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This paper presents a novel adaptive Virtual Reality (VR) system that aims to mitigate cybersickness, specifically visually induced motion sickness (VIMS) 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. By 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.

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Dynamic Cybersickness Mitigation via Adaptive FFR and FoV Adjustments

  • 会议论文

  • 发表日期：

    2025年

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  • Ananth N. Ramaseri-Chandra,
  • Hassan Reza

摘要

This paper presents a novel adaptive Virtual Reality (VR) system that aims to mitigate cybersickness, specifically visually induced motion sickness (VIMS) 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. By 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.