Dynamic Cybersickness Mitigation via Adaptive FFR and FoV adjustments

TL;DR

This paper introduces an adaptive VR system that reduces cybersickness by predicting discomfort levels with machine learning and dynamically adjusting rendering and FOV for improved user comfort and system performance.

Contribution

It presents a novel real-time cybersickness mitigation approach using ML-based predictions to adapt FFR and FOV in VR environments.

Findings

ML model accurately predicts cybersickness levels

Adaptive adjustments improve user comfort

System maintains performance while reducing discomfort

Abstract

This paper presents a novel adaptive Virtual Reality (VR) system that aims to mitigate cybersickness in immersive environments through dynamic, real-time adjustments. The system predicts cybersickness levels in real-time using a machine learning (ML) model trained on head tracking and kinematic data. The adaptive system adjusts foveated rendering (FFR) strength and field of view (FOV) to enhance user comfort. With a goal to balance usability with system performance, we believe our approach will optimize both user experience and performance. Adapting responsively to user needs, our work explores the potential of a machine learning-based feedback loop for user experience management, contributing to a user-centric VR system design.