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.

• Klíčová slova
• SARA, ataxia, classification, gait, machine learning,
• MeSH
• ataxie diagnóza   MeSH
• chůze (způsob) MeSH
• kvalita života * MeSH
• lidé MeSH
• neurologické poruchy chůze * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart rate during cycling, under different body loads. Acquired data include 1293 signal segments recorded by the mobile phone and the Garmin device for uphill and downhill cycling. The proposed method is based upon digital processing of the heart rate and the mean power in different frequency bands of accelerometric data. The classification of the resulting features was performed by the support vector machine, Bayesian methods, k-nearest neighbor method, and neural networks. The proposed criterion is then used to find the best positions for the sensors with the highest discrimination abilities. The results suggest the sensors be positioned on the spine for the classification of uphill and downhill cycling, yielding an accuracy of 96.5% and a cross-validation error of 0.04 evaluated by a two-layer neural network system for features based on the mean power in the frequency bands 〈 3 , 8 〉 and 〈 8 , 15 〉 Hz. This paper shows the possibility of increasing this accuracy to 98.3% by the use of more features and the influence of appropriate sensor positioning for motion monitoring and classification.

• Klíčová slova
• accelerometers, classification, computational intelligence, machine learning, motion monitoring, multimodal signal analysis,
• MeSH
• akcelerometrie metody   MeSH
• algoritmy MeSH
• Bayesova věta MeSH
• cvičení MeSH
• cyklistika * MeSH
• fitness náramky * MeSH
• lidé MeSH
• mobilní telefon přístrojové vybavení   MeSH
• neuronové sítě MeSH
• počítačové zpracování signálu MeSH
• pohyb těles MeSH
• reprodukovatelnost výsledků MeSH
• rozpoznávání automatizované MeSH
• software MeSH
• srdeční frekvence * MeSH
• statistické modely MeSH
• support vector machine MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH