Subspace identification and classification of healthy human gait.

ABSTRACT:

Purpose

The classification between different gait patterns is a frequent task in gait assessment. The base vectors were usually found using principal component analysis (PCA) is replaced by an iterative application of the support vector machine (SVM). The aim was to use classifyability instead of variability to build a subspace (SVM space) that contains the information about classifiable aspects of a movement. The first discriminant of the SVM space will be compared to a discriminant found by an independent component analysis (ICA) in the SVM space.

Methods

Eleven runners ran using shoes with different midsoles. Kinematic data, representing the movements during stance phase when wearing the two shoes, was used as input to a PCA and SVM. The data space was decomposed by an it

SUBMITTER: von Tscharner V 

PROVIDER: S-EPMC3704597 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature