Article

Modelling and Control for Heart Rate Regulation during Treadmill Exercise

Biomed. Syst. Lab., New South Wales Univ., Sydney, NSW, Australia.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2006; 1:4299-302. DOI: 10.1109/IEMBS.2006.260573
Source: IEEE Xplore

ABSTRACT

This paper proposes a novel integrated approach for the identification and control of Hammerstein systems to achieve desired heart rate tracking performance for an automated treadmill system. The pseudo-random binary sequence input is employed to decouple the identification of dynamic linear part from static nonlinearity. The powerful e-insensitivity support vector regression is adopted to obtain sparse representations of the inversion of static nonlinearity in order to obtain an approximated linear model of the Hammerstein system. An H(infinity) controller is designed for the approximated linear model to achieve robust tracking performance. This new approach is applied to the design of a computer-controlled treadmill system for the regulation of heart rate during treadmill exercise. Minimizing deviations of heart rate from a preset profile is achieved by controlling the speed of the treadmill. Both conventional proportional-integral-derivative (PID) control and the proposed approaches have been employed for the controller design. The proposed algorithm achieves much better heart rate tracking performance.

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    • "However, even in the absence of external interference the heart rate can vary substantially over time under the influence of various internal or external factors. Therefore , an improved exponential weighted moving average filter together with a simple outlier detection algorithm [3], [50], [51] is adopted for the estimation of the heart rate. Specifically, the control computer collects heart rate signal from a Polar receiver through an analog input port every 2 s, and calculates heart rate by using an edge detection algorithm. "
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    ABSTRACT: This paper proposes a novel integrated approach for the identification and control of Hammerstein systems to achieve desired heart rate profile tracking performance for an automated treadmill system. For the identification of Hammerstein systems, the pseudorandom binary sequence input is employed to decouple the identification of dynamic linear part from input nonlinearity. The powerful epsilon-insensitivity support vector regression method is adopted to obtain sparse representations of the inverse of static nonlinearity in order to obtain an approximate linear model of the Hammerstein system. An Hinfinity controller is designed for the approximated linear model to achieve robust tracking performance. This new approach is successfully applied to the design of a computer-controlled treadmill system for the regulation of heart rate during treadmill exercise. Minimizing deviations of heart rate from a preset profile is achieved by controlling the speed of the treadmill. Both conventional proportional-integral-derivative (PID) control and the proposed approaches have been employed for the controller design. The proposed algorithm achieves much better heart rate tracking performance.
    Full-text · Article · Aug 2007 · IEEE Transactions on Biomedical Engineering
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    • "[17], we presented a H ∞ based control approach for the control of heart rate response with exercises and obtained desired tracking performance for healthy young exercisers. In this study, we plan to cope with heart rate regulation for rehabilitation. "
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    ABSTRACT: This paper proposed a novel nonparametric model based model predictive control approach for the regulation of heart rate during treadmill exercise. As the model structure of human cardiovascular system is often hard to determine, nonparametric modelling is a more realistic manner to describe complex behaviours of cardiovascular system. This paper presents a new nonparametric Hammerstein model identification approach for heart rate response modelling. Based on the pseudo-random binary sequence experiment data, we decouple the identification of linear dynamic part and input nonlinearity of the Hammerstein system. Correlation analysis is applied to acquire step response of linear dynamic component. Support Vector Regression is adopted to obtain a nonparametric description of the inverse of input static nonlinearity that is utilized to form an approximate linear model of the Hammerstein system. Based on the established model, a model predictive controller under predefined speed and acceleration constraints is designed to achieve safer treadmill exercise. Simulation results show that the proposed control algorithm can achieve optimal heart rate tracking performance under predefined constraints.
    Full-text · Article · Feb 2007 · Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
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    ABSTRACT: Exercise testing systems with appropriate gas exchange measurements offer important data for sport training, medical diagnosis, rehabilitation and evaluation of cardio respiratory kinetics. Measuring of basic physiological parameters during exercise test is an important task in order to establish the working capacity or physical condition of the patient and also enable derive models of important physiological parameters. Exercise testing offers the investigator the possibility to study simultaneously cellular, cardiovascular and respiratory systems and their response during accurately controlled workload. Heart rate, pulmonary ventilation, breathing frequency and blood pressure are automatically measured during the examination. Also oxygen and carbon dioxide content in expired air are measured and intensity of the workload is set by computer. From these data other standard parameters like oxygen uptake, CO2 expenditure etc. during the whole test could be calculated by means of personal computer. In this paper, nonlinear modelling approach is used for Hammerstein system which is applied to estimate cardiovascular system response to exercise. These models can be used in situations, where only limited parameters can be registered e.g. during out door workload testing.
    No preview · Article · Jan 2011
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