机器学习模型在预测加速计衍生的步行速度方面的表现
Aleksej Logacjov1, Tonje Pedersen Ludvigsen2, Kerstin Bach1
1Department of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Heliyon
|February 10, 2025
概括
这项研究开发了一种机器学习分类器,可以使用加速度计准确预测步行速度. 该模型有效地区分慢步,中步和快步,为大规模研究提供了一个新的工具.
科学领域:
- 生物力学 生物力学
- 机器学习 机器学习
- 可穿戴技术可穿戴技术
背景情况:
- 在大型研究中,很难准确地长期测量行走速度.
- 步行速度是健康和流动性的关键指标.
- 目前用于测量步行速度的方法通常不适合大规模的长期研究.
研究的目的:
- 开发和评估用于预测步行速度的机器学习分类器.
- 通过使用双和单个加速度计设置来评估分类器的性能.
- 为了确定分类缓慢,中等和快速步行速度的准确性.
主要方法:
- 训练了一个极端梯度提升 (XGBoost) 机器学习分类器.
- 使用了24名成年人的数据,这些成年人使用了大腿和腰部的三轴加速度计.
- 通过使用1,3和5秒窗口的leave-one-out交叉验证验证了分类器.
主要成果:
- 机器学习分类器在预测步行速度 (缓慢,中等,快速) 和慢跑方面取得了很高的准确性.
- 在双和单个加速度计设置之间以及在不同的窗口长度之间,性能是可比的.
- 最高的精度达到91%,采用双加速度计设置和5秒窗口.
结论:
- 机器学习分类器可以使用加速度计数据准确预测步行速度.
- 双和单个加速度计设置都对这种预测有效.
- 这种方法为大规模研究中评估步行速度提供了一种可行的方法.
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