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Machine learning in rehabilitation assessment for thermal and heart rate data processing

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dc.title Machine learning in rehabilitation assessment for thermal and heart rate data processing en
dc.contributor.author Procházka, Aleš
dc.contributor.author Charvátová, Hana
dc.contributor.author Vaseghi, Saeed
dc.contributor.author Vyšata, Oldřich
dc.relation.ispartof IEEE Transactions on Neural Systems and Rehabilitation Engineering
dc.identifier.issn 1534-4320 OCLC, Ulrich, Sherpa/RoMEO, JCR
dc.date.issued 2018
utb.relation.volume 26
utb.relation.issue 6
dc.citation.spage 1209
dc.citation.epage 1214
dc.type article
dc.language.iso en
dc.publisher Institute of Electrical and Electronics Engineers Inc.
dc.identifier.doi 10.1109/TNSRE.2018.2831444
dc.relation.uri https://ieeexplore.ieee.org/document/8352748/
dc.subject Computational intelligence en
dc.subject Machine learning en
dc.subject Multimodal signal analysis en
dc.subject Physical rehabilitation assessment en
dc.subject Respiratory monitoring en
dc.subject Thermal imaging en
dc.description.abstract Multimodal signal analysis based on sophisticated noninvasive sensors, efficient communication systems, and machine learning, have a rapidly increasing range of different applications. The present paper is devoted to pattern recognition and the analysis of physiological data acquired by heart rate and thermal camera sensors during rehabilitation. A total number of 56 experimental data sets, each 40 min long, of the heart rate and breathing temperature recorded on an exercise bike have been processed to determine the fitness level and possible medical disorders. The proposed general methodology combines machine learning methods for the detection of the changing temperature ranges of the thermal camera and adaptive image processing methods to evaluate the frequency of breathing. To determine the individual temperature values, a neural network model with the sigmoidal and the probabilistic transfer function in the first and the second layers are applied. Appropriate statistical methods are then used to find the correspondence between the exercise activity and selected physiological functions. The evaluated mean delay of 21 s of the heart rate drop related to the change of the activity level corresponds to results obtained in real cycling conditions. Further results include the average value of the change of the breathing temperature (167 s) and breathing frequency (49 s). © 2001-2011 IEEE. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1008060
utb.identifier.obdid 43878540
utb.identifier.scopus 2-s2.0-85046376219
utb.identifier.wok 000438078700011
utb.identifier.pubmed 29877845
utb.identifier.coden ITNSB
utb.source j-scopus
dc.date.accessioned 2018-07-27T08:47:42Z
dc.date.available 2018-07-27T08:47:42Z
utb.contributor.internalauthor Charvátová, Hana
utb.scopus.affiliation Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, Czech Republic; Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic; Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, Prague, Czech Republic
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