Adaptive Sports Optimization Framework Based on I-JEPA: Standing Long Jump Performance Prediction and PSO Trajectory Optimization

Abstract Standing long jump performance optimization has long been a focal point in sports biomechanics research. This study introduces an adaptive framework integrating I-JEPA (Image-based Joint Embedding Predictive Architecture) world models with Particle Swarm Optimization (PSO) for performance prediction and feature space exploration. Through self-supervised masked prediction mechanisms, the proposed framework learns motion dynamics representations employing an architecture comprising multi-scale temporal stem, Transformer encoder, and attention statistical pooling modules. Further, PSO is introduced as a hypothesis generation tool for heuristic exploration in the trained model’s feature space. Experimental validation on a collected sports biomechanics dataset (11 athletes, 35 jump trials in total; 10 athletes with 34 trials used for training and cross-validation, 1 athlete reserved for external validation) reveals that, under an athlete-level grouped cross-validation protocol, the model achieves Mean Absolute Error (MAE) of $$0.157 \pm 0.053$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0.157</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.053</mml:mn> </mml:mrow> </mml:math> m, outperforming Transformer without JEPA pretraining (0.199 m), TCN (0.207 m), and Graph Skeleton (0.221 m) baselines. Repeated resampling experiments further validate model robustness. Systematic ablation experiments verify module effectiveness: removing attention pooling increases MAE by $$19.9\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>19.9</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> , and removing reconstruction loss increases MAE by $$21.6\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>21.6</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> . PSO explores a sample’s predicted score from 1.542 m to 1.583 m in the feature space; this result represents a model-space hypothesis rather than a guaranteed real-world performance improvement. This work, as a proof-of-concept for future personalized training systems, combines world models with heuristic exploration, providing an exploratory analysis framework for athlete training.