Analysis of ML Models for the Recognition of 3D Objects Based on Biometric Eye-Tracking Data in the Context of UX and User-Centered Design

Recent advances in the analysis of machine learning (ML) models for accurate prediction have led to new approaches for classifying and clustering variables related to user experience (UX). In this study, however, the focus is not only on modeling variables, but also on capturing the dynamics of visual attention in relation to object properties. We evaluate models such as KNN, SVM, and K-means, which emerge as the most relevant for classifying visual attention patterns. When combined with the principles of user-centered design, this approach enables the construction of user profiles based on their interaction capabilities with the interface. Furthermore, it examines how 3D object properties—such as shape, color, shading, direction of motion, and motion acceleration—influence the information conveyed to users, thus shaping their UX directly. This method aims to enhance UX design and enable more precise representation of target users. In 11 exploration tests, the three ML models analyzed obtained an accuracy of 44, 72, and 65%.