Integrating Eye-Tracking and Artificial Intelligence for Quantitative Assessment of Visuocognitive Performance in Sports and Education

Abstract

Background: Eye-tracking technology enables the objective quantification of oculomotor behavior, providing key insights into visuocognitive performance. This study presents a comparative analysis of visual attention patterns between rhythmic gymnasts and school-aged students using an optical eye-tracking system combined with machine learning algorithms. Methods: Eye movement data were recorded during controlled visual tasks using the DIVE system (sampling rate: 120 Hz). Spatiotemporal metrics—including fixation duration, saccadic amplitude, and gaze entropy—were extracted and used as input features for supervised models: Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Decision Tree (CART), Random Forest, XGBoost, and a one-dimensional Convolutional Neural Network (1D-CNN). Data were divided according to a hold-out scheme (70/30) and evaluated using accuracy, F1-macro score, and Receiver Operating Characteristic (ROC) curves. Results: XGBoost achieved the best performance (accuracy = 94.6%; F1-macro = 0.945), followed by Random Forest (accuracy = 94.0%; F1-macro = 0.937). The neural network showed intermediate performance (accuracy = 89.3%; F1-macro = 0.888), whereas SVM and k-NN exhibited lower values. Gymnasts demonstrated more stable and goal-directed gaze patterns than students, reflecting greater efficiency in visuomotor control. Conclusions: Integrating eye-tracking with artificial intelligence provides a robust framework for the quantitative assessment of visuocognitive performance. Ensemble algorithms demonstrated high discriminative power, while neural networks require further optimization. This approach shows promising applications in sports science, cognitive diagnostics, and the development of adaptive human–machine interfaces.