A Real-Time Error Prevention System for Gaze-Based Interaction in Virtual Reality Based on Anomaly Detection

TL;DR

This paper introduces a real-time error prevention system using anomaly detection to improve gaze-based interaction accuracy in VR, significantly reducing errors and enhancing user experience.

Contribution

The study presents a novel anomaly detection system (TCNAE) for gaze interaction that effectively prevents errors in real-time VR environments.

Findings

Error reduction of up to 95% in gaze-based selections

Positive user feedback on system effectiveness

Performance varies with gesture type and individual differences

Abstract

Gaze-based interaction enables intuitive, hands-free control in immersive environments, but remains susceptible to unintended inputs. We present a real-time error prevention system (EPS) that uses a temporal convolutional network autoencoder (TCNAE) to detect anomalies in gaze dynamics during selection tasks. In a visual search task in VR, 41 participants used three gaze-based methods - dwell time, gaze and head direction alignment, and nod - with and without EPS. The system reduced erroneous selections by up to 95% for dwell time and gaze and head, and was positively received by most users. Performance varied for nodding and between individuals, suggesting the need for adaptive systems. Objective metrics and subjective evaluations show that anomaly-based error prevention can improve gaze interfaces without disrupting interaction. These findings demonstrate the potential of anomaly-based error prevention for gaze interfaces and suggest applications in VR, AR, and assistive technologies.