基于集合经验模态分解的老年人步态信号的降噪方法

基于手机传感器的行人航位推算(PDR)作为一种不依赖外部信息和硬件就可以自主定位导航的方法,在室内环境下试图去普及应用。老年人与年轻人在步频与步幅上有很大差异,常用的年轻人步态降噪方法并不适用于老年人,大大降低PDR的定位精度。针对该问题,本文以手机内加速度传感器信号为数据依据,以检测老年人步数为研究目标,采用卡尔曼滤波、巴特沃斯低通滤波、集合经验模态分解等方法,确定老年人的步数。通过对比分析,确定基于集合经验模态分解的降噪方法,对老年的检步精度达到98%以上,较采用卡尔曼滤波与巴特沃斯低通滤波的方法提高了19.4%。

Denoising Method for Gait Signals of Elderly People Based on EEMD

Pedestrian dead reckoning(PDR)based on mobile phone sensor can be used to popularize applications in an indoor environment as a method of autonomous navigation without relying on external information and hardware.Older people and young people have great differences in stride frequency and stride length.Commonly used young gait noise reduction methods are not suitable for the elderly,which greatly reduces the positioning accuracy of PDR.Aiming at this problem,this paper takes the acceleration sensor signal in mobile phone as the data basis,and takes the detection of the number of steps in the elderly as the research goal.The kalman filter,butterworth low-pass filtering,and the collection of empirical mode decomposition are used to determine the steps of the elderly.number.Through comparative analysis,the noise reduction method based on ensemble empirical mode decomposition is determined.The accuracy of detection for older people is over 98%,which is 19.4%higher than that of kalman filter and butterworth low-pass filtering.