000165133 001__ 165133
000165133 005__ 20251212165958.0
000165133 0247_ $$2doi$$a10.3390/photonics12121167
000165133 0248_ $$2sideral$$a146511
000165133 037__ $$aART-2025-146511
000165133 041__ $$aeng
000165133 100__ $$aPovedano-Montero, Francisco Javier
000165133 245__ $$aIntegrating Eye-Tracking and Artificial Intelligence for Quantitative Assessment of Visuocognitive Performance in Sports and Education
000165133 260__ $$c2025
000165133 5060_ $$aAccess copy available to the general public$$fUnrestricted
000165133 5203_ $$aBackground: 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.
000165133 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000165133 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000165133 700__ $$aBernardez-Vilaboa, Ricardo
000165133 700__ $$0(orcid)0000-0002-7998-5476$$aTrillo, José Ramon$$uUniversidad de Zaragoza
000165133 700__ $$aGonzález-Jiménez, Rut
000165133 700__ $$aOtero-Currás, Carla
000165133 700__ $$aMartínez-Florentín, Gema
000165133 700__ $$aCedrún-Sánchez, Juan E.
000165133 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000165133 773__ $$g12, 12 (2025), 1167 [16 pp.]$$pPhotonics (Basel)$$tPhotonics$$x2304-6732
000165133 8564_ $$s1890549$$uhttps://zaguan.unizar.es/record/165133/files/texto_completo.pdf$$yVersión publicada
000165133 8564_ $$s2518107$$uhttps://zaguan.unizar.es/record/165133/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000165133 909CO $$ooai:zaguan.unizar.es:165133$$particulos$$pdriver
000165133 951__ $$a2025-12-12-14:43:21
000165133 980__ $$aARTICLE