Bourke A K, Lyons G M
Biomedical Electronics Laboratory, Department of Electronic and Computer Engineering, University of Limerick, Limerick, Ireland.
Med Eng Phys. 2008 Jan;30(1):84-90. doi: 10.1016/j.medengphy.2006.12.001. Epub 2007 Jan 11.
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 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.
本文描述了一种基于阈值的算法，用于区分日常生活活动（ADL）和跌倒。使用了基于陀螺仪的跌倒检测传感器阵列。通过年轻志愿者在监督条件下在防撞垫上进行模拟跌倒以及老年受试者进行日常生活活动，利用安装在躯干上的双轴陀螺仪传感器测量俯仰和滚动角速度，并结合基于阈值的算法，实现了区分跌倒和日常生活活动的能力。使用Matlab进行数据分析，以确定在八种不同的跌倒和日常生活活动类型中记录的角加速度、角速度和躯干角度变化。确定了三个阈值，以便将跌倒与日常生活活动区分开来：如果合成角速度大于3.1弧度/秒（跌倒阈值1），合成角加速度大于0.05弧度/秒²（跌倒阈值2），并且躯干角度的合成变化大于0.59弧度（跌倒阈值3），则检测到跌倒。结果表明，对于总共480次运动的数据集，跌倒与日常生活活动的区分准确率可达100%。
