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Classification of Ataxic Gait

O. Vyšata, O. Ťupa, A. Procházka, R. Doležal, P. Cejnar, AM. Bhorkar, O. Dostál, M. Vališ

. 2021 ; 21 (16) : . [pub] 20210819

Jazyk angličtina Země Švýcarsko

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/bmc21025107

Grantová podpora
FN HK 00179906 Ministerstvo Zdravotnictví Ceské Republiky
PROGRES Q40 Charles University in Prague, Czech Republic
CZ.02.1.01-0.0-0.0-17 048-0007441 Charles University in Prague, Czech Republic

Gait disorders accompany a number of neurological and musculoskeletal disorders that significantly reduce the quality of life. Motion sensors enable high-quality modelling of gait stereotypes. However, they produce large volumes of data, the evaluation of which is a challenge. In this publication, we compare different data reduction methods and classification of reduced data for use in clinical practice. The best accuracy achieved between a group of healthy individuals and patients with ataxic gait extracted from the records of 43 participants (23 ataxic, 20 healthy), forming 418 segments of straight gait pattern, is 98% by random forest classifier preprocessed by t-distributed stochastic neighbour embedding.

Citace poskytuje Crossref.org

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