Please use this identifier to cite or link to this item: https://hdl.handle.net/1822/69786

TitleObjective graphical clustering of spatiotemporal gait pattern in patients with Parkinsonism
Author(s)Ferreira, Flora José Rocha
Gago, Miguel
Mollaei, Nafiseh
Bicho, Estela
Sousa, Nuno
Gama, João
Ferreira, Carlos
KeywordsParkinsonism
Gait analysis
Clustering
Classification
Issue date2020
PublisherAssociação Internacional de Paremiologia (AIP-IAP)
JournalAIP Conference Proceedings
CitationFerreira, F., Gago, M., Mollaei, N., Bicho, E., Sousa, N., Gama, J., & Ferreira, C. (2020, November). Objective graphical clustering of spatiotemporal gait pattern in patients with Parkinsonism. In AIP Conference Proceedings (Vol. 2293, No. 1, p. 420101). AIP Publishing LLC
Abstract(s)The goal of this study was grouping patients with parkinsonism that share similar gait characteristics based on principal component analysis (PCA). Spatiotemporal gait data during self-selected walking were obtained from 15 patients with Vascular Parkinsonism, 15 patients with Idiopathic Parkinson's Disease and 15 Controls. PCA was used to reduce the dimensionality of 12 gait characteristics for the 45 subjects. Fuzzy C-mean cluster analysis was performed plotting the first two principal components, which accounted for 84.1% of the total variability. Results indicates that it is possible to quantitatively differentiate different gait types in patients with parkinsonism using PCA. Objective graphical classification of gait patterns could assist in clinical evaluation as well as aid treatment planning.
TypeConference paper
URIhttps://hdl.handle.net/1822/69786
ISBN9780735440258
DOI10.1063/5.0026489
ISSN0094-243X
e-ISSN1551-7616
Publisher versionhttps://aip.scitation.org/doi/10.1063/5.0026489
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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