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To maintain the hemodynamic stability of patient undergoing hemodialysis, this article proposes a novel model-based control methodology to regulate the changes in relative blood volume (RBV) and percentage change in heart rate (∆HR(%)) during hemodialysis by adjusting the ultrafiltration rate (UFR). The control algorithm uses model predictive control (MPC) to account for system variability and to explicitly handle the constraints on UFR. Linear state-space system with time-varying parameters is introduced to model the RBV and ∆HR. MPC was used to track the change in RBV and ∆HR to pre-defined reference trajectories. At each sampling instant, the system parameters are updated to get the best fitting into the parameterized model. Simulation results demonstrate that the system is able to regulate RBV and ∆HR to the reference by adjusting UFR while keeping it within practically realizable bounds. The results show that adjusting UFR may improve the stability of patient during dialysis when compared to conventional hemodialysis with constant UFR. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The main motivation of this study is to establish an ambulatory cardio-respiratory analysis system for the monitoring and evaluation of exercise and regular daily physical activity. We explored the estimation of oxygen uptake by using noninvasive portable sensors. These sensors are easy to use but may suffer from malfunctions under free living environments. A promising solution is to combine sensors with different measuring mechanisms to improve both reliability and accuracy of the estimation results. For this purpose, we selected a wireless heart rate sensor and a tri-axial accelerometer to form a complementary sensor platform. We analyzed the relationship between oxygen uptake measured by gas analysis and data collected from the simple portable sensors using multivariable nonlinear modeling approaches. It was observed that the resulting nonlinear multivariable model could not only achieve a better estimate compared with single input single output models, but also had greater potential to improve reliability. [ABSTRACT FROM AUTHOR]