Development of a Classifier to Determine Factors Causing Cybersickness in Virtual Reality Environments

The goal of this contribution is to develop a classifier able to determine if cybersickness (CS) has occurred after immersion in a virtual reality (VR) scenario, based on a combination of biosignals and game parameters. We collected electrocardiographic, electrooculographic, respiratory, and skin conductivity data from a total of 66 participants. In addition, we also captured relevant game parameters such as avatar linear and angular speed as well as acceleration, head movements, and on-screen collisions. The data were collected while the participants were in a 10-minute VR experience, which was developed in Unity. The experience forced rotation and lateral movements upon the participants to provoke CS. A baseline was captured during a first simple scenario. The data were then split in per-level, per-60-second, and per-30-second windows. Furthermore, participants filled a pre- and postimmersion simulator sickness questionnaire. Simulator sickness scores were then used as a reference for binary (CS vs. no CS) and ternary (no CS-mild CS-severe CS) classification patterns. Several classification methods (support vector machines, K-nearest neighbors, and neural networks) were tested. A maximum classification accuracy of 82% was achieved for binary classification and 56% for ternary classification. Given the sample size and the variety of movement patterns presented in the demonstration, we conclude that a combination of biosignals and game parameters suffice to determine the occurrence of CS. However, substantial further research is required to improve binary classification accuracy to adequate values for real-life scenarios and to determine better approaches to classify its severity.