Machine learning-based nonlinear model predictive control for heart rate response to exercise

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Computational Intelligence and its Applications: Evolutionary Computation, Fuzzy Logic, Neural Network and Support Vector Machine Techniques, 2012, pp. 271 - 285
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© 2012 by Imperial College Press. All rights reserved. This study explores control methodologies to handle time variant behavior for heart rate dynamics at onset and offset of exercise. To achieve this goal, a novel switching model predictive control (MPC) algorithm is presented to optimize the exercise effects at both onset and offset of exercise. Specifically, dynamic matrix control (DMC), one of the most popular MPC control algorithms, has been employed as the essential of the optimization of process regulation while switching strategy has been adopted during the transfer between onset and offset of exercise. The parameters of the DMC/MPC controller have been well tuned based on a previously established SVM-based regression model relating to both onset and offset of treadmill walking exercises. The effectiveness of the proposed modeling and control approach has been shown from the regulation of dynamical heart rate response to exercise through simulation using Matlab.
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