在中风后的人在日常生活中复制上肢表现的基于传感器的分类,以及对其他人群的概括性
Chelsea E Macpherson1, Marghuretta D Bland1,2,3, Christine Gordon1
1Program in Physical Therapy, Washington University School of Medicine, St. Louis, MO 63110, USA.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
可穿戴传感器显示上肢 (UL) 活动最好被视为连续性,而不是不同的类别,用于跟踪各种条件的功能变化.
科学领域:
- 康复科学 康复科学 康复科学
- 生物医学工程 生物医学工程
- 运动科学 运动科学
背景情况:
- 可穿戴式传感器提供了对上肢 (UL) 活动的客观测量.
- 这些传感器的单个变量可能不能完全代表复杂的UL性能.
- 之前的研究在中风患者和对照人群中确定了五个多变量类别的UL活性.
研究的目的:
- 复制和扩展有关UL活动绩效类别的先前发现.
- 调查这些类别在各种 UL 条件中的通用性.
- 确定分析可穿戴传感器UL活动数据的最佳模型.
主要方法:
- 收集了来自324名参与者与各种UL条件 (中风,多发性硬化,骨折,疼痛,乳腺癌后) 和对照者的人口统计,自我报告和可穿戴传感器数据.
- 使用不同数量的加速度计变量的主要组件 (PC) 分析.
- 使用集群分析和评估模型适合样本子集.
主要成果:
- 两个主要成分 (PC) 解释了大量的差异:总体UL活性和首选的肢体使用.
- 一个五个变量,五个集群模型证明了样本之间的最佳匹配.
- PC和个体加速度计变量与集群相比,与自我报告结果的收有效性更强.
结论:
- 发现UL活动的五个变量,五个集群模型是可复制和可概括的.
- 有证据表明,日常生活中的UL活动更好地作为连续体而不是离散类别来表示.
- 本研究提出了一种统一的,数据驱动的方法来监测UL条件的功能变化.
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