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

arXiv: https://arxiv.org/abs/2601.15146 · HTML: https://mlsanity.com/paper/2601.15146/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2026.

## Built on by (most cited first, 8 of 8)

- [GeomPrompt: Geometric Prompt Learning for RGB-D Semantic Segmentation Under Missing and Degraded Depth](https://mlsanity.com/paper/2604.11585/) (2026)
- [Learning Image-Adaptive Scale Fields for Metric Depth Recovery](https://mlsanity.com/paper/2605.07418/) (2026)
- [Markerless Head Tracking for Accurate and Accessible Neuronavigation](https://mlsanity.com/paper/2602.07052/) (2026)
- [FAAR: Efficient Frequency-Aware Multi-Task Fine-Tuning via Automatic Rank Selection](https://mlsanity.com/paper/2603.20403/) (2026)
- [Depth2Pose: A Pose-Based Benchmark for Monocular Depth Estimation without Ground-Truth Depth](https://mlsanity.com/paper/2605.19797/) (2026)
- [Depth-Semantic Alignment and Affinity-Guided Fusion for Structured Radar Point Cloud Generation](https://mlsanity.com/paper/2606.26743/) (2026)
- [Reliability-Aware 3D Geometric Injection for Universal Person Re-identification](https://mlsanity.com/paper/2607.18863/) (2026)
- [Monocular Depth Estimation from a Single Image: Progress and Opportunities](https://mlsanity.com/paper/2609.01172/) (2026)

## Abstract (arXiv)

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.