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Validation of an Automated Scoring Algorithm That Assesses Eye Exploration in a 3-Dimensional Virtual Reality

Or Koren¹, Anais Di Via Ioschpe^1,2, Meytal Wilf^1,3

¹Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan 52621, Israel.

Sensors (Basel, Switzerland)

|July 24, 2025

Summary

This summary is machine-generated.

This study validates an automated eye-tracking algorithm for virtual reality (VR) head-mounted displays (HMDs). The algorithm accurately measures gaze behavior on static and dynamic areas of interest, reducing manual scoring time.

Keywords:

cognition eye tracking fixation time virtual reality

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Area of Science:

Neuroscience
Computer Science
Human-Computer Interaction

Background:

Eye-tracking in virtual reality (VR) is crucial for understanding behavior.
Manual scoring of gaze data is time-consuming and labor-intensive.
Previous automated algorithms lacked validation against manual scoring, especially with dynamic areas of interest (AOIs).

Purpose of the Study:

To validate an automated eye-tracking algorithm against manual scoring for temporal fixation behavior in VR.
To assess the algorithm's accuracy on both static and dynamic AOIs.
To establish a reliable automated system for analyzing gaze data in VR.

Main Methods:

An automated scoring algorithm was developed to determine time of first fixation (TOFF) and total fixation duration (TFD).
Ten participants were tested using a VR head-mounted display (HMD) with 36 static and dynamic AOIs.
Algorithm-generated TOFF and TFD were compared against subjective human annotations using interclass-correlation coefficient (ICC).

Main Results:

The automated algorithm demonstrated high accuracy, achieving ICC values ≥0.982 (p < 0.0001) for both TOFF and TFD.
The algorithm reliably measured temporal gaze parameters on static and dynamic AOIs.
Results indicate strong agreement between automated scoring and human annotation.

Conclusions:

The validated automated algorithm accurately quantifies temporal gaze behavior in VR HMDs.
This system can replace laborious manual scoring, saving significant time and resources.
The algorithm is suitable for research, including studies differentiating apathy from depression in Alzheimer's dementia patients.