Long-term mobility monitoring of older adults using accelerometers in a clinical environment
Long-term mobility monitoring of older adults using accelerometers in a clinical environment
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DOI：
10.1191/0269215504cr734oa
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发表时间：
2004-05-01
影响因子：
3
通讯作者：
Lyons, D
中科院分区：
文献类型：
--
作者：
Culhane, KM;Lyons, GM;Lyons, D
目的：评估在临床环境中对老年人进行扩展测量期间基于加速度计的移动性监测的准确性，并评估两种不同的阈值方法。设计：监测设备由两个 Analog Devices ADXL202 加速度计、一个动态数据记录器和相关布线组成。监测系统使用定制设计的分析软件来检测日常生活活动，即监测期间坐、站、躺和移动的持续时间。一名研究人员在整个记录期间对受试者进行跟踪。 受试者和环境：本研究在四天的时间里监测了 5 名居住在康复诊所的具有不同程度活动能力的老年人。干预措施：使用 MATLAB(R) 程序分析加速度计数据，该程序允许设置躯干和大腿阈值角度，以区分坐、站、躺和移动。研究了设置这些阈值的两种不同方法：(1) 使用中点容差值 458 和 (2) 使用“最佳估计”容差值。分析程序生成活动摘要，然后将其与观察者创建的手动摘要逐行进行比较。结果是代表系统准确性的命中/未命中率。结果：使用中点公差值的坐卧检测精度较差，平均检测精度为75%。 “最佳估计”方法将坐和躺的检测准确度提高了约 18%，平均值为 93%。 结论：在老年人群中，使用此处概述的技术和阈值可以区分坐、站、躺的静态活动和动态活动，准确度可达 92% 或更高。
Objective: To assess the accuracy of accelerometer-based mobility monitoring during extended measurements on older adults in a clinical setting and to evaluate two different approaches to thresholding.Design: The monitoring device consisted of two Analog Devices ADXL202 accelerometers, an ambulatory data-logger and associated cabling. The monitoring system used custom-designed analysis software to detect activities of daily living, namely duration of sitting, standing, lying and moving during the period monitored. An investigator shadowed the subjects throughout the recording period.Subjects and setting: This study monitored five older adults, with varying degrees of mobility, resident in a rehabilitation clinic, over four days.Interventions: The accelerometer data were analysed using a MATLAB(R) program that allowed trunk and thigh threshold angles to be set to distinguish between sitting, standing, lying and moving. Two different approaches to setting these thresholds were investigated: (1) using a midpoint tolerance value of 458 and (2) using a 'best estimate' tolerance value. The analysis program generates a summary of activities, which is then compared line-by-line with the manual summary created by the observer. The result was a hit/miss ratio representative of the system's accuracy.Results: The detection accuracies for sitting and lying using a mid-point tolerance value were poor, with an average detection accuracy of 75% obtained. The 'best estimate' approach improved the detection accuracies for sitting and lying by approximately 18% to an average value of 93%.Conclusion: In a population of older adults, the static activities of sitting, standing and lying and dynamic activities can be distinguished using the technique and threshold values outlined here to a degree of accuracy of 92% and higher.