使用可穿戴传感器的定量步态数据,分析与高大脑小血管疾病负担相关的步态模式
Kelin Xu1, Yingzhe Wang2, Yanfeng Jiang3
1Department of Biostatistics, Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China.
Computer methods and programs in biomedicine
|April 17, 2024
概括
可穿戴传感器显示出与大脑小血管疾病 (CSVD) 负担相关的独特步态模式. 这种以改变步态参数为特征的模式有助于识别患CSVD风险较高的个体.
科学领域:
- 神经学 神经学
- 生物医学工程 生物医学工程
- 老年学是一门学科.
背景情况:
- 大脑小血管疾病 (CSVD) 显著导致步行障碍.
- 与高CSVD负担相关的特定步态模式需要进一步调查.
- 穿戴式传感器技术为特定疾病的模式提供了定量步态分析.
研究的目的:
- 识别和描述与大脑小血管疾病 (CSVD) 的高负担相关的步态模式.
- 探索定量步态参数在检测CSVD中的实用性.
- 为高CSVD负担建立特定疾病的步态模式.
主要方法:
- 来自泰州成像研究的720名参与者 (55-65岁) 的步态分析.
- 使用内类可穿戴设备和脑MRI进行定量步态评估.
- 稀疏切片逆回归的应用,以提取与高CSVD负担相关的步态模式.
主要成果:
- 一个特定的步态模式,包括角度,阶段,变化和时空领域,与CSVD负担有显著的关联.
- 具有高CSVD负担的个体表现出更小的步行角度,增加的姿势和双重支时间,更大的步行变化和更慢的速度.
- 这种步态与高CSVD负担风险增加25%相关.
结论:
- 建立了高CSVD负担的稳定,疾病特异性的定量步态模式.
- 这种步行模式可以帮助识别社区和一般人口中CSVD负担较高的个体.
- 穿戴式步态分析为早期检测和管理CSVD相关步态问题提供了有价值的工具.
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