使用无监督机器学习来描述奥林匹克训练年中精英独木舟-皮划艇运动员的恢复模式
Audric Foucaud1, Fabienne Durand1, Henri Meric1
1ESPACE DEV, Universite de Perpignan Via Domitia, Perpignan, France.
Frontiers in sports and active living
|December 5, 2025
概括
精英运动员的精英运动员.
科学领域:
- 运动科学 运动科学 运动科学
- 生理学 生理学 生理学
- 机器学习 机器学习
背景情况:
- 可穿戴技术产生了大量的运动员数据.
- 将这些数据转化为可操作的恢复洞察力是一项挑战.
- 无监督机器学习可以描述恢复状态和生理现象型.
研究的目的:
- 应用机器学习管道来分析精英运动员的可穿戴数据.
- 描述夜间恢复状态和季节性生理现象.
- 确定恢复和表现的关键生理标志物.
主要方法:
- 收集的内部负载数据 (心率,HRV,呼吸率,睡眠结构) 和外部负载数据.
- 使用K-means集群在标准化和验证的数据上.
- 分析了17名奥林匹克级别独木舟和皮划艇的5855个夜晚的数据.
主要成果:
- 根据睡眠结构和呼吸速率确定了四种不同的夜间恢复类型.
- 发现睡眠量和碎片化解释了大部分恢复差异 (79%).
- 发现精英运动员具有优异的心脏自主标记可能会补偿低于最佳的睡眠效率.
结论:
- 睡眠架构是夜间恢复的主要驱动因素.
- 呼吸率升高表明残留的代谢应变.
- 个性化,ML驱动的监控对于高性能运动至关重要,阴道声可能比完美的睡眠更为关键.
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