To effectively study radial pulses for various applications, there exists a tremendous need for a pulsatile simulator that can generate various radial pulse waveforms cost-effectively and reliably. Such a radial… Click to show full abstract
To effectively study radial pulses for various applications, there exists a tremendous need for a pulsatile simulator that can generate various radial pulse waveforms cost-effectively and reliably. Such a radial simulator can make significant contributions to the research and development efforts in estimating central blood pressures, validating computational work on arterial pulse mechanics, developing wearable devices, and modernizing pulse diagnosis. The primary goal of this study is to develop a pulsatile simulator that can generate a wide range of radial pulse waveforms. To develop an effective and well-controlled simulator, it proposes to use Magneto-Rheological (MR) fluids whose motion can be controlled instantly based on the applied magnetic field strength. The MR pulsatile simulator is designed to produce various 'two-peak' pulse waveform patterns and control the shape of waveforms by adjusting the ratio of the peak amplitudes and the distance or the time delay between the two peaks (effectively changing the augmentation index, AI). To this end, an experimental setup consists of a peristaltic pump, a tubing connected to the MR fluid reservoir, and electromagnets with control electronics is constructed. The Pulse Width Modulation (PWM) technique is employed to control magnetic fields that regulates the MR fluids flow. Using the prototype, a series of experiments are performed to evaluate its effectiveness in generating various radial pulse patterns and controlling the pulse waveforms. The results show that the MR simulator is capable of generating actual human radial pulse waveforms for different ages. The results further show that the MR simulator changed the amplitude of the systolic pressure peak and the time delay of the peaks according to the input PWM signals, demonstrating its controllability for the AI of pressure waveforms.
               
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