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Intelligent strength training for football players using resnext optimized by upgraded chimp optimization algorithm.

Wu Zhang1, Yinglong Zhang2

  • 1Physical Education College of Yan'an University, 716000, Yan'an, China.

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|November 25, 2025
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Summary

This study introduces an intelligent strength training system for football players, enhancing athletic performance and injury prevention. The novel approach uses an optimized ResNeXt model for accurate action recognition, leading to personalized exercise recommendations.

Keywords:
Deep learningFootball performanceHuman action recognitionIntelligent trainingResNeXtUpgraded chimp optimization algorithm

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Area of Science:

  • Sports Science
  • Artificial Intelligence
  • Biomechanical Analysis

Background:

  • Intelligent training systems are crucial for athletic performance and injury prevention in sports science.
  • Current action-recognition systems struggle with biomechanically similar football actions and inefficient hyperparameter optimization.
  • Traditional methods like manual tuning or basic metaheuristics (e.g., Particle Swarm Optimization, Genetic Algorithms) exhibit slow convergence and poor exploration-exploitation balance in high-dimensional spaces.

Purpose of the Study:

  • To develop an advanced intelligent strength training model for football players.
  • To improve the accuracy of distinguishing biomechanically similar football actions.
  • To enhance hyperparameter optimization for deep learning models in sports science applications.

Main Methods:

  • Implementation of an improved motion discrimination ResNeXt convolutional neural network with cardinality-based feature learning.
  • Integration of an Upgraded Chimp Optimization Algorithm (UCOA) for hyperparameter optimization, featuring chaotic map initialization and an elimination step to avoid local optima.
  • Testing the system on the Berkeley MHAD dataset for automatic recognition and visualization of football-specific motions (kicking, jumping, squats) linked to muscle groups.

Main Results:

  • The UCOA-optimized ResNeXt model achieved a classification accuracy of 93.7% and an F1-score of 92.
  • The proposed system demonstrated superior accuracy compared to traditional deep learning models and hybrid optimization baselines.
  • The system successfully recognized football actions and mapped them to specific muscle groups for targeted exercise recommendations.

Conclusions:

  • The novel intelligent strength training system, combining UCOA and ResNeXt, significantly improves football action recognition accuracy.
  • This approach offers a more efficient and effective method for hyperparameter optimization in sports science AI.
  • The system provides a foundation for personalized strength training recommendations, aiding athletic performance enhancement and injury prevention.