학술논문
Human Activities Classification Using Biaxial Seismic Sensors
Document Type
Periodical
Author
Source
IEEE Sensors Letters IEEE Sens. Lett. Sensors Letters, IEEE. 4(10):1-4 Oct, 2020
Subject
Language
ISSN
2475-1472
Abstract
In this letter, we propose a method for passive human activity classification exploiting ground vibrations observed by a biaxial geophone. The solution is grounded on the idea that some activities can be better analyzed by the horizontal channel (bicycle and car) and others by the vertical one (walk and run). Thus, the following two solutions are proposed: first, joint processing of the vertical and horizontal data by a single classifier and, second, cascade processing by two classifiers that analyze the two channels separately. Numerical results based on real data show that while a parametric method such as a support vector machine performs well in both cases, a nonparametric method such as the $k$-nearest neighbors reaches a higher accuracy in cascade processing. Besides, the results are compared with those obtained using a monoaxial geophone only.