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

Temporal RIT-Eyes: From Real Infrared Eye-Images to Synthetic Sequences of Gaze Behavior.

IEEE transactions on visualization and computer graphics·2022
Same author

OpenEDS2020 Challenge on Gaze Tracking for VR: Dataset and Results.

Sensors (Basel, Switzerland)·2021
Same author

Automated detection and sorting of microencapsulation via machine learning.

Lab on a chip·2019
Same author

Emergent gamma synchrony in all-to-all interneuronal networks.

Frontiers in computational neuroscience·2015
Same author

Genesis of interictal spikes in the CA1: a computational investigation.

Frontiers in neural circuits·2014
Same author

Computational modeling of channelrhodopsin-2 photocurrent characteristics in relation to neural signaling.

Bulletin of mathematical biology·2013

Related Experiment Video

Updated: Oct 30, 2025

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

11.1K

Ultrasound for Gaze Estimation-A Modeling and Empirical Study.

Andre Golard1, Sachin S Talathi1

  • 1Facebook Reality Labs, Redmond, WA 98052, USA.

Sensors (Basel, Switzerland)
|July 2, 2021
PubMed
Summary
This summary is machine-generated.

This study explores ultrasound for eye tracking in augmented reality glasses, offering a light-insensitive alternative. Researchers successfully modeled and tested an ultrasound system, achieving accurate gaze estimation with a machine learning model.

Keywords:
CMUTComsol ModelingGradient Boosted Regression TreesMachine Learningeye trackinggaze estimationultrasound

More Related Videos

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.3K
Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
05:49

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

1.0K

Related Experiment Videos

Last Updated: Oct 30, 2025

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

11.1K
Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.3K
Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
05:49

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

1.0K

Area of Science:

  • Biomedical Engineering
  • Sensor Technology
  • Human-Computer Interaction

Background:

  • Conventional light-based eye tracking methods struggle with ambient light variations, limiting outdoor applications, particularly for augmented reality (AR) glasses.
  • Ultrasound sensing presents a promising, low-power, and light-insensitive alternative for eye tracking, overcoming limitations of camera-based systems.

Purpose of the Study:

  • To model the integration of ultrasound sensors into an AR glasses form factor for eye-gaze estimation.
  • To evaluate the feasibility of using ultrasound for gaze tracking in various configurations.

Main Methods:

  • Developed a model for integrating ultrasound sensors into AR glasses.
  • Designed a benchtop experimental setup to collect time-of-flight and amplitude data of reflected ultrasound waves from a model eye across different gaze angles.
  • Utilized a gradient-boosted tree machine learning regression model with empirical data for gaze estimation.

Main Results:

  • Demonstrated effective eye-gaze estimation using ultrasound signals.
  • Achieved a gaze root-mean-square error (RMSE) of 0.965 ± 0.178 degrees.
  • Obtained a high adjusted R-squared score of 90.2 ± 4.6, indicating strong model performance.

Conclusions:

  • Ultrasound technology is feasible for eye-gaze estimation in AR glasses.
  • The developed low-complexity machine learning model effectively processes ultrasound data for accurate gaze tracking.
  • This approach offers a viable, light-insensitive solution for eye tracking in diverse environments.