Visual Communication Design: Research on Visual Dizziness Factors in Virtual Reality Games

Visual dizziness, characterized by sensations of head spinning and blurred vision, arises from various factors including vestibular disorders, migraines, and ocular issues. The degree of dizziness experienced can influence treatment options, which may encompass medications or vestibular rehabilitation therapy. This study focuses on formulating a reasoning model to identify factors contributing to visual dizziness, particularly in virtual reality (VR) games. A Kernel‐refined Support Vector Machine (KSVM) was employed to enhance accuracy in classifying these factors, improving upon traditional methodologies by utilizing complex kernel functions for better data separation in high‐dimensional spaces. The research included a sample of 212 participants who completed a specialized cybersickness profile over multiple sessions, taking into account variables such as age, gender, prior experiences, and reactions to virtual scenarios. The advanced KSVM model was implemented using Python, and its efficacy was assessed through various metrics. The results indicated impressive classification performance with a precision of 90%, an F1‐score of 92%, accuracy of 95%, and recall of 91%. These findings significantly outperformed conventional approaches, evidencing notable advancements in both accuracy and robustness in predicting visual dizziness factors within VR gaming environments.