In:
Sensors, MDPI AG, Vol. 21, No. 16 ( 2021-08-19), p. 5576-
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1424-8220
Language:
English
Publisher:
MDPI AG
Publication Date:
2021
detail.hit.zdb_id:
2052857-7
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