Tan Juquan, Chen Jingwen
College of P.E.Teaching, South China Agricultural University, Guangzhou, Guangdong, China.
College of Education for the Future, Beijing Normal University, Zhuhai, Guangdong, China.
PLoS One. 2025 Jan 30;20(1):e0318321. doi: 10.1371/journal.pone.0318321. eCollection 2025.
Personalized sports training plans are essential for addressing individual athlete needs, but traditional methods often need to integrate diverse data types, limiting adaptability and effectiveness. Existing machine learning (ML) and rule-based approaches cannot dynamically generate context-specific training programs, reducing their applicability in real-world scenarios. This study aims to develop a Generative Adversarial Network (GAN)- based framework to create context-specific training plans by integrating numeric attributes (e.g., age, heart rate) and motion features from video data. The research focuses on improving context-specific efficiency and real-time adaptability while addressing the limitations of traditional methods. The proposed GAN framework combines numeric and motion features using a generator-discriminator architecture to produce tailored training plans. The model is evaluated quantitatively through metrics like mean square error (MSE) and generation time and qualitatively through subjective ratings from athletes and coaches using a five-point Likert scale for context-specific, scientificity, applicability, and feasibility. Statistical significance is analyzed using ANOVA testing. The proposed GAN model outperforms traditional ML and rule-based methods, achieving a 22% reduction in MSE and a 45% improvement in generation time. Subjective evaluations reveal significant improvements in context-specific and applicability, with ratings averaging 4.8/5 compared to 3.9/5 for baseline models. The GAN framework effectively integrates multimodal data, demonstrating dynamic adaptability and high efficiency suitable for real-world applications. The proposed GAN-based framework advances the generation of personalized sports training plans by integrating numeric and motion data, achieving superior adaptability and efficiency. These results highlight the model's potential for practical deployment in athletic coaching systems, addressing critical gaps in existing methodologies and offering scalable solutions for individualized training.
个性化运动训练计划对于满足运动员个体需求至关重要，但传统方法往往难以整合多种数据类型，从而限制了适应性和有效性。现有的机器学习（ML）和基于规则的方法无法动态生成特定情境的训练计划，降低了它们在实际场景中的适用性。本研究旨在开发一个基于生成对抗网络（GAN）的框架，通过整合数值属性（如年龄、心率）和视频数据中的运动特征来创建特定情境的训练计划。该研究聚焦于提高特定情境下的效率和实时适应性，同时解决传统方法的局限性。所提出的GAN框架使用生成器-判别器架构结合数值和运动特征，以生成量身定制的训练计划。通过均方误差（MSE）和生成时间等指标对模型进行定量评估，并通过运动员和教练使用五点李克特量表对特定情境性、科学性、适用性和可行性进行主观评分来进行定性评估。使用方差分析测试分析统计显著性。所提出的GAN模型优于传统的ML和基于规则的方法，均方误差降低了22%，生成时间提高了45%。主观评估显示，在特定情境性和适用性方面有显著改善，评分平均为4.8/5，而基线模型为3.9/5。GAN框架有效地整合了多模态数据，展现出适用于实际应用的动态适应性和高效率。所提出的基于GAN的框架通过整合数值和运动数据，推动了个性化运动训练计划的生成，实现了卓越的适应性和效率。这些结果凸显了该模型在运动教练系统中实际部署的潜力，填补了现有方法中的关键空白，并为个性化训练提供了可扩展的解决方案。
