Postural profile of Peruvian youth soccer players using artificial intelligence-assisted assessment
DOI:
https://doi.org/10.47197/retos.v83.119328Keywords:
Artificial intelligence, biomechanics, photogrammetry, postural assessment, youth footballAbstract
Introduction. Postural assessment in professional football academies provides screening information for monitoring motor development during sensitive growth stages.
Objective. To describe and compare, in an exploratory manner, the postural profile — measured through six articular angles computed by an artificial-intelligence system (APECS) — in 120 Peruvian youth footballers from U-13, U-15, U-17 and Reserve categories.
Methods. A quantitative, non-experimental, cross-sectional, descriptive-comparative design was used. Digital AI-assisted photogrammetry (APECS) was applied in the frontal plane. Statistical analysis included Shapiro–Wilk, Levene, Kruskal–Wallis, Dunn post-hoc with Bonferroni correction, Spearman's rho and effect sizes (ε² and η²). An exploratory multivariate layer comprised PCA and four clustering techniques (K-Means/Ward, Fuzzy C-Means, GMM and archetypal analysis).
Results. Significant between-category differences emerged in five of the six variables, with large effects for foot angle (H=23.15; p<0.001; ε²=0.17) and pelvic inclination (H=17.39; p<0.001; ε²=0.12). U-17 players showed the best-aligned frontal-plane profile. The multivariate analysis revealed a weak latent structure (KMO=0.496) with fuzzy boundaries between postural profiles.
Conclusions. AI-assisted postural assessment is a useful and scalable exploratory screening tool for youth football academies; its prognostic value requires further validation against gold-standard measurements and longitudinal injury data.
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Copyright (c) 2026 Darvin Manuel Ramírez Guerra, Yusleidy Marlie Gordo Gómez, Maikel Yelandi Leyva Vázquez, Joel Blanco Pérez, Giceya de la Caridad Maqueira Caraballo
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