Asociación entre ángulos articulares y altura de salto en deportistas competitivos mediante un sistema de análisis de movimiento basado en inteligencia artificial

Abstract

Introduction

Jump height is a key indicator of athletic performance across multiple sports disciplines. Its accurate measurement typically requires costly laboratory equipment.

Objective

To investigate, using an artificial intelligence-based movement analysis system —Aictive—, the associations between joint angles and countermovement jump height in competitive athletes, and to evaluate the applicability of the system under real field conditions.

Methods

A cross-sectional, exploratory, analytical observational study was conducted in 29 competitive athletes from four sports disciplines. All participants performed the Venegas Test (30 countermovement jumps). Hip, knee, and ankle joint angles were recorded by Aictive during the propulsion and take-off phases; jump height was measured using the OPTO JUMP system (Microgate, Bolzano, Italy). Associations were analysed using a Linear Mixed-Effects Model adjusted for height, body weight, sport discipline, and jump number.

Results

Aictive successfully tracked whole-body kinematics in all participants. At take-off, hip flexion (β = −0.064; IC 95% [−0.081, −0.047]; p < 0.001) and ankle angle (β = −0.061; IC 95% [−0.088, −0.033]; p < 0.001) showed significant negative associations with jump height. During propulsion, trunk inclination (β = 0,438; IC 95% [0.359, 0.518]; p < 0.001) and knee flexion (β = 0.070; IC 95% [0.023, 0.117]; p = 0.004) were positively associated with jump height.

Conclusion

Statistically significant associations between lower limb joint angles and jump height were identified. Aictive proved applicable for collecting kinematic data of the jump in real training environments, with relevant implications for evidence-based performance optimization and training program design.