The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation Walking
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 system...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9866077/ |
| _version_ | 1797805105834098688 |
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| author | Ales Prochazka Hana Charvatova Oldrich Vysata |
| author_facet | Ales Prochazka Hana Charvatova Oldrich Vysata |
| author_sort | Ales Prochazka |
| collection | DOAJ |
| 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 <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-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. |
| first_indexed | 2024-03-13T05:47:08Z |
| format | Article |
| id | doaj.art-a959f0ca3f014ca8a9868da6e80618e5 |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:47:08Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-a959f0ca3f014ca8a9868da6e80618e52023-06-13T20:09:13ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102022-01-01302467247310.1109/TNSRE.2022.32014879866077The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation WalkingAles Prochazka0https://orcid.org/0000-0002-0270-1738Hana Charvatova1https://orcid.org/0000-0001-7363-976XOldrich Vysata2Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, Prague, Czech RepublicFaculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech RepublicDepartment of Neurology, Faculty of Medicine, Charles University in Hradec Králové, Hradec Králové, Czech RepublicGait 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 <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-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.https://ieeexplore.ieee.org/document/9866077/Classificationcomputational intelligenceface masksgait analysismachine learningmotion monitoring |
| spellingShingle | Ales Prochazka Hana Charvatova Oldrich Vysata The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation Walking IEEE Transactions on Neural Systems and Rehabilitation Engineering Classification computational intelligence face masks gait analysis machine learning motion monitoring |
| title | The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation Walking |
| title_full | The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation Walking |
| title_fullStr | The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation Walking |
| title_full_unstemmed | The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation Walking |
| title_short | The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation Walking |
| title_sort | effect of face masks on physiological data and the classification of rehabilitation walking |
| topic | Classification computational intelligence face masks gait analysis machine learning motion monitoring |
| url | https://ieeexplore.ieee.org/document/9866077/ |
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