Wearable Method for Fall Detection Based on Kalman Filter and k-NN Algorithm

被引：4
|
作者：
He J. [1 ,2 ]
Zhou M. [2 ]
Wang X. [1 ,2 ]
机构：
[1] Advanced Innovation Center for Future Internet Technology, Beijing
[2] Beijing Engineering Research Center for IOT Software and Systems, Beijing
来源：
Wang, Xiaoyi (wxy@mail.bjut.edu.cn) | 1600年 / Science Press卷 / 39期
基金：
中国国家自然科学基金;
关键词：
Attitude angle; Computer application technology; Data fusion; Fall detection; K-NN algorithm; Kalman filter; Signal vector magnitude;
D O I：
10.11999/JEIT170173
中图分类号：
学科分类号：
摘要：
According to the accurate and real-time requirement for fall detection. An activity model based on attitude angles is firstly established. A sensor board integrated with trial-axil accelerator and gyroscope is developed, which can capture the accelerations and angular velocities of human activities and transmit them to a smart phone by Bluetooth. Secondly, the three-dimensional attitude angle and acceleration signal vector magnitude are selected as features for fall detection. The collected data is preprocessed using Kalman filter to reduce noise and enhance the precision of attitude angle calculation. The k-Nearest Neighbor (k-NN) algorithm and appropriate sliding window are introduced to develop the fall detection and alert system. At last, the experimental results show that the system discriminates falls from the activities of daily living with accuracy of 98.9%, while the sensitivity and specificity are 98.9%, and 98.5% respectively. It proves that the method has favorable accuracy and reliability. © 2017, Science Press. All right reserved.
引用
收藏
页码：2627 / 2634
页数：7
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