Estimating User Gaze Depth Perception in Real -Time for Extended Reality Environments
Abstract
The progression of head-mounted displays (HMDs) to incorporate eye tracking in extended reality settings enables opportunities to develop adaptive HMDs capable of responding to user context and state to provide relevant information faster, which can result in faster training and better decision-making. The purpose of this research is to develop a method that allows for real-time estimation of perceptual depth in extended reality settings using eye-tracking data. In three different experimental conditions (virtual reality, augmented reality,and real), 13 human subjects fixated on targets at four varying depths while eye-tracking data was collected. Here, fixation periods are isolated and segmented in the eye-tracking data and the average inverse eye vergence angle (EVA) and interpupillary distance (IPD) are calculated. Using these two variables as features and the depth as the target, support vector machine (SVM), random forest, and XGBoost models exhibited mean classification accuracy of 50.1 , 48.9 , and 49.0 , respectively. Near/far classification for distances of 0.25 m versus 4.0 m yielded classification accuracies of 61.0 , 85.4 , and 85.6 for SVM, random forest, and XGBoost, respectively. We conclude that user perceptual depth can be estimated using EVA and IPD and adaptive HMDs are feasible using similar models for gaze depth estimation.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 30, 2023
- Accession Number
- AD1214025
Entities
People
- Nathan Villavicencio
- Russell Cohen Hoffing
Organizations
- California State University
- United States Army Research Laboratory