Key result
A modelling and control integrated approach based on Support Vector Regression and switching Model Predictive Control effectively regulated dynamical heart rate response to exercise in simulation.
A novel switching Model Predictive Control algorithm based on Support Vector Regression can effectively model and regulate heart rate response during exercise in simulations.
Simulation supports SVR-MPC for exercise HR control; leaves open human validation before any clinical use.
In order to accurately regulate cardiovascular response to exercise for individual exerciser, this study proposes a modelling and control integrated approach based on ε-insensitive Support Vector Regression (SVR) and switching control strategy. Firstly, a control oriented modelling approach is proposed to depict nonlinear behaviours of cardiovascular response at both onset and offset of treadmill exercises by using support vector machine regression. Then, based on the established nonlinear time-variant model, a novel switching Model Predictive Control (MPC) algorithm has been proposed for the optimisation of exercise efforts. The designed controller can take into account both coefficient drifting and parameter jump by embedding the identified model coefficient into the optimiser and adopting switching strategy during the transfer between onset and offset of exercises. The effectiveness of the proposed modelling and control approach was shown from the regulation of dynamical heart rate response to exercise through simulation using MATLAB.
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Zhang et al. (2012) studied Heart rate response to exercise. Modelling and control integrated approach based on ε-insensitive Support Vector Regression (SVR) and switching Model Predictive Control (MPC) was evaluated on Regulation of dynamical heart rate response to exercise. A modelling and control integrated approach based on Support Vector Regression and switching Model Predictive Control effectively regulated dynamical heart rate response to exercise in simulation.
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