Journal
SENSORS
Volume 21, Issue 16, Pages -Publisher
MDPI
DOI: 10.3390/s21165576
Keywords
gait; ataxia; SARA; classification; machine learning
Funding
- Ministry of Health of the Czech Republic [FN HK 00179906]
- Charles University in Prague, Czech Republic [PROGRES Q40]
- European Regional Development Fund (ERDF)
- governmental budget of the Czech Republic [INTER-ACTION LTAIN19007]
- Development of Advanced Computational Algorithms [LTAIN19007]
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The study compares different data reduction methods and classification methods for clinical use. The best accuracy achieved is 98% by a random forest classifier preprocessed by t-distributed stochastic neighbour embedding.
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.
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