Same author

Combined organic-inorganic fouling of forward osmosis hollow fiber membranes.

Water research·2012
Same author

Expression of the human glucokinase gene: important roles of the 5' flanking and intron 1 sequences.

PloS one·2012
Same author

Long term outcome after conservative and surgical treatment of haemorrhagic moyamoya disease.

Journal of neurology, neurosurgery, and psychiatry·2012
Same author

A role for low-abundance miRNAs in colon cancer: the miR-206/Krüppel-like factor 4 (KLF4) axis.

Clinical epigenetics·2012
Same author

Impact of the 2008 US Preventive Services Task Force recommendation to discontinue prostate cancer screening among male Medicare beneficiaries.

Archives of internal medicine·2012

Smartphone eye-tracking, powered by deep learning, offers comparable accuracy to gold-standard systems. This technology shows promise for detecting depressive symptoms with 76.67% accuracy in clinical applications.

Area of Science:

  • Computer Vision
  • Neuroscience
  • Mobile Health

Background:

  • Eye-tracking is crucial for attention measurement across various fields.
  • Advancements in AI and mobile computing enable computer vision-based eye tracking on smartphones.

Purpose of the Study:

  • To present a real-time smartphone eye-tracking system using deep neural networks.
  • To benchmark its performance against a gold-standard eye tracker.
  • To evaluate its potential in clinical applications, specifically for depressive symptom assessment.

Main Methods:

  • Developed a deep neural network trained on 7.4 million facial images for eye tracking.
  • Benchmarked the system against an EyeLink eye tracker with 32 participants.
  • Conducted a field test with 98 volunteers using visual tasks on a smartphone to assess depressive symptoms.
Keywords:
Computer visionData qualityDepressive symptomsEye-trackingSmartphone

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

585
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

4.1K

Related Experiment Videos

Last Updated: Sep 19, 2025

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
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

585
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

4.1K

Main Results:

  • Smartphone eye-tracking demonstrated comparable accuracy (1.32° vs. 1.20°) to the EyeLink tracker, though with lower precision (0.177° vs. 0.028°).
  • The system achieved 76.67% accuracy in predicting depressive symptoms based on visual task performance.

Conclusions:

  • Smartphone eye-tracking provides quality data suitable for scientific and clinical use.
  • This technology has significant potential for accessible and widespread application in mental health assessment.