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.
Wireless smart infusion pumps are currently under development. It is critical to ensure that the patient receives the correct drug concentration. Practically, the performance of the pump has relied on the minimum startup delay. The minimization of the startup delay is prominent in open-type infusion pumps and rarely in closed types. The emphasis on reducing startup delay puts practitioners and caregivers at ease while ensuring patient safety. The startup delay of the infusion pump is based on the flow rate and the lag time. The prediction of the flow rate and lag time for an infusion pump is necessitated to ensure a safe drug dosage for the patient. Currently, machine learning methods and computational methods to predict the desired parameter are widely used in healthcare applications and medical device performance. The reduction of start-up delay can be achieved by predicting its associated parameters lag time and flow rate. The flow rate is dependent on the speed of the infusion pump, which has to be calculated based on the number of gears and revolutions. The speed of the pump has to be predicted for accurate flow delivery. Our present research attempts to predict the lag time of an infusion pump using different kernel functions of support vector regression (SVR). The performance of the SVR for each kernel function is compared with R2, RMSE, MAE, and prediction accuracy. The prediction accuracy of 99,7 % has been obtained in optimized SVM.
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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