Data mining time series of human locomotion data based on functional approximation

Abstract

In many of medical applications specially one considering time sequence data like data describing human gait, searching through large, unstructured databases based on sample sequences is often desirable. Such similarity-based retrieval has attracted a great deal of attention in recent years. Although several different approaches have appeared, most are not specialized for the problem of human locomotion. This paper gives an overview of one proposed approach how to efficiency process human gait by using Fourier’s series approximation, genetic algorithms for coefficients corrections and Spectral Signatures as data mining method. This paper shows how these methods fit into a general context of signature extraction and disorder recognition in the human gait case.