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

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

Optimizing heart rate regulation for safe exercise

Safe exercise protocols are critical for effective rehabilitation programs. This paper aims to develop a novel control strategy for an automated treadmill system to reduce the danger of injury during cardiac rehabilitation. We have developed a control-oriented nonparametric Hammerstein model for the control of heart rate during exercises by using support vector regression and correlation analysis. Based on this nonparametric model, a model predictive controller has been built. In order to guarantee the safety of treadmill exercise during rehabilitation, this new automated treadmill system is capable of optimizing system performance over predefined ranges of speed and acceleration. The effectiveness of the proposed approach was demonstrated with six subjects by having their heart rate track successfully a predetermined heart rate. © 2009 Biomedical Engineering Society

Heart rate control during treadmill exercise

A computer-controlled treadmill and related data collection and processing systems have been developed for the control of heart rate during treadmill exercise. Minimizing deviations of heart rate from a preset profile is achieved by controlling the speed and/or the gradient of the treadmill. A simple and practical heart rate measurement algorithm has been developed to robustly measure the variations of heart rate. Both conventional Proportional-Integral- Derivative (PID) control and fuzzy Proportional-Integral (PI) control approaches have been employed for the controller design. The fuzzy Proportional-Integral algorithm achieved better heart rate tracking performance. Finally, a heart rate based exercising protocol was successfully implemented on the newly designed exercise system. © 2005 IEEE

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

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. © 2007 IEEE

Heart rate regulation during cycle-ergometer exercise via bio-feedback

© 2015 IEEE. This paper explains our developed control system which regulates the heart rate (HR) to track a desired trajectory. The controller is indeed a non-conventional non-model-based proportional, integral and derivative (PID) controller. The controller commands are interpreted as biofeedback auditory commands. These commands can be heard and implemented by the exercising subject as a part of the control-loop. However, transmitting a feedback signal while the pedals are not in the appropriate position to efficiently exert force may lead to a cognitive disengagement of the user from the feedback controller. This note explains a novel form of control system regarding as 'actuator-based event-driven control system', designed specifically for the purpose of this project. We conclude that the developed event-driven controller makes it possible to precisely regulate HR to a predetermined HR profile

Robust control of heart rate for cycle ergometer exercise

The objective was to assess the performance and robustness of a novel strategy for automatic control of heart rate (HR) during cycle ergometry. Control design used a linear plant model and direct shaping of the closed-loop input-sensitivity function to achieve an appropriate response to disturbances attributable to broad-spectrum heart rate variability (HRV). The controller was evaluated in 73 feedback control experiments involving 49 participants. Performance and stability robustness were analysed using a separately identified family of 73 plant models. The controller gave highly accurate and stable HR tracking performance with mean root-mean-square tracking error between 2.5 beats/min (bpm) and 3.1 bpm, and with low average control signal power. Although plant parameters varied over a very wide range, key closed-loop transfer functions remained invariant to plant uncertainty in important frequency bands, while infinite gain margins and large phase margins (>62◦) were preserved across the whole plant model family. Highly accurate, stable and robust HR control can be achieved using LTI controllers of remarkably simple structure. The results highlight that HR control design must focus on disturbances caused by HRV. The input-sensitivity approach evaluated in this work provides a transparent method of addressing this challenge

Nonlinear modelling and control of heart rate response to treadmill walking exercise

In this study, a nonlinear system was developed for the modelling of the heart rate response to treadmill walking exercise. The model is a feedback interconnected system which can represent the neural response and peripheral local response to exercise. The parameters of the model were identified from an experimental study which involved 6 healthy adult male subjects, each completed 3 sets of walking exercise at different speeds. The proposed model will be useful in explaining the cardiovascular response to exercise. Based on the model, a 2-degree-of-freedom controller was developed for the regulation of the heart rate response during exercise. The controller consists of a piecewise LQ and an H∞, controllers. Simulation results showed that the proposed controller had the ability to regulate heart rate at a given target, indicating that the controller can play an important role in the design of exercise protocols for individuals

On heart rate regulation in cycle-ergometer exercise

© 2014 IEEE. In this paper, we have focused on the issue of regulating the human heart rate (HR) to a predefined reference trajectory, especially for cycle-ergometer exercise used for training or rehabilitation. As measuring HR is relatively easy compared to exercise intensity, it has been used in the wide range of training programs. The aim of this paper is to develop a non-model-based control strategy using proportional, integral and derivative (PID) controller/relay controller to regulate the HR to track a desired trajectory. In the case of using PID controller, the controller output signal is interpreted as a voice or auditory command, referred to as biofeedback, which can be heard by the exercising subject as a part of the control-loop. Alternatively, the relay controller output signals can be converted to some special words which can be recognised by the exerciser. However, in both cases, to effectively communicate to the user a change in exercise intensity, the timing of this feedback signal relative to the positions of the pedals becomes quite critical. A feedback signal delivered when the pedals are not in a suitable position to efficiently exert force may be ineffective and may lead to a cognitive disengagement of the user form the feedback controller. In this paper we examine the need and the consequence of synchronising the delivery of the feedback signal with an optimal and user specific placement of the pedal

Physiological Control of Human Heart Rate and Oxygen Consumption during Rhythmic Exercises

Physical exercise has significant benefits for humans in improving the health and quality of their lives, by improving the functional performance of their cardiovascular and respiratory systems. However, it is very important to control the workload, e.g. the frequency of body movements, within the capability of the individual to maximise the efficiency of the exercise. The workload is generally represented in terms of heart rate (HR) and oxygen consumption VO2. We focus particularly on the control of HR and VO2 using the workload of an individual body movement, also known as the exercise rate (ER), in this research. The first part of this report deals with the modelling and control of HR during an unknown type of rhythmic exercise. A novel feature of the developed system is to control HR via manipulating ER as a control input. The relation between ER and HR is modelled using a simple autoregressive model with unknown parameters. The parameters of the model are estimated using a Kalman filter and an indirect adaptive H1 controller is designed. The performance of the system is tested and validated on six subjects during rowing and cycling exercise. The results demonstrate that the designed control system can regulate HR to a predefined profile. The second part of this report deals with the problem of estimating VO2 during rhythmic exercise, as the direct measurement of VO2 is not realisable in these environments. Therefore, non-invasive sensors are used to measure HR, RespR, and ER to estimate VO2. The developed approach for cycling and rowing exercise predicts the percentage change in maximum VO2 from the resting to the exercising phases, using a Hammerstein model.. Results show that the average quality of fit in both exercises is improved as the intensity of exercise is increased