# Rule-based Adaptations to Control Cybersickness in Social Virtual Reality Learning Environments

arXiv: https://arxiv.org/abs/2108.12315 · HTML: https://mlsanity.com/paper/2108.12315/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2021.

## Built on by (most cited first, 1 of 1)

- [TruVR: Trustworthy Cybersickness Detection using Explainable Machine Learning](https://mlsanity.com/paper/2209.05257/) (2022)

## Abstract (arXiv)

Social virtual reality learning environments (VRLEs) provide immersive experience to users with increased accessibility to remote learning. Lack of maintaining high-performance and secured data delivery in critical VRLE application domains (e.g., military training, manufacturing) can disrupt application functionality and induce cybersickness. In this paper, we present a novel rule-based 3QS-adaptation framework that performs risk and cost aware trade-off analysis to control cybersickness due to performance/security anomaly events during a VRLE session. Our framework implementation in a social VRLE viz., vSocial monitors performance/security anomaly events in network/session data. In the event of an anomaly, the framework features rule-based adaptations that are triggered by using various decision metrics. Based on our experimental results, we demonstrate the effectiveness of our rule-based 3QS-adaptation framework in reducing cybersickness levels, while maintaining application functionality. Using our key findings, we enlist suitable practices for addressing performance and security issues towards a more high-performing and robust social VRLE.