通过结合生成对抗网络来生成特定环境的体育培训计划.
1College of P.E.Teaching, South China Agricultural University, Guangzhou, Guangdong, China.
PloS one
|January 30, 2025
概括
本研究介绍了个性化体育训练的生成对抗网络 (GAN) 框架. 该模型整合了各种运动员数据,以创建动态的,特定于环境的训练计划,提高效率和适应能力.
科学领域:
- 运动科学和生物力学
- 运动中的人工智能
- 数据科学用于性能优化的数据科学.
背景情况:
- 传统的个性化体育训练方法难以整合各种数据类型,限制了适应性.
- 现有的机器学习和基于规则的方法缺乏动态的特定环境的程序生成.
研究的目的:
- 开发基于生成对抗网络 (GAN) 的框架,用于创建特定环境的体育培训计划.
- 从视频数据中整合数值属性和运动特征,以增强训练个性化.
- 提高体育训练计划生成的效率和实时适应性.
主要方法:
- 一个使用生成器-歧视器架构的生成对抗网络 (GAN) 框架.
- 整合多式联络数据,包括数值属性 (如年龄,心率) 和基于视频的运动特征.
- 通过平均平方误差 (MSE) 和生成时间进行定量评估;通过运动员/教练的主观评分 (利克尔特尺度) 进行定性评估.
主要成果:
- 与传统方法相比,GAN模型实现了MSE的22%降低和生成时间的45%改善.
- 主观评估显示,在特定环境和适用性方面,平均评分为4.8/5,明显高于基线模型 (3.9/5).
- 证明了多式联运数据的有效集成,从而实现了动态适应性和高效率.
结论:
- 拟议的GAN框架在生成个性化的体育训练计划方面取得了重大进展.
- 该模型有效地整合了多式联运数据,在现实应用中实现了卓越的适应性和效率.
- 突出了在运动教练系统中实际部署的潜力,提供可扩展的,个性化的训练解决方案.
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