Abstract
Cardiac rehabilitation with treadmill is widely used in heart disease medical centers in developed countries including Korea because it significantly reduces the prevalence of heart disease and is effective in treating mild and severe patients. For the automation of cardiac rehabilitation exercise, dynamic model of patient heart rate according to treadmill speed has been proposed several times in developed countries where heart rehabilitation studies are active. The heart rate model of the human body differs from person to person because it is greatly influenced by various factors such as age, weight, health condition, and heart disease severity. However, existing studies on human heart rate modeling do not reflect these individual differences. In this paper, we introduce a method of estimating the heart rate model by using only the exercise that can be easily performed before the treadmill cardiac rehabilitation by using the signal compression method. In the experiment, the heart rate model was estimated as a 2-order transfer function closest to the equivalent impulse response. Based on the estimated model, we simulated in MATLAB using sliding mode control with sliding perturbation observer (SMCSPO), which is a robust controller for system nonlinearity, parameter uncertainty and disturbance.
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Acknowledgement
This research was supported by the Ministry of Trade, Industry & Energy and the Korea Evaluation Institute of Industrial Technology (KEIT) with the program number of “10038660”, the MOTIE (Ministry of Trade, Industry & Energy), Korea, under the Industry Convergence Liaison Robotics Creative Graduates Education Program supervised by the KIAT (N0001126), and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (1345253125).
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Kim, H.H., Kim, H.Y., Lee, H.Y., Lee, M.C. (2018). Signal Compression Method Based Heart Rate Model Estimation and SMCSPO Control for Cardiac Rehabilitation with Treadmill. In: Chen, Z., Mendes, A., Yan, Y., Chen, S. (eds) Intelligent Robotics and Applications. ICIRA 2018. Lecture Notes in Computer Science(), vol 10984. Springer, Cham. https://doi.org/10.1007/978-3-319-97586-3_19
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DOI: https://doi.org/10.1007/978-3-319-97586-3_19
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