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Recognition of motion patterns using accelerometers for ataxic gait assessment

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11150%2F21%3A10422642" target="_blank" >RIV/00216208:11150/21:10422642 - isvavai.cz</a>

  • Alternative codes found

    RIV/60461373:22340/20:43921136 RIV/68407700:21730/21:00347475 RIV/00179906:_____/21:10422642

  • Result on the web

    <a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=qHmpns7HMq" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=qHmpns7HMq</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>

Alternative languages

  • 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.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    30103 - Neurosciences (including psychophysiology)

Result continuities

  • Project

    <a href="/en/project/EF17_048%2F0007441" target="_blank" >EF17_048/0007441: PERSONMED - Center for the Development of Personalized Medicine in Age-Related Diseases</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2021

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Neural Computing and Applications

  • ISSN

    0941-0643

  • e-ISSN

  • Volume of the periodical

    33

  • Issue of the periodical within the volume

    7

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    9

  • Pages from-to

    2207-2215

  • UT code for WoS article

    000543276900001

  • EID of the result in the Scopus database

    2-s2.0-85087045961