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

TL;DR

This paper introduces a rule-based 3QS-adaptation framework for social VR learning environments that effectively reduces cybersickness caused by performance and security anomalies, ensuring better user experience and application robustness.

Contribution

It presents a novel rule-based adaptation framework that performs risk and cost analysis to control cybersickness during VRLE sessions, enhancing stability and user comfort.

Findings

Framework reduces cybersickness levels effectively

Maintains application functionality during anomalies

Demonstrates robustness in social VRLE environments

Abstract

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.