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生态落预测在患有轻度认知障碍,落高风险的患者的敏感性,特异性和准确性

Chaesu Kim^1,2, Haeun Park^1,2, Joshua Sung You^1,2

¹Sports Movement Artificial-Intelligence Robotics Technology (SMART) Institute, Department of Physical Therapy, Yonsei University, Wonju 26493, Republic of Korea.

Sensors (Basel, Switzerland)

|August 12, 2023

概括

这项研究开发了一种姿势摇摆检测 (PSD) 系统,用于预测轻度认知障碍 (MCI) 的老年人跌倒. 该系统在现实环境中表现出良好的灵敏度和特异性.

科学领域:

老年学是指老年学的学科.
神经学神经学
生物医学工程生物医学工程

背景情况:

对于患有轻度认知障碍 (MCI) 的老年人来说,跌倒是一个显著的风险.
增加的姿势摆动与跌倒有关,但在现实环境中的研究有限.
需要有效的姿势摇摆检测 (PSD) 系统来预测MCI人群的跌倒.

研究的目的:

评估新型PSD系统的跌落预测灵敏度,特异性和准确性.
评估MCI患者在生态环境中的系统性能.
确定系统在落户者和非落户者之间进行区分的能力.

主要方法:

一组由40名健康的年轻人和患有MCI且患有高跌倒风险的老年人组成的队列参与了这项研究.
在生态环境中使用PSD系统测量姿势摇摆.
用接收器操作特征 (ROC) 曲线和曲线下的面积 (AUC) 来分析数据.
同时记录Berg平衡尺度和定时上升和去测试进行.

主要成果:

降落预测灵敏度在0.82到0.99.9之间.
秋季预测的特异性在0.69和0.90之间变化.
预测跌倒的准确性在0.53到0.81.8之间.
该PSD系统显示了合理的歧视能力.

结论:

开发的PSD系统表明,在患有MCI的老年人中,有潜力区分失败者和非失败者.
该系统对预测这种人群在现实世界环境中的跌倒非常有希望.
在生态环境中进一步验证PSD系统是有必要的.

关键词:

准确度准确度准确度准确度准确度生态环境生态环境在秋天,秋天,秋天下降.轻度的认知障碍轻度的认知障碍姿势摇摆是一种姿势摇摆.灵敏度灵敏度灵敏度灵敏度灵敏度特殊性的特异性

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