通过纵向数据推进大众体育的训练效率预测:基于健身疲劳模型的数学模型方法
Wenxing Wang1, Yuanhui Zhao1, Xiao Hou1
1School of Sport Science, Beijing Sport University, Beijing, China.
PloS one
|December 3, 2025
概括
使用个人数据和心率变化 (HRV) 和心率恢复 (HRR) 指标优化健身疲劳模型 (FFM) 改善了群体运动中的训练负载评估. 这种增强的FFM可以更准确地预测培训效率.
科学领域:
- 运动科学 运动科学 运动科学
- 运动生理学 运动生理学
- 生物技术是生物技术.
背景情况:
- 准确的训练负载评估在群众体育中至关重要.
- 现有的适应性疲劳模型 (FFM) 需要数学优化和实际输出指标以更好地应用.
- 个性化预测培训效率仍然是一个挑战.
研究的目的:
- 在FFM中优化"适应"和"疲劳"之间的数学关系.
- 确定可通用的模型输出指标,用于预测培训效率.
- 评估优化的FFM在大众体育环境中的表现.
主要方法:
- 提出了新的数学假设,以解释非线性和时间变化的训练有效性.
- 使用个人纵向数据 (外部和内部负荷) 优化模型参数.
- 收集的数据包括每名参与者28-42天的速度,功率,心率变化 (HRV) 和心率恢复 (HRR).
主要成果:
- 与原来的FFM相比,优化的模型显示了更好的性能 (R2: 0.61-0.95,RMSE: 0.07-0.37).
- 心率变化 (HRV) 和心率恢复 (HRR) 指标被证明可以用于预测训练有效性.
- 最佳指标选择因个人而异,有些人遇到更大的错误与不合适的指标.
结论:
- 优化FFM与个人数据和HRV/HRR指标增强训练负载评估和预测训练有效性在群众体育.
- 可穿戴设备和机器学习可以利用HRV和HRR进行实际应用.
- 建议在各种体育和人群中进行进一步的验证.
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