Assessing Pediatric Gait Symmetry Through Accelerometry and Computational Intelligence
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064165%3A_____%2F24%3A10485300" target="_blank" >RIV/00064165:_____/24:10485300 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21730/24:00380857 RIV/00216208:11110/24:10485300 RIV/00216208:11130/24:10485300 RIV/00216208:11150/24:10485300 and 4 more
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=GiuKQ3lC2I" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=GiuKQ3lC2I</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2024.3453933" target="_blank" >10.1109/ACCESS.2024.3453933</a>
Alternative languages
Result language
angličtina
Original language name
Assessing Pediatric Gait Symmetry Through Accelerometry and Computational Intelligence
Original language description
This paper focuses on the use of wearable sensors to acquire and process motion data, which is essential for monitoring physiological movement and identifying gait disorders. It is particularly relevant in pediatrics, neurology, and rehabilitation. The research evaluates body motion symmetry in children using accelerometric data, taking into account factors such as age, diagnosis, and gender. Signals were recorded from 35 children (average age 10.8 years) using mobile sensors and were analyzed using digital signal processing techniques and classification methods. The proposed methodology includes data acquisition by smartphone sensors, wireless data export to a remote drive, and data processing through a graphical user interface. The highest classification accuracy of walking features, at 92.0%, was achieved with a two-layer neural network. The findings underscore the effectiveness of these tools in rehabilitation, fitness monitoring, and neurological studies.
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
30209 - Paediatrics
Result continuities
Project
<a href="/en/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotics and advanced industrial production</a><br>
Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
2169-3536
Volume of the periodical
12
Issue of the periodical within the volume
September
Country of publishing house
US - UNITED STATES
Number of pages
11
Pages from-to
125358-125368
UT code for WoS article
001315989900001
EID of the result in the Scopus database
2-s2.0-85203529722