Scholarlink

<p>Traditional training load management methods in competitive sports rely heavily on subjective assessments and standardized protocols, often failing to account for individual physiological variations and dynamic adaptation responses. This research proposes a deep reinforcement learning (DRL) framework for personalized training load optimization that integrates real-time physiological monitoring, individual athlete characteristics, and adaptive decision-making algorithms. The proposed system employs a hybrid neural network architecture combining multilayer perceptrons and convolutional neural networks to process heterogeneous physiological data and generate training prescriptions. Empirical validation across multiple sports disciplines including track and field, swimming, and ball sports, with ethical approval (IRB: DU-IRB-2023-001), shows performance improvements averaging 12.3% (95% CI: 10.1–14.5%, <i>p</i> &lt; 0.001) compared to traditional periodization-based methods as measured by sport-specific performance tests using independent samples t-tests, with injury rate reductions of 43% and training efficiency enhancements ranging from 1.15 to 1.42 times conventional approaches. The system maintains operational reliability with 99.7% availability and sub-2-second response times in tested environments. Cost-benefit analysis reveals favorable return on investment for professional teams achieving payback periods of 8–12 months. The research establishes theoretical foundations through mathematical modeling of personalized training load relationships and fatigue-recovery dynamics. Important limitations include cold-start periods requiring 2–4 weeks of data accumulation before optimal performance, high implementation costs ($50,000-200,000), technical infrastructure requirements, and validation primarily conducted in well-resourced competitive environments with predominantly male Asian athletes (72% male, mean age 23.1 ± 3.2 years). This adaptive training load control system suggests potential applications for evidence-based personalized athletic training optimization in competitive sports settings with adequate resources and technical support.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Deep reinforcement learning-driven personalized training load control algorithm for competitive sports performance optimization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月1日

查看全文
  • Xiaoyu Xia,
  • Qiaonan Chen,
  • Zizhuo Wang

摘要

Traditional training load management methods in competitive sports rely heavily on subjective assessments and standardized protocols, often failing to account for individual physiological variations and dynamic adaptation responses. This research proposes a deep reinforcement learning (DRL) framework for personalized training load optimization that integrates real-time physiological monitoring, individual athlete characteristics, and adaptive decision-making algorithms. The proposed system employs a hybrid neural network architecture combining multilayer perceptrons and convolutional neural networks to process heterogeneous physiological data and generate training prescriptions. Empirical validation across multiple sports disciplines including track and field, swimming, and ball sports, with ethical approval (IRB: DU-IRB-2023-001), shows performance improvements averaging 12.3% (95% CI: 10.1–14.5%, p < 0.001) compared to traditional periodization-based methods as measured by sport-specific performance tests using independent samples t-tests, with injury rate reductions of 43% and training efficiency enhancements ranging from 1.15 to 1.42 times conventional approaches. The system maintains operational reliability with 99.7% availability and sub-2-second response times in tested environments. Cost-benefit analysis reveals favorable return on investment for professional teams achieving payback periods of 8–12 months. The research establishes theoretical foundations through mathematical modeling of personalized training load relationships and fatigue-recovery dynamics. Important limitations include cold-start periods requiring 2–4 weeks of data accumulation before optimal performance, high implementation costs ($50,000-200,000), technical infrastructure requirements, and validation primarily conducted in well-resourced competitive environments with predominantly male Asian athletes (72% male, mean age 23.1 ± 3.2 years). This adaptive training load control system suggests potential applications for evidence-based personalized athletic training optimization in competitive sports settings with adequate resources and technical support.