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Updated: Jan 15, 2026

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在羽毛球运动员的有氧训练期间的能量消耗估计
Xinke Yan1, Jingmin Yang2, Jin Dai1
1Sports Coaching College, Beijing Sport University, Beijing 100084, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
对于运动员来说,能源消耗 (EE) 建模需要基于竞争水平的不同策略. 精英球员需要更少的传感器和更少的数据,而爱好者受益于更多的传感器和更大的样本大小,以准确监测训练负载.
科学领域:
- 运动科学 运动科学 运动科学
- 生物机械工程 生物机械工程
- 数据科学数据科学数据科学
背景情况:
- 精确的能源消耗 (EE) 监测对于运动员个性化训练负载管理至关重要.
- 现有的EE预测模型往往缺乏有关运动员竞争水平和传感器配置的具体性.
- 评估使用Triple-E等框架的模型通用性和效率对于实际应用至关重要.
研究的目的:
- 调查竞争水平如何影响能源支出 (EE) 的模型准确性和通用性.
- 为了确定可靠的EE预测模型的最佳传感器数量和样本大小.
- 为羽毛球运动员的个性化训练负载监测提供基于证据的建议.
主要方法:
- 收集了50名羽毛球运动员 (25名精英,25名爱好者) 在四项有氧活动中使用加速度计和心率计收集的数据.
- 开发并比较了多种机器学习模型 (线性回归,贝叶斯山脊回归,随机森林,梯度提升) 用于EE预测.
- 使用R2,MAPE,RMSE和Triple-E框架 (有效性,效率,扩展) 评估模型性能.
主要成果:
- 精英运动员通过最小的传感器 (2-3核心加速度计+HR) 实现了高预测准确性 (R2),显示出稳定的运动模式.
- 爱好者需要多位置传感器来考虑更大的运动变化,最佳配置涉及2-3个加速度计和HR数据.
- 增加样本大小显著提高了模型稳定性和通用性,运行任务的R2从0.49 (N=20) 增加到0.95 (N=40).
结论:
- 竞争水平是EE建模的一个重要因素;精英运动员受益于"低传感器,小样本"的方法,而爱好者需要"多传感器,大样本"的策略.
- 战略性传感器最小化和足够的样本大小最大限度地提高了预测性能,降低了计算成本,并简化了部署.
- 这些发现支持开发个性化训练负载监控系统,以适应运动员个体特征.
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