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.

• Keywords
• SARA, ataxia, classification, gait, machine learning,
• MeSH
• Ataxia diagnosis   MeSH
• Gait MeSH
• Quality of Life * MeSH
• Humans MeSH
• Gait Disorders, Neurologic * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH