Blood Oxygen Concentration and Physiological Data Changes During Motion While Wearing Face Masks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F22%3A43924520" target="_blank" >RIV/60461373:22340/22:43924520 - isvavai.cz</a>
Alternative codes found
RIV/70883521:28140/22:63556098 RIV/00216208:11150/22:10453349 RIV/68407700:21730/22:00364359
Result on the web
<a href="https://ieeexplore.ieee.org/document/9869822" target="_blank" >https://ieeexplore.ieee.org/document/9869822</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2022.3202931" target="_blank" >10.1109/ACCESS.2022.3202931</a>
Alternative languages
Result language
angličtina
Original language name
Blood Oxygen Concentration and Physiological Data Changes During Motion While Wearing Face Masks
Original language description
The study of physiological changes recorded by wearable devices during physical exercises belongs to very important research topics in neurology for the detection of motion disorders or monitoring of the fitness level during sports activities. This paper contributes to this area with studies of the effect of face masks and respirators on blood oxygen concentration, breathing frequency, and the heart rate changes. Experimental data sets include 296 segments of their total length of 60 hours, recorded on a home exercise bike under different motion conditions. Wearable instruments with oximetric, heart rate, accelerometric, and thermal camera sensors were used to fill the own database of signals recorded with selected sampling frequencies. The proposed methodology includes fundamental signal and image processing methods for signal analysis and machine learning tools for labeling image components and detecting facial temperature changes. Results show the minimal effect of mask wearing on blood oxygen concentration but its substantial influence on the breathing frequency and the heart rate. The use of a respirator substantially increased the respiratory rate for the given set of experiments under the load. This indicates how wearable sensors, computational intelligence, and machine learning can be used for motion monitoring and data analysis of signals recorded in different conditions. © 2013 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 Access
ISSN
2169-3536
e-ISSN
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Volume of the periodical
10
Issue of the periodical within the volume
Neuveden
Country of publishing house
US - UNITED STATES
Number of pages
8
Pages from-to
91763-91770
UT code for WoS article
000852480700001
EID of the result in the Scopus database
2-s2.0-85137583288