Retraction Note to: Predictive analysis of identification and disease condition monitoring using bioimpedance data

Santhosh, Sheeba; Juliet, A. Vimala; Krishnan, G. Hari

doi:10.1007/s12652-022-04029-y

J Ambient Intell Human Comput

2022

DOI: 10.1007/s12652-022-04029-y

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Retraction Note to: Predictive analysis of identification and disease condition monitoring using bioimpedance data

Sheeba Santhosh

A. Vimala Juliet

G. Hari Krishnan³

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“…In this proposed work, a monitoring system for automatic control of liquid level with the help of the Internet of things is used to control flow rate manually using a mobile app, monitor IV fluid level in the bag and provide warning to medical personnel to change the bag, and also a monitoring system for automatic control of liquid level with the help of the Internet of things is used to control flow rate manually using a mobile app (IoT) 14,15 . A Bluetooth module was used to detect the infusion rate and empty infusion in a remote drip infusion device 16,17 .…”

mentioning

confidence: 99%

IoT Based Automated Saline Drip and Hand Movement Detection System for Critical Care Patients

Krishnan¹,

Kumaraswamy²,

Umashankar³

et al. 2022

Biosci., Biotech. Res. Asia

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The major goal of this project is to develop a saline drip detection and control system used to inject saline or intravenous fluids into a patient's veins. This project presents an IoT-based system to address issues such as fluid backflow, blood loss, and so on. Solution to concerns like fluid backflow and blood loss, among others. As a result, we devised the Automated Intravenous Drip Monitoring System. In this project, we utilize an Arduino-based microcontroller to manage the drop rate, detect tube obstruction, and warn the user when the drips are emptied. Here we are going to monitor the drip rate whether it is low or over, by using ultrasonic sensor. By using the MEMS motion sensor, we can also detect the patient's movement while taking drips after they have undergone some surgery or in ICU condition. In addition, we are going to estimate the possibilities of blood clotting in the veins of the patient. If motion is detected in the infusion hose, the sensors will alert the doctors or nurses with a buzzer and an LED light. The flow rate and infusion interruption problems were monitored by transmitting the data wirelessly via Bluetooth via their smart phones using mobile applications and computer-based applications. As a result, at the nurse's station, the developing system can closely monitor the drip infusion situation of numerous patients.

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confidence: 99%

IoT Based Automated Saline Drip and Hand Movement Detection System for Critical Care Patients

Krishnan¹,

Kumaraswamy²,

Umashankar³

et al. 2022

Biosci., Biotech. Res. Asia

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Retraction Note to: Predictive analysis of identification and disease condition monitoring using bioimpedance data

Cited by 1 publication

References 0 publications

IoT Based Automated Saline Drip and Hand Movement Detection System for Critical Care Patients

IoT Based Automated Saline Drip and Hand Movement Detection System for Critical Care Patients

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