开发一个基于深度学习的多穿戴站点的体力活动强度分类算法,使用原始加速数据进行分类
Johan Y Y Ng1, Joni H Zhang2, Stanley S Hui1
1Department of Sports Science and Physical Education, The Chinese University of Hong Kong, Hong Kong, Hong Kong.
PloS one
|March 7, 2024
概括
新的深度神经网络模型准确地分类了加速度计的位置和活动强度,性能优于现有的方法. 这种进步允许参与者获得更多的自主权,并可能提供更准确的体育活动数据.
科学领域:
- 生物医学工程 生物医学工程
- 可穿戴技术是可穿戴的技术.
- 机器学习用于健康.
背景情况:
- 加速度计常用于测量身体活动,但依赖于特定站点的算法.
- 不遵守佩戴说明书可能导致不准确的活动强度估计.
- 现有的算法受限于它们依赖于精确的设备放置.
研究的目的:
- 开发深度神经网络模型,用于分类加速度计磨损部位和活动强度.
- 评估这些模型的性能与基本真相和现有的基于计数的算法相比.
主要方法:
- 在54名参与者的原始加速数据上训练长期短期记忆深度神经网络.
- 用部,手腕和胸部的加速度计收集活动数据.
- 使用便携式COSMED K5来测量基底真相强度的代谢等价物.
主要成果:
- 模型在分类磨损地点和活动强度方面达到90%以上的准确性.
- 性能超过了传统的基于计数的算法,这些算法的准确性较低.
- 包括参与者的年龄,身高和体重在内,模型的准确性提高到95%以上.
结论:
- 深度学习模型提供了准确的,非磨损的特定活动强度分类.
- 这些模型的性能优于现有的基于计数的算法.
- 允许参与者在加速度计放置方面的自主权可以提高合规性和数据准确性.
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