Smart Infusion Pump Control: The Control System Perspective

Alamelu, J. V.; Mythili, A.

doi:10.1007/978-3-030-68723-6_10

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Apart from prediction accuracy, the R 2 was 0,87 for Q c 10 ml/hr and 0,71 for Q c 50 ml/hr. The performance of the infusion pump exclusively relies upon its speed and dosage drip and flow rate, the system performance based on the control system strategies needs analysis (25) and it can be further incorporated. A quick induction of the dosage with less dead time, lag time, has to be achieved.…”

Section: Resultsmentioning

confidence: 99%

Machine learning approach to predict delay in smart infusion pump

Alamelu

Sivanantham

2022

Salud, Ciencia y Tecnología

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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.

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Section: Resultsmentioning

confidence: 99%

Machine learning approach to predict delay in smart infusion pump

Alamelu

Sivanantham

2022

Salud, Ciencia y Tecnología

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“…Even when programming is carried out using intelligent alerts, precise motor control flow must be taken into account. Alamelu & Mythili (2021) discusses a framework for the best motor control to carry out a precise medication flow to the patient utilising an infusion device. A mathematical model of the infusion pump and the electric motor it is connected to makes up the framework.…”

Section: Introductionmentioning

confidence: 99%

Working Principle and Intelligence of Smart Infusion Pump

Ali¹,

Rehan

Zahid

et al. 2023

PJS

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There is a tremendous demand on the critical care resources due to the extensivespread of the ongoing coronavirus pandemic and the large number of patients requiring critical care.The efficacy of a device directly influences how long a patient lives since patients are often receivingcritical care. Smart infusion pump is a medical device that can drip fluids into the patient's body. Thisdevice is considered as one of the most safety-critical medical devices due to the way it functions andthe risks it presents. The main objective of this work is to develop an affordable infusion pump usingembedded technology. The main tactic is to develop equipment that can identify air bubbles in infusionpump tubing since even a little one might obstruct blood flow and result in mortality. This method mayprovide consumers an accurate result, making it the greatest method for identifying bubbles and savinglives.

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Medical Cyber Physical System Architecture for Smart Medical Pumps

Alamelu

Moyya

Mythili

2022

Advances in Systems Analysis, Software Engineering, and High Performance Computing

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The transformations through technological innovations have influenced the medical field. There are significant developments in medical devices in their usage. The utilization of the devices is automated in a local, remote environment. The medical devices used in the remote cyber environment uses different network protocols. These devices comprise micro, nanofabricated sensors and actuators which have the facility to communicate using network protocols. The devices that have network capability to integrate into cyberspace through physical methods are typical medical cyber physical systems (MCPS). In MCPS, medical device modelling is an important aspect. Several medical devices are available, and here in the current research, emphasis is focused on smart medical pumps in the MCPS environment. This chapter highlights the essential concepts of the smart medical drug delivery device, its architecture, control, actuation, communication, and analysis in the cyber environment.

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Smart Infusion Pump Control: The Control System Perspective

Cited by 3 publications

References 23 publications

Machine learning approach to predict delay in smart infusion pump

Machine learning approach to predict delay in smart infusion pump

Working Principle and Intelligence of Smart Infusion Pump

Medical Cyber Physical System Architecture for Smart Medical Pumps

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