使用可穿戴生理监视器数据进行运动运动水平预测
Aref Smiley1, Te-Yi Tsai1, Aileen Gabriel1
1Department of Biomedical Informatics, University of Utah, Salt Lake City, UT.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|January 15, 2024
概括
机器学习模型可以使用可穿戴传感器数据预测运动量. 在骑自行车时,k-最近邻近算法在识别高与低的劳动力水平方面取得了85.7%的准确性.
科学领域:
- 运动科学 运动科学 运动科学
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 可穿戴设备在运动期间收集生理数据.
- 准确的实时评估所感知的劳动力是具有挑战性的.
- 机器学习为自动化炼监控提供了潜力.
研究的目的:
- 开发和评估机器学习算法,用于预测运动炼水平.
- 用可穿戴传感器的生理参数来预测炼.
- 为了比较各种机器学习分类器的性能.
主要方法:
- 在骑行过程中收集实时心电图,氧和度,脉率和RPM.
- 从心电图数据中推导出心率变化特征.
- 根据RPE,标记2分钟的炼窗口为高或低功耗.
- 使用的最小冗余性 功能选择的最大相关性.
- 培训和测试了十个ML分类器,包括KNN和组合模型.
主要成果:
- k-最近邻居 (KNN) 模型获得了最高的准确度 (85.7%) 和F1得分 (83%).
- 一个整体模型表明曲线下的最高面积 (AUC) 为0.92.
- 选择的特征有效地预测了不同ML模型中的炼水平.
结论:
- 机器学习模型可以准确地预测实时感知到的运动量.
- 穿戴式传感器数据与ML相结合,为自动化运动跟踪提供了一种可行的方法.
- 开发的方法在健身监测和培训中具有潜在的应用.
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