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.

Human activities classification using biaxial seismic sensors

Lorenzo Pucci;Enrico Testi;Elia Favarelli;Andrea Giorgetti
2020

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.
Lorenzo Pucci; Enrico Testi; Elia Favarelli; Andrea Giorgetti
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/802210
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