# Towards Cybersickness Severity Classification from VR Gameplay Videos Using Transfer Learning and Temporal Modeling

arXiv: https://arxiv.org/abs/2510.10422 · HTML: https://mlsanity.com/paper/2510.10422/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 12 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [VR.net: A Real-world Dataset for Virtual Reality Motion Sickness Research](https://mlsanity.com/paper/2306.03381/) (2023)
- [Measurement of exceptional motion in VR video contents for VR sickness assessment using deep convolutional autoencoder](https://mlsanity.com/paper/1804.03939/) (2018)
- [Mazed and Confused: A Dataset of Cybersickness, Working Memory, Mental Load, Physical Load, and Attention During a Real Walking Task in VR](https://mlsanity.com/paper/2409.06898/) (2024)
- [Reducing cybersickness in 360-degree virtual reality](https://mlsanity.com/paper/2103.03898/) (2021)

## Abstract (arXiv)

With the rapid advancement of virtual reality (VR) technology, its adoption across domains such as healthcare, education, and entertainment has grown significantly. However, the persistent issue of cybersickness, marked by symptoms resembling motion sickness, continues to hinder widespread acceptance of VR. While recent research has explored multimodal deep learning approaches leveraging data from integrated VR sensors like eye and head tracking, there remains limited investigation into the use of video-based features for predicting cybersickness. In this study, we address this gap by utilizing transfer learning to extract high-level visual features from VR gameplay videos using the InceptionV3 model pretrained on the ImageNet dataset. These features are then passed to a Long Short-Term Memory (LSTM) network to capture the temporal dynamics of the VR experience and predict cybersickness severity over time. Our approach effectively leverages the time-series nature of video data, achieving a 68.4% classification accuracy for cybersickness severity. This surpasses the performance of existing models trained solely on video data, providing a practical tool for VR developers to evaluate and mitigate cybersickness in virtual environments. Furthermore, this work lays the foundation for future research on video-based temporal modeling for enhancing user comfort in VR applications.