Analysis of methods for modeling human daily thermometry data
- Authors: Shugurova M.A.1, Tsyganov A.V.1, Tsyganova Y.V.2
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Affiliations:
- Ulyanovsk State Pedagogical University named after I.N. Ulyanov
- Ulyanovsk State University
- Issue: Vol 24, No 4 (2022)
- Pages: 469-484
- Section: Mathematical modeling and computer science
- Published: 23.11.2022
- URL: https://journals.rcsi.science/2079-6900/article/view/368275
- DOI: https://doi.org/10.15507/2079-6900.24.202204.469-484
- ID: 368275
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Abstract
Mathematical and computer modeling of daily thermometry allows to study processes of human thermal homeostasis more deeply. In practice, thermometry data is obtained using a digital thermometer, which autonomously reads the temperature of human skin in certain time intervals. The aim of present work is to analyse the methods of modeling and processing of human daily thermometry data. The first method consists in applying linear discrete stochastic models in the state space with Gaussian noises and known vector of input actions, while the estimation of the state vector is performed by discrete covariance Kalman filter. The second method assumes that the vector of input actions is unknown, and the S. Gillijns and B.D. Moor algorithm is used to process daily thermometry data. An alternative option is to use a model with an extended state vector and a Kalman filtering algorithm. The third method takes into account the presence of anomalous measurements (outliers) in the measurement data, and correntropy filter is proposed for their effective filtering. Numerical experiments for modeling and processing of daily thermometry data in MATLAB were carried out in order to compare the quality of discrete filtering algorithms. Modeling of thermometry data was carried out using a three-dimensional model 3dDRCM (3-dimension Discrete-time Real-valued Canonical Model). The results obtained can be used in the study of human daily thermometry processes, for example, to study the reaction of the athlete’s body to the received load.
About the authors
Marina A. Shugurova
Ulyanovsk State Pedagogical University named after I.N. Ulyanov
Email: m.a.shugurova@gmail.com
ORCID iD: 0000-0001-9697-3816
PhD Student, School of Higher Mathematics
Russian Federation, 4/5 Lenin Sq., Ulyanovsk 432011, RussiaAndrey V. Tsyganov
Ulyanovsk State Pedagogical University named after I.N. Ulyanov
Author for correspondence.
Email: andrew.tsyganov@gmail.com
ORCID iD: 0000-0002-4173-5199
Professor, School of Higher Mathematics
Russian Federation, 4/5 Lenin Sq., Ulyanovsk 432011, RussiaYulia V. Tsyganova
Ulyanovsk State University
Email: tsyganovajv@gmail.com
ORCID iD: 0000-0001-8812-6035
Dr.Sci. (Physics and Mathematics), Professor, School of Information Technology, Faculty of Mathematics
Russian Federation, 42 Leo Tolstoy St., Ulyanovsk 432017, RussiaReferences
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