数据增强机器学习为个性化碳水化合物-蛋白质补充剂推耐力.
Wang Xiangyu1,2, Wu Hao3
1Department of Physical Education, Capital Normal University, Beijing, 100048, China.
Scientific reports
|November 17, 2025
概括
个性化碳水化合物蛋白质补充剂可以提高耐力表现. 一个新的机器学习框架使用瓦斯斯坦生成对抗网络与梯度惩罚 (WGAN-GP) 预测个人反应,优化运动策略.
科学领域:
- 运动科学 运动科学 运动科学
- 机器学习 机器学习
- 营养生物化学 营养生物化学
背景情况:
- 众所周知,碳水化合物-蛋白质补充剂可以提高耐力表现.
- 个人对补充剂的反应因独特的个人特征而有很大差异.
研究的目的:
- 开发一个预测机器学习框架,用于个性化碳水化合物-蛋白质补充策略.
- 为了解决数据稀缺问题,使用瓦斯斯坦带有梯度惩罚的生成对抗网络 (Wasserstein Generative Adversarial Networks with Gradient Penalty, WGAN-GP) 预测运动表现.
主要方法:
- 利用了231个划船试验,其中有46个输入特征 (基线特征,饮食摄入量).
- 采用混合特征选择方法和WGAN-GP进行数据增强.
- 训练回归模型 (XGBoost,SVR,MLP) 来预测划船性能.
主要成果:
- 从46个初始输入中确定了21个关键绩效指标.
- 增加了WGAN-GP数据的XGBoost模型实现了高预测精度 (R2 = 0.53).
- 关键预测因素包括体重,爆炸力和营养投入.
结论:
- 一种数据增强的机器学习方法有效地模拟了个体对补充剂的反应.
- 开发的框架为个性化营养策略提供了数据驱动的途径,以优化运动表现.
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