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使用嵌入智能手机的惯性传感器进行临床衰弱综合征评估。

Clinical frailty syndrome assessment using inertial sensors embedded in smartphones.

作者信息

Galán-Mercant A, Cuesta-Vargas A I

机构信息

Department of Physical Therapy, School of Medicine, University of Málaga, Málaga, Spain.

出版信息

Physiol Meas. 2015 Sep;36(9):1929-42. doi: 10.1088/0967-3334/36/9/1929. Epub 2015 Aug 6.

DOI:10.1088/0967-3334/36/9/1929
PMID:26245213
Abstract

The aim of this study was to identify the series of kinematic variables demonstrating the greatest precision in discriminating between the function of two groups of elderly persons (frail and non-frail) in the 10 m expanded timed up and go (ETUG) test using inertial sensors embedded in the iPhone 4(®). A cross-sectional study was conducted to identify the kinematic variables with the highest degree of precision in discriminating between the two groups. The predicted capability of the kinematic variables was evaluated using receiver operating characteristic curves. The sample comprised 30 participants over 65 years old, 14 frail and 16 non-frail, assessed for frailty syndrome using the Fried criteria. Acceleration variables discriminated between the participant groups in the study; specifically these were the peak negative acceleration variables for motion axes x, y and z. In terms of sensitivity, the values were greater than or equal to those for the variable traditionally used to discriminate in the ETUG test, namely time. The kinematic parameters obtained from the internal inertial sensors in the iPhone 4(®) are promising additions to the ETUG analysis. There are encouraging signs that the analyses of these parameters in the separate phases of the ETUG procedure offer the potential for improved discrimination between frail and non-frail individuals. However, further in-depth study is required to verify the findings.

摘要

本研究的目的是确定一系列运动学变量,这些变量在使用嵌入iPhone 4(®)的惯性传感器进行的10米扩展计时起立行走(ETUG)测试中,能够最精准地区分两组老年人(虚弱和非虚弱)的功能。开展了一项横断面研究,以确定在区分这两组时精度最高的运动学变量。使用受试者工作特征曲线评估运动学变量的预测能力。样本包括30名65岁以上的参与者,其中14名虚弱者和16名非虚弱者,采用Fried标准评估虚弱综合征。加速度变量在研究中区分了参与者组;具体而言,这些是运动轴x、y和z的负向峰值加速度变量。在敏感性方面,这些值大于或等于传统上用于ETUG测试中进行区分的变量(即时间)的值。从iPhone 4(®)内部惯性传感器获得的运动学参数有望为ETUG分析增添内容。有令人鼓舞的迹象表明,在ETUG程序的不同阶段对这些参数进行分析,有可能改善对虚弱和非虚弱个体的区分。然而,需要进一步深入研究以验证这些发现。

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