赢得你的比赛目标:一个通用的方法来预测运行性能
1Electrical and Computer Engineering, Portland State University, Portland, United States.
Sports medicine international open
|October 23, 2024
概括
一种新的长期短期记忆 (LSTM) 网络方法可以准确地预测各种比赛距离和跑者类型的跑步表现. 这种数据驱动的方法为训练和比赛准备提供了宝贵的见解.
科学领域:
- 运动科学 运动科学 运动科学
- 数据科学数据科学数据科学
- 计算生理学计算生理学
背景情况:
- 准确预测运行性能对于训练优化和比赛策略至关重要.
- 现有的预测模型可能不涵盖跑步条件和跑者能力的全部范围.
研究的目的:
- 开发和验证一种新的,数据驱动的方法来预测运行性能.
- 用回归和时间序列回归 (TSR) 方法评估长期短期记忆 (LSTM) 网络的预测准确性.
- 将LSTM模型的性能与已建立的运行预测公式进行比较.
主要方法:
- 分析了15686个跑步日志,来自15名跑步者,跨越各种比赛距离和高度.
- 使用LSTM网络实现和比较回归和TSR方法.
- 使用60场比赛的数据集,对LSTM模型与Riegel和UltraSignup公式进行评估.
主要成果:
- 使用LSTM的回归方法获得了89.13%的准确性,超过了TSR方法 (85.21%).
- 与Riegel公式 (80%) 和UltraSignup公式 (87.5%) 相比,LSTM模型显示出更高的预测准确性 (90.4%).
- 测试数据集包括每个跑者日志中的最后15次跑步,以进行可靠的评估.
结论:
- 一种基于LSTM的新型回归方法提供了高度准确的运行性能预测.
- 这种方法显示了将其集成到正在运行的应用程序和可穿戴设备中的潜力,以增强用户洞察力.
- 这些发现支持使用先进的机器学习来进行个性化的运动性能预测.
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