The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation Walking

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/70883521:28140/22:63556079 RIV/00216208:11150/22:10453351 RIV/68407700:21730/22:00364356

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation Walking

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation Walking

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LTAIN19007" target="_blank" >LTAIN19007: Vývoj pokročilých výpočetních algoritmů pro objektivní posouzení pooperační rehabilitace</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE transactions on neural systems and rehabilitation engineering

ISSN

1534-4320

e-ISSN

1558-0210

Svazek periodika

30

Číslo periodika v rámci svazku

Neuveden

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

7

Strana od-do

2467-2473

Kód UT WoS článku

000849260100011

EID výsledku v databázi Scopus

2-s2.0-85137136123