Machine Learning in Rehabilitation Assessment for Thermal and Heart Rate Data Processing
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F18%3A63519836" target="_blank" >RIV/70883521:28140/18:63519836 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21730/18:00328956 RIV/00216208:11150/18:10381466 RIV/60461373:22340/18:43915773
Result on the web
<a href="https://ieeexplore.ieee.org/document/8352748/authors#authors" target="_blank" >https://ieeexplore.ieee.org/document/8352748/authors#authors</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TNSRE.2018.2831444" target="_blank" >10.1109/TNSRE.2018.2831444</a>
Alternative languages
Result language
angličtina
Original language name
Machine Learning in Rehabilitation Assessment for Thermal and Heart Rate Data Processing
Original language description
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).
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
ISSN
1534-4320
e-ISSN
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Volume of the periodical
26
Issue of the periodical within the volume
6
Country of publishing house
US - UNITED STATES
Number of pages
6
Pages from-to
1209-1214
UT code for WoS article
000438078700011
EID of the result in the Scopus database
2-s2.0-85046376219