Discrimination of cycling patterns using accelerometric data and deep learning techniques
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11150%2F21%3A10438922" target="_blank" >RIV/00216208:11150/21:10438922 - isvavai.cz</a>
Alternative codes found
RIV/70883521:28140/20:63526346 RIV/60461373:22340/20:43920990 RIV/68407700:21730/21:00347478 RIV/00179906:_____/21:10438922
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=YmEHK1T4HS" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=YmEHK1T4HS</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00521-020-05504-3" target="_blank" >10.1007/s00521-020-05504-3</a>
Alternative languages
Result language
angličtina
Original language name
Discrimination of cycling patterns using accelerometric data and deep learning techniques
Original language description
The monitoring of physical activities and recognition of motion disorders belong to important diagnostical tools in neurology and rehabilitation. The goal of the present paper is in the cotribution to this topic by analysis of accelerometric signals recorded by wearable sensors located as specitifc body positions and by implementation of deep searning methods to classify signatl features.This paper uses the general methodology to analysis of accelerometric signals acquired during cycling at different routes followed by the global positioning system.
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
<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
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Volume of the periodical
33
Issue of the periodical within the volume
13
Country of publishing house
US - UNITED STATES
Number of pages
11
Pages from-to
7603-7613
UT code for WoS article
000590534800007
EID of the result in the Scopus database
2-s2.0-85096301452