Motion Analysis Using Global Navigation Satellite System and Physiological Data
Motion analysis using wearable sensors is an essential research topic with broad mathematical foundations and applications in various areas, including engineering, robotics, and neurology. This paper presents the use of the global navigation satellite system (GNSS) for detecting and recording the po...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10107613/ |
| _version_ | 1797833235577700352 |
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| author | Ales Prochazka Alexandra Molcanova Hana Charvatova Oana Geman Oldrich Vysata |
| author_facet | Ales Prochazka Alexandra Molcanova Hana Charvatova Oana Geman Oldrich Vysata |
| author_sort | Ales Prochazka |
| collection | DOAJ |
| description | Motion analysis using wearable sensors is an essential research topic with broad mathematical foundations and applications in various areas, including engineering, robotics, and neurology. This paper presents the use of the global navigation satellite system (GNSS) for detecting and recording the position of a moving body, along with signals from additional sensors, for monitoring of physical activity and analyzing heart rate dynamics during running on route segments of different slopes and speeds. This method provides an alternative to the heart monitoring on the treadmill ergometer in the cardiology laboratory. The proposed computational methodology involves digital data preprocessing, time synchronization, and data resampling to enable their correlation, feature extraction both in time and frequency domains, and classification. The datasets include signals acquired during ten experimental runs in the selected area. The motion patterns detection involves segmenting the signals by analysing the GNSS data, evaluating the patterns, and classifying the motion signals under different terrain conditions. This classification method compares neural networks, support vector machine, Bayesian, and <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbour methods. The highest accuracy of 93.3 % was achieved by using combined features and a two-layer neural network for classification into three classes with different slopes. The proposed method and graphical user interface demonstrate the efficiency of multi-channel and multi-dimensional signal processing with applications in rehabilitation, fitness movement monitoring, neurology, cardiology, engineering, and robotic systems. |
| first_indexed | 2024-04-09T14:21:08Z |
| format | Article |
| id | doaj.art-de758b4440434b65858efabcfc3bd8ec |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T14:21:08Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-de758b4440434b65858efabcfc3bd8ec2023-05-04T23:00:11ZengIEEEIEEE Access2169-35362023-01-0111420964210310.1109/ACCESS.2023.327010210107613Motion Analysis Using Global Navigation Satellite System and Physiological DataAles Prochazka0https://orcid.org/0000-0002-0270-1738Alexandra Molcanova1Hana Charvatova2https://orcid.org/0000-0001-7363-976XOana Geman3Oldrich Vysata4Department of Mathematics, Informatics and Cybernetics, University of Chemistry and Technology Prague, Prague, Czech RepublicDepartment of Mathematics, Informatics and Cybernetics, University of Chemistry and Technology Prague, Prague, Czech RepublicFaculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech RepublicDepartment of Health and Human Development, Stefan cel Mare University of Suceava, Suceava, RomaniaDepartment of Neurology, Faculty of Medicine in Hradec Králové, Charles University, Hradec Králové, Czech RepublicMotion analysis using wearable sensors is an essential research topic with broad mathematical foundations and applications in various areas, including engineering, robotics, and neurology. This paper presents the use of the global navigation satellite system (GNSS) for detecting and recording the position of a moving body, along with signals from additional sensors, for monitoring of physical activity and analyzing heart rate dynamics during running on route segments of different slopes and speeds. This method provides an alternative to the heart monitoring on the treadmill ergometer in the cardiology laboratory. The proposed computational methodology involves digital data preprocessing, time synchronization, and data resampling to enable their correlation, feature extraction both in time and frequency domains, and classification. The datasets include signals acquired during ten experimental runs in the selected area. The motion patterns detection involves segmenting the signals by analysing the GNSS data, evaluating the patterns, and classifying the motion signals under different terrain conditions. This classification method compares neural networks, support vector machine, Bayesian, and <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbour methods. The highest accuracy of 93.3 % was achieved by using combined features and a two-layer neural network for classification into three classes with different slopes. The proposed method and graphical user interface demonstrate the efficiency of multi-channel and multi-dimensional signal processing with applications in rehabilitation, fitness movement monitoring, neurology, cardiology, engineering, and robotic systems.https://ieeexplore.ieee.org/document/10107613/Multichannel signal processingglobal navigation satellite systemsfeature extractionmachine learningcomputational intelligenceclassification |
| spellingShingle | Ales Prochazka Alexandra Molcanova Hana Charvatova Oana Geman Oldrich Vysata Motion Analysis Using Global Navigation Satellite System and Physiological Data IEEE Access Multichannel signal processing global navigation satellite systems feature extraction machine learning computational intelligence classification |
| title | Motion Analysis Using Global Navigation Satellite System and Physiological Data |
| title_full | Motion Analysis Using Global Navigation Satellite System and Physiological Data |
| title_fullStr | Motion Analysis Using Global Navigation Satellite System and Physiological Data |
| title_full_unstemmed | Motion Analysis Using Global Navigation Satellite System and Physiological Data |
| title_short | Motion Analysis Using Global Navigation Satellite System and Physiological Data |
| title_sort | motion analysis using global navigation satellite system and physiological data |
| topic | Multichannel signal processing global navigation satellite systems feature extraction machine learning computational intelligence classification |
| url | https://ieeexplore.ieee.org/document/10107613/ |
| work_keys_str_mv | AT alesprochazka motionanalysisusingglobalnavigationsatellitesystemandphysiologicaldata AT alexandramolcanova motionanalysisusingglobalnavigationsatellitesystemandphysiologicaldata AT hanacharvatova motionanalysisusingglobalnavigationsatellitesystemandphysiologicaldata AT oanageman motionanalysisusingglobalnavigationsatellitesystemandphysiologicaldata AT oldrichvysata motionanalysisusingglobalnavigationsatellitesystemandphysiologicaldata |