In this work, an attempt has been made to identify the appropriate parameters of Permanent Magnet Direct Current (PMDC) motor for infusion pump. PMDC motor plays important role in medical devices. In this, selection of parameters such as rotor inertia, armature resistance, armature inductance and back electro motive force constant is crucial that help to achieve the required speed. The proposed work uses PID controller (Proportional Integral Derivative) and LQG (Linear-Quadratic Gaussian) control algorithm to evaluate the parameters for transient response of the PMDC motor. It is demonstrated that the chosen parameters are able to reach the required speed with quick rise time by 0.691 seconds by employing LQG.
The revolutionary change in recent technologies has dominated the Medical field. Huge medical devices are deployed in hospitals and in-home environment. The innovations of smart medical devices are prominent which can be used in the fully automated and semi-automated environment. For several decades, numerous medical devices are designed and utilized effectively. These devices are designed for the past few years, manufactured in micro and nanotechnologies with smart sensing techniques. Such devices are network-enabled and integrated into the cyber world in physical means, leading to a medical cyber physical system. The design, working, and optimal control of smart medical devices are essential. In this paper, the emphasis on the review of infusion pump control mechanism with essential parameters such as drug delivery, flow rate, pump control has been considered.
Prediction techniques are extensively used in medical applications and health care devices. The prediction of the infusion flow rate for the required drug dosage and drug concentration in a smart wireless infusion pump is necessary for precise drug flow for the patients. In this paper, the prediction model has been developed to predict the lag time using Gaussian Process Regression (GPR) technique with a squared exponential kernel. Currently, a smart wireless infusion pump is incorporated with its smart drug library. The required parameters such as drug dosage, drug flow rate are utilized as inputs to predict the lag time and to minimize start-up delays using the proposed regression technique. The evaluation of the prediction model is done by the coefficient of determination (R2), mean absolute error (MAE), and root-mean-squared error (RMSE). These prediction results are verified for predicting lag time for two different carrier flowrates 10 ml/hr and 50 ml/hr. The outcome of the study indicates that the regression model GPR has better prediction accuracy with a mean R2 of 0.9. Hence, the GPR technique is capable to achieve quick infusion and optimal flow rate with minimized lag time for smart infusion pumps.
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