GazeIntent: Adapting dwell-time selection in VR interaction with real-time intent modeling

TL;DR

This paper introduces GazeIntent, a real-time gaze-based intent model that adapts dwell-time thresholds in VR to improve gaze-only selection, accounting for user experience and task variability.

Contribution

It presents a novel intent prediction technique using gaze data and demonstrates its effectiveness in adapting dwell times for improved VR interaction.

Findings

Personalized models for returning users are preferred by 63% of users.

Intent prediction models achieved an F1 score of 0.94.

Adaptive dwell-time selection improves user experience in VR.

Abstract

The use of ML models to predict a user's cognitive state from behavioral data has been studied for various applications which includes predicting the intent to perform selections in VR. We developed a novel technique that uses gaze-based intent models to adapt dwell-time thresholds to aid gaze-only selection. A dataset of users performing selection in arithmetic tasks was used to develop intent prediction models (F1 = 0.94). We developed GazeIntent to adapt selection dwell times based on intent model outputs and conducted an end-user study with returning and new users performing additional tasks with varied selection frequencies. Personalized models for returning users effectively accounted for prior experience and were preferred by 63% of users. Our work provides the field with methods to adapt dwell-based selection to users, account for experience over time, and consider tasks that vary by selection frequency