Recognition of motion patterns using accelerometers for ataxic gait assessment

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F20%3A43921136" target="_blank" >RIV/60461373:22340/20:43921136 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11150/21:10422642 RIV/00179906:_____/21:10422642 RIV/68407700:21730/21:00347475

Result on the web

<a href="https://link.springer.com/article/10.1007/s00521-020-05103-2" target="_blank" >https://link.springer.com/article/10.1007/s00521-020-05103-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00521-020-05103-2" target="_blank" >10.1007/s00521-020-05103-2</a>

Result language

angličtina

Original language name

Recognition of motion patterns using accelerometers for ataxic gait assessment

Original language description

The recognition of motion patterns belongs to very important research areas related to neurology, rehabilitation, and robotics. It is based on modern sensor technologies and general mathematical methods, multidimensional signal processing, and machine learning. The present paper is devoted to the detection of features associated with accelerometric data acquired by 31 time-synchronized sensors located at different parts of the body. Experimental data sets were acquired from 25 individuals diagnosed as healthy controls and ataxic patients. The proposed method includes the application of the discrete Fourier transform for the estimation of the mean power in selected frequency bands and the use of these features for data segments classification. The study includes a comparison of results obtained from signals recorded at different positions. Evaluations are based on classification accuracy and cross-validation errors estimated by support vector machine, Bayesian, nearest neighbours (k-NN], and neural network (NN) methods. Results show that highest accuracies of 77.1%, 78.9%, 89.9%, 98.0%, and 98.5% were achieved by NN method for signals acquired from the sensors on the feet, legs, uplegs, shoulders, and head/spine, respectively, recorded in 201 signal segments. The entire study is based on observations in the clinical environment and suggests the importance of augmented reality to decisions and diagnosis in neurology. © 2020, Springer-Verlag London Ltd., part of Springer Nature.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Neural Computing and Applications

ISSN

0941-0643

e-ISSN

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Volume of the periodical

Neuveden

Issue of the periodical within the volume

25 June 2020

Country of publishing house

GB - UNITED KINGDOM

Number of pages

9

Pages from-to

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UT code for WoS article

000543276900001

EID of the result in the Scopus database

2-s2.0-85087045961