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Motion symmetry evaluation using accelerometers and energy distribution

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dc.title Motion symmetry evaluation using accelerometers and energy distribution en
dc.contributor.author Procházka, Aleš
dc.contributor.author Vyšata, Oldřich
dc.contributor.author Charvátová, Hana
dc.contributor.author Vališ, Martin
dc.relation.ispartof Symmetry
dc.identifier.issn 2073-8994 OCLC, Ulrich, Sherpa/RoMEO, JCR
dc.date.issued 2019
utb.relation.volume 11
utb.relation.issue 7
dc.type article
dc.language.iso en
dc.publisher Multidisciplinary Digital Publishing Institute (MDPI AG)
dc.identifier.doi 10.3390/sym11070871
dc.relation.uri https://www.mdpi.com/2073-8994/11/7/871
dc.subject microelectromechanical sensors en
dc.subject motion analysis en
dc.subject symmetry en
dc.subject digital signal processing en
dc.subject wavelet transform en
dc.subject feature extraction en
dc.subject classification en
dc.subject augmented reality en
dc.subject neurology en
dc.description.abstract Analysis of motion symmetry constitutes an important area with many applications in engineering, robotics, neurology and biomedicine. This paper presents the use ofmicroelectromechanical sensors (MEMS), including accelerometers and gyrometers, to acquire data via mobile devices so as to monitor physical activities and their irregularities. Special attention is devoted to the analysis of the symmetry of the motion of the body when the same exercises are performed by the right and the left limb. The analyzed data include the motion of the legs on a home exercise bike under different levels of load. The method is based on signal analysis using the discrete wavelet transform and the evaluation of signal segment features such as the relative energy at selected decomposition levels. The subsequent classification of the evaluated features is performed by k-nearest neighbours, a Bayesian approach, a support vector machine, and neural networks. The highest average classification accuracy attained is 91.0% and the lowest mean cross-validation error is 0.091, resulting from the use of a neural network. This paper presents the advantages of the use of simple sensors, their combination and intelligent data processing for the numerical evaluation of motion features in the rehabilitation and monitoring of physical activities. © 2019 by the authors. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1008953
utb.identifier.scopus 2-s2.0-85068578952
utb.identifier.wok 000481979000036
utb.source j-scopus
dc.date.accessioned 2019-08-16T09:30:12Z
dc.date.available 2019-08-16T09:30:12Z
dc.description.sponsorship Ministry of Health of the Czech Republic [FN HK 00179906]; Charles University in Prague, Czech Republic [PROGRES Q40]; project PERSONMED - European Regional Development Fund (ERDF) [CZ.02.1.010.00.017_0480007441]; governmental budget of the Czech Republic
dc.rights Attribution 4.0 International
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.rights.access openAccess
utb.contributor.internalauthor Charvátová, Hana
utb.fulltext.sponsorship This research was funded by grant projects of the Ministry of Health of the Czech Republic (FN HK 00179906) and of the Charles University in Prague, Czech Republic (PROGRES Q40), as well as by the project PERSONMED, Reg. No. CZ.02.1.010.00.017_0480007441, co-financed by European Regional Development Fund (ERDF) and the governmental budget of the Czech Republic.
utb.wos.affiliation [Prochazka, Ales; Vysata, Oldrich] Univ Chem & Technol Prague, Dept Comp & Control Engn, Prague 16628 6, Czech Republic; [Prochazka, Ales] Czech Tech Univ, Czech Inst Informat Robot & Cybernet, Prague 16000 6, Czech Republic; [Vysata, Oldrich; Valis, Martin] Charles Univ Prague, Fac Med Hradec Kralove, Univ Hosp Hradec Kralove, Dept Neurol, Hradec Kralove 50005, Czech Republic; [Charvatova, Hana] Tomas Bata Univ Zlin, Fac Appl Informat, Zlin 76001, Czech Republic
utb.scopus.affiliation Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, Prague 6, 166 28, Czech Republic; Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague 6, 160 00, Czech Republic; Department of Neurology, University Hospital Hradec Králové, Faculty of Medicine in Hradec Králové, Charles University in Prague, Hradec Králové, 500 05, Czech Republic; Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, 760 01, Czech Republic
utb.fulltext.projects FN HK 00179906
utb.fulltext.projects PROGRES Q40
utb.fulltext.projects CZ.02.1.010.00.017_0480007441
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