CAJ | 학술논문

针对行人行走和跑步步态差异会影响行人定位精度的问题,提出一种基于支持向量机(support vector machine,SVM)的步态实时分类方法,将行人步态分为行走和跑步2类。行人步态原始数据由安装于足部的加速度计和陀螺仪提供,通过对原始数据进行坐标系转换、快速傅里叶变换(fast fourier transform,FFT)和数据降维等步骤建立 SVM的训练数据输入量。算法采用测试多组匀速或变速的行走和跑步数据进行实验验证,以支持向量空间中的高斯分布作为判断依据。结果表明,SVM的实时分类成功率达到98.6%以上。
침대행인행주화포보보태차이회영향행인정위정도적문제,제출일충기우지지향량궤(support vector machine,SVM)적보태실시분류방법,장행인보태분위행주화포보2류。행인보태원시수거유안장우족부적가속도계화타라의제공,통과대원시수거진행좌표계전환、쾌속부리협변환(fast fourier transform,FFT)화수거강유등보취건립 SVM적훈련수거수입량。산법채용측시다조균속혹변속적행주화포보수거진행실험험증,이지지향량공간중적고사분포작위판단의거。결과표명,SVM적실시분류성공솔체도98.6%이상。
It presents an SVM-based gait classification method to the problem of positioning accuracy in pedestrian inertial navigation because of the differences between walking and running.The algorithm divides pedestrian gaits into walking and running in real time using an SVM classifier.The original gait data was collected from an IMU installed on the foot.The establishment of training data includes coordinates transformation,FFT and dimension reduction.Tests of several walking and running data on both constant and variable speed are used to verify this method,according to the Gaussian distribution in SVM space.The results show a 98.6% successful rate of real time classification using SVM.