对比基于节奏和基于机器学习的物理活动强度分类估计:英国生物银行.
Le Wei1, Matthew N Ahmadi1, Mark Hamer2
1Mackenzie Wearables Research Hub, Charles Perkins Centre, The University of Sydney, Australia; School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Australia.
Journal of science and medicine in sport
|June 9, 2024
概括
节奏值为群体水平提供可接受的体力活动强度估计,但可能不适合个人评估. 一级节奏适用于中度至剧烈的活动,而两级节奏更适合剧烈和轻度的活动.
科学领域:
- 身体活动研究研究.
- 可穿戴式传感器技术的技术.
- 监测健康和健身,监测健康和健身.
背景情况:
- 节奏值通常用于对健康研究中的体力活动强度进行分类.
- 将这些方法与先进的算法进行验证对于准确的数据解释至关重要.
- 佩戴在手腕上的加速度计提供了对身体活动的客观测量.
研究的目的:
- 评估两种基于节奏的身体活动强度分类方法的融合有效性.
- 将基于节奏的方法与基于机器学习的强度方案进行比较.
- 评估节奏值的准确性,以估计在不同强度的体力活动中花费的时间.
主要方法:
- 一项有效性研究涉及84,315名40岁及以上的参与者.
- 一级和两级节奏方法与机器学习强度图的比较.
- 使用重叠图表,平均绝对误差和斯皮尔曼相关系数进行分析.
- 根据各种强度的实际重要差异值评估协议.
主要成果:
- 一级节奏为中度至强度和中度体力活动提供了可接受的群体级估计.
- 两级节奏显示了强烈和轻度体力活动的可接受的群体级估计.
- 个人层面的估计显示了基于节奏的和机器学习的方法之间存在很大的差异,参与者的比例在实际上重要的差异范围内很低.
结论:
- 基于节奏的方法显示了群体级体育活动强度估计的潜力.
- 一级节奏适合在组级对中度至强度和中度强度的评估.
- 两级节奏更适合在小组层面进行激烈和光强度评估.
- 基于节奏的方法可能不适合个人级别强度特定时间估计.
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