Dynamic modelling of heart rate response under different exercise intensity.

Heart rate is one of the major indications of human cardiovascular response to exercises. This study investigates human heart rate response dynamics to moderate exercise. A healthy male subject has been asked to walk on a motorised treadmill under a predefined exercise protocol. ECG, body movements, and oxygen saturation (SpO2) have been reliably monitored and recorded by using non-invasive portable sensors. To reduce heart rate variation caused by the influence of various internal or external factors, the designed step response protocol has been repeated three times. Experimental results show that both steady state gain and time constant of heart rate response are not invariant when walking speed is faster than 3 miles/hour, and time constant of offset exercise is noticeably longer than that of onset exercise

Identification and control for heart rate regulation during treadmill exercise

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 ε-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 H ∞ 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. © 2007 IEEE

Nonparametric Hammerstein model based model predictive control for heart rate regulation.

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

A nonlinear dynamic model for heart rate response to treadmill walking exercise

A nonlinear dynamic model for heart rate response to treadmill walking exercise, Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE, 22-26 Aug. 2007]. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Technology, Sydney's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it A nonlinear dynamic model for heart rate response to treadmill walking exercise Teddy M. Cheng, Andrey V. Savkin, Branko G. Celler, Lu Wang, Steven W. Su Abstract-A dynamic model of the heart rate response to treadmill walking exercise is presented. The model is a feedback interconnected system; the subsystem in the forward path represents the neural response to exercise, while the subsystem in the feedback path describes the peripheral local response. The parameters of the model were estimated from 5 healthy adult male subjects, each undertaking 3 sets of walking exercise at different speeds. Simulated responses from the model closely match the experimental data both in the exercise and the recovery phases. The model will be useful in explaining the cardiovascular response to exercise and in the design of exercise protocols for individuals

Modelagem por partes da cinética da resposta cronotrópica cardíaca durante a caminhada em esteira ergométrica

The objective of this study was to model the variations in the intervals between a R-peak and another (IRR) of the electrocardiogram (ECG) in response to walking steps through an exponential first order model. Twenty-five men, young and healthy, performed the test protocol comprised of: 1 min of rest, followed by 2 min of warming up at 3 km / h and four steps, with 4 min of duration at speeds of 5 km / h, 7 km / h, 5 km / h and 0 km / h. The IRR signal was acquired continuously by means of an electrocardiograph and a cardiofrequency meter. The Nelder Mead Simplex method was applied to adjust the time constant (τ) and the gain (k) of the model to the actual IRR values in each step, so that the subsequent steps curve were added to the previous ones. The adjustment obtained R2 = 0.9 (0.85-0.92) and statistically different values of τ and k between the steps, with p << 0.05. These results indicate that the model is able to describe the HR during walking at different speeds and that its dynamics, in submaximal exercise, is non-linear, since steps of same value presented different responses.O objetivo deste estudo foi modelar as variações dos intervalos entre um pico R e outro (IRR) do eletrocardiograma (ECG) em resposta a degraus de velocidade durante a caminhada, por meio de um modelo exponencial de primeira ordem. Vinte e cinco homens, jovens e saudáveis, realizaram o protocolo de teste compreendido por: 1 min de repouso, seguido por 2 min de aquecimento a 3 km/h e quatro degraus, com 4 min de duração para as velocidades de 5 km/h, 7 km/h, 5 km/h e 0 km/h. O sinal de IRR foi adquirido continuamente por meio de um eletrocardiógrafo e um cardiofrequencímetro. Aplicou-se o método Nelder Mead Simplex para ajustar a constante de tempo () e o ganho () do modelo aos valores reais de IRR em cada degrau, de modo que a curva dos degraus subsequentes foram somadas às anteriores. O ajuste obteve R2 = 0,9 (0,85-0,92) e valores de e estatisticamente diferentes entre os degraus, com p << 0,05. Tais resultados indicam que o modelo é capaz de descrever a FC durante a caminhada em diferentes velocidades e que a sua dinâmica, em exercício submáximo, é não linear, posto que degraus de mesmo valor apresentaram respostas diferentes