Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11150%2F21%3A10428922" target="_blank" >RIV/00216208:11150/21:10428922 - isvavai.cz</a>
Alternative codes found
RIV/60461373:22340/21:43922527 RIV/00179906:_____/21:10428922 RIV/68407700:21730/21:00354817
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=Kda8cVpxJR" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=Kda8cVpxJR</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TNSRE.2021.3051093" target="_blank" >10.1109/TNSRE.2021.3051093</a>
Alternative languages
Result language
angličtina
Original language name
Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring
Original language description
Ataxic gait monitoring and assessment of neurological disorders belong to important multidisciplinary areas that are supported by digital signal processing methods and machine learning tools. This paper presents the possibilityof using accelerometricdata to optimise deep learning convolutional neural network systems to distinguish between ataxic and normal gait. The experimental dataset includes 860 signal segments of 16 ataxic patients and 19 individuals from the control set with the mean age of 38.6 and 39.6 years, respectively. The proposed methodology is based upon the analysis of frequency components of accelerometric signals simultaneously recorded at specific body positionswith a sampling frequencyof 60Hz. The deep learning system uses all of the frequency components in a range of < 0, 30 > Hz. Our classification results are compared with those obtained by standard methods, which include the support vector machine, Bayesian methods, and the two-layer neural network with features estimated as the relative power in selected frequency bands. Our results show that the appropriate selection of sensor positions can increase the accuracy from 81.2% for the foot position to 91.7% for the spine position. Combining the input data and the deep learning methodology with five layers increased the accuracy to 95.8%. Our methodology suggests that artificial intelligence methods and deep learning are efficient methods in the assessment of motion disorders and they have a wide range of further applications.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30103 - Neurosciences (including psychophysiology)
Result continuities
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)<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
IEEE Transactions on Neural Systems and Rehabilitation Engineering
ISSN
1534-4320
e-ISSN
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Volume of the periodical
29
Issue of the periodical within the volume
MAR
Country of publishing house
US - UNITED STATES
Number of pages
8
Pages from-to
360-367
UT code for WoS article
000626331500016
EID of the result in the Scopus database
2-s2.0-85099576318