The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation Walking
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F22%3A43924513" target="_blank" >RIV/60461373:22340/22:43924513 - isvavai.cz</a>
Alternative codes found
RIV/70883521:28140/22:63556079 RIV/00216208:11150/22:10453351 RIV/68407700:21730/22:00364356
Result on the web
<a href="https://ieeexplore.ieee.org/document/9866077" target="_blank" >https://ieeexplore.ieee.org/document/9866077</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TNSRE.2022.3201487" target="_blank" >10.1109/TNSRE.2022.3201487</a>
Alternative languages
Result language
angličtina
Original language name
The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation Walking
Original language description
Gait analysis and the assessment of rehabilitation exercises are important processes that occur during fitness level monitoring and the treatment of neurological disorders. This paper presents the possibility of using oximetric, heart rate (HR), accelerometric, and global navigation satellite systems (GNSSs) to analyse signals recorded during uphill and downhill walking without and with a face mask to find its influence on physiological functions during selected walking patterns. The experimental dataset includes 86 signal segments acquired under different conditions. The proposed methodology is based on signal analysis in both the time and frequency domains. The results indicate that face mask use has a minimal effect on blood oxygen concentration and heart rate, with the average mean changes of these parameters being less than 2%. The support vector machine, a Bayesian method, the k-nearest neighbour method, and a two-layer neural network showed very good separation abilities and successfully classified different walking patterns only in the case when the effect of face mask wearing was not included in the classification process. Our methodology suggests that artificial intelligence and machine learning tools are efficient methods for the assessment of motion patterns in different motion conditions and that face masks have a negligible effect for short-duration experiments. © 2001-2011 IEEE.
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
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/LTAIN19007" target="_blank" >LTAIN19007: Development of Advanced Computational Algorithms for evaluating post-surgery rehabilitation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
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
1558-0210
Volume of the periodical
30
Issue of the periodical within the volume
Neuveden
Country of publishing house
US - UNITED STATES
Number of pages
7
Pages from-to
2467-2473
UT code for WoS article
000849260100011
EID of the result in the Scopus database
2-s2.0-85137136123