VisionaryVR: An Optical Simulation Tool for Evaluating and Optimizing Vision Correction Solutions in Virtual Reality.

In the rapidly advancing field of vision science, traditional research approaches struggle to accurately simulate and evaluate vision correction methods, leading to time-consuming evaluations with limited scope and flexibility. To overcome these challenges, we introduce 'VisionaryVR', a virtual reality (VR) simulation framework designed to enhance optical simulation fidelity and broaden experimental capabilities. VisionaryVR leverages a versatile VR environment to support dynamic vision tasks and integrates comprehensive eye-tracking functionality. Its experiment manager's scene-loading feature fosters a scalable and flexible research platform. Preliminary validation through an empirical study has demonstrated VisionaryVR's effectiveness in replicating a wide range of visual impairments and providing a robust platform for evaluating vision correction solutions. Key findings indicate a significant improvement in evaluating vision correction methods and user experience, underscoring VisionaryVR's potential to transform vision science research by bridging the gap between theoretical concepts and their practical applications. This validation underscores VisionaryVR's contribution to overcoming traditional methodological limitations and establishing a foundational framework for research innovation in vision science.

Soccer goalkeeper expertise identification based on eye movements.

By focusing on high experimental control and realistic presentation, the latest research in expertise assessment of soccer players demonstrates the importance of perceptual skills, especially in decision making. Our work captured omnidirectional in-field scenes displayed through virtual reality glasses to 12 expert players (picked by DFB), 10 regional league intermediate players, and13 novice soccer goalkeepers in order to assess the perceptual skills of athletes in an optimized manner. All scenes were shown from the perspective of the same natural goalkeeper and ended after the return pass to that goalkeeper. Based on the gaze behavior of each player, we classified their expertise with common machine learning techniques. Our results show that eye movements contain highly informative features and thus enable a classification of goalkeepers between three stages of expertise, namely elite youth player, regional league player, and novice, at a high accuracy of 78.2%. This research underscores the importance of eye tracking and machine learning in perceptual expertise research and paves the way for perceptual-cognitive diagnosis as well as future training systems.