Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm
Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm
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DOI：
10.1016/j.gaitpost.2006.09.012
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发表时间：
2007-07-01
期刊：
影响因子：
2.4
通讯作者：
Lyons, G. M.
中科院分区：
文献类型：
--
作者：
Bourke, A. K.;O'Brien, J. V.;Lyons, G. M.
使用模拟跌倒和老年受试者日常生活活动(ADL)，研究了安装在躯干和大腿上的三轴加速度计传感器区分跌倒和ADL的能力。使用MatLab进行数据分析，以确定在八种不同类型的跌倒中记录的峰值加速度。这些动作包括：向前摔、向后摔和左右横摔，双腿伸直和屈曲。跌倒检测算法是使用阈值技术设计的。从480个动作的总数据集来看，跌倒可以与ADL区分开来。这是使用由坠落事件数据集确定的单个阈值来实现的，该阈值应用于来自位于主干的三轴加速度计的合成幅度加速度信号。(C)2006爱思唯尔B.V.保留所有权利。
Using simulated falls performed under supervised conditions and activities of daily living (ADL) performed by elderly subjects, the ability to discriminate between falls and ADL was investigated using tri-axial accelerometer sensors, mounted on the trunk and thigh. Data analysis was performed using MATLAB to determine the peak accelerations recorded during eight different types of falls. These included; forward falls, backward falls and lateral falls left and right, performed with legs straight and flexed. Falls detection algorithms were devised using thresholding techniques. Falls could be distinguished from ADL for a total data set from 480 movements. This was accomplished using a single threshold determined by the fall-event data-set, applied to the resultant-magnitude acceleration signal from a tri-axial accelerometer located at the trunk. (c) 2006 Elsevier B.V. All rights reserved.