Selection of gait parameters for modified Gillette Gait Index using Hellwig Correlation Based Filter method, random forest method, and correlation methods

Małgorzata Syczewska , Krzysztof Kocel , Anna Święcicka , Krzysztof Graff , Maciej Krawczyk , Piotr Wąsiewicz , Małgorzata Kalinowska , Ewa Szczerbik


Objective gait analysis provide a large number of data, which are used for planning further treatment of the patient. Data from groups of patients are used for comparisons of different treatment methods, assessment of the severity of gait deviations, design of classification systems. The Gilette Gait Index (GGI) was designed to express the level of abnormality of the gait pattern of patients with cerebral palsy by one number: a measure of distance between the set of discrete gait parameters of a patient from a similar set of a healthy subject, based on 16 parameters. Gait pathology in other disorders is different, thus other variables may better describe their level of pathology. The aim was to see if modified GGI can be constructed based on other sets of gait variables. To decide which gait variables should be taken three different analytical methods were used: Hellwig Correlation Based Filter, random forest, and correlation methods. Gait data of 84 patients were retrospectively selected: 30 had spastic cerebral palsy, 24 scoliosis, 30 suffered the stroke. The results show, that in the final sets of the 16 parameters chosen by the analyses there are some parameters, which were not present in the original list of GGI. If the number of sixteen parameters is kept, there are more than one optimal set of parameters. In conclusion, the use of analytical methods enabled the choice of sets of 16 gait parameters which are specific for the medical problem, and the calculation of modified GGIs.
Author Małgorzata Syczewska
Małgorzata Syczewska,,
, Krzysztof Kocel
Krzysztof Kocel,,
, Anna Święcicka
Anna Święcicka,,
, Krzysztof Graff
Krzysztof Graff,,
, Maciej Krawczyk
Maciej Krawczyk,,
, Piotr Wąsiewicz (FEIT / IN)
Piotr Wąsiewicz,,
- The Institute of Computer Science
, Małgorzata Kalinowska
Małgorzata Kalinowska,,
, Ewa Szczerbik
Ewa Szczerbik,,
Journal seriesBiocybernetics and Biomedical Engineering, ISSN 0208-5216
Issue year2020
Publication size in sheets0.5
Keywords in EnglishGillette Gait Index Gait parameters Hellwig method Random forest
ASJC Classification2204 Biomedical Engineering
Languageen angielski
Score (nominal)100
Score sourcejournalList
ScoreMinisterial score = 100.0, 20-08-2020, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2018 = 1.131; WoS Impact Factor: 2018 = 2.159 (2) - 2018=1.943 (5)
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