Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/12821
Title: Gait Classification Using A Support Vector Machine Algorithm
Authors: Petrovic Savic, Suzana
Ristic, Branko
Prodanovic, Nikola
Devedzic, Goran
Issue Date: 2020
Abstract: © 2020 IEEE. The gait pattern, as well as the walking process itself, can be an indicator of the overall health of patients. For this reason, it is very important to accurately, clearly and quickly determine the affiliation of the gait pattern (healthy or pathological) and take appropriate measures if necessary. As anterior cruciate ligament injuries are common and may be undetectable, this study presents a classification of gait using a support vector machine (SVM) algorithm. The test data were taken from a Gait LAB laboratory; anterior posterior translation and internal external rotation were used as significant parameters. The classifier performance was evaluated using a confusion matrix. These results showed that the SVM algorithm can be successfully used in tasks of this type of classification.
URI: https://scidar.kg.ac.rs/handle/123456789/12821
Type: conferenceObject
DOI: 10.1109/MECO49872.2020.9134075
SCOPUS: 2-s2.0-85088531533
Appears in Collections:Faculty of Engineering, Kragujevac
Faculty of Medical Sciences, Kragujevac

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