A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor
A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor
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DOI：
10.1016/j.medengphy.2006.12.001
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发表时间：
2008-01-01
影响因子：
2.2
通讯作者：
Lyons, G. M.
中科院分区：
文献类型：
--
作者：
Bourke, A. K.;Lyons, G. M.
描述了一种基于阈值的算法，用于区分日常生活活动（ADL）和福尔斯。使用基于陀螺仪的跌倒检测传感器阵列。使用由年轻志愿者在监督条件下在防撞垫上进行的模拟福尔斯跌倒和由老年受试者进行的ADL，使用安装在躯干上的双轴陀螺仪传感器测量俯仰和滚转角速度以及基于阈值的算法来实现区分福尔斯和ADL的能力。使用MATLAB（R）进行数据分析，以确定在八种不同的跌倒和ADL类型期间记录的角加速度、角速度和躯干角度的变化。确定了三个阈值，以便可以将跌倒与ADL区分开：如果合成角速度大于3.1 rads/s（跌倒阈值1），合成角加速度大于0.05 rads/s（2）（跌倒阈值2），并且躯干角度的合成变化大于0.59 rad（跌倒阈值3），则检测到跌倒。结果表明，福尔斯可以区分从ADL与100%的准确性，为480个动作的总数据集。(c)2006年IPEM。由爱思唯尔有限公司出版。保留所有权利。
A threshold-based algorithm, to distinguish between Activities of Daily Living (ADL) and falls is described. A gyroscope based fall-detection sensor array is used. Using simulated-falls performed by young volunteers under supervised conditions onto crash mats and ADL performed by elderly subjects, the ability to discriminate between falls and ADL was achieved using a bi-axial gyroscope sensor mounted on the trunk, measuring pitch and roll angular velocities, and a threshold-based algorithm. Data analysis was performed using MATLAB (R) to determine the angular accelerations, angular velocities and changes in trunk angle recorded, during eight different fall and ADL types. Three thresholds were identified so that a fall could be distinguished from an ADL: if the resultant angular velocity is greater than 3.1 rads/s (Fall Threshold 1), the resultant angular acceleration is greater than 0.05 rads/s(2) (Fall Threshold 2), and the resultant change in trunk-angle is greater than 0.59 rad (Fall Threshold 3), a fall is detected. Results show that falls can be distinguished from ADL with 100% accuracy, for a total data set of 480 movements. (c) 2006 IPEM. Published by Elsevier Ltd. All rights reserved.