Principal component analysis of gait kinematics data in acute and chronic stroke patients
type: Item
instance of: scholarly article
title: Principal component analysis of gait kinematics data in acute and chronic stroke patients
zbMATH Open document ID: 1234.92033
DOI: 10.1155/2012/649743
author: Ivana Milovanović
author: Dejan B. Popovic
published in: Computational \& Mathematical Methods in Medicine
publication date: 2012-03-13T00:00:00Z
review text: Summary: We present the joint angles analysis by means of principal components analysis (PCA). The data from twenty-seven acute and chronic hemiplegic patients were used and compared with data from five healthy subjects. The data were collected during walking along a 10-meter long path. The PCA was applied on a data set consisting of hip, knee, and ankle joint angles of the paretic and the nonparetic leg. The results point to significant differences in joint synergies between the acute and chronic hemiplegic patients that are not revealed when applying typical methods for gait assessment (clinical scores, gait speed, and gait symmetry). The results suggest that the PCA allows classification of the origin for the deficit in the gait when compared to healthy subjects; hence, the most appropriate treatment can be applied in the rehabilitation.
Mathematics Subject Classification ID: 92C50
Mathematics Subject Classification ID: 62H25
Mathematics Subject Classification ID: 62P10
zbMATH DE Number: 6014142
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/649743
OpenAlex ID: W2117456027
Wikidata QID: Q34188056
cites work: An index of factorial simplicity
cites work: Identification of neuromuscular synergies in natural upper-arm movements
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description: scientific article; zbMATH DE number 6014142