Enhancing Visual Perception in Sports Environments: A Virtual Reality and Machine Learning Approach

The sports environment plays a crucial role in shaping the physical and mental well-being of individuals engaged in sports activities. Understanding how environmental factors and emotional experiences influence sports perceptions is essential for advancing public health research and guiding optimal design interventions. However, existing studies in this field often rely on subjective evaluations, lack objective validation, and fail to provide practical insights for design applications. To address these gaps, this study adopts a data-driven approach. Quantitative data were collected to explore the visual environment of badminton courts using eye-tracking technology and a semantic differential questionnaire. The relationships between environmental factors—such as illuminance (IL), height (Ht), roof saturation (RSa), roof slope (RS), backwall saturation (BSa), and natural materials proportion on the backwall (BN)—and sports perception (W) were analyzed. Furthermore, this study identifies the best-performing machine learning model for predicting sports perception, which is subsequently integrated with a genetic algorithm to optimize environmental design thresholds. These findings provide actionable insights for creating sports environments that enhance user experience and support public health objectives.

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