An Intelligent Real Time IoT Based System (IRTBS) for Monitoring ICU Patient

Prajapati, Bharat; Parikh, Satyen M.; Patel, Jignesh M.

doi:10.1007/978-3-319-63645-0_44

Cited by 15 publications

(7 citation statements)

References 16 publications

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“…In case of medication discrepancy, such as missed or over dosage, an alarm is generated alerting both the patients as well as the medical staff. Moreover, in [ 97 ], authors designed a patient monitoring system for critically ill patients in the intensive care unit (ICU). The proposed system informs and assists all stakeholders in real time, whenever abrupt changes occurs in the pre-defined conditions for timely action.…”

Section: A Taxonomy Of Machine Learning Techniques For Big Data Analymentioning

confidence: 99%

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

et al. 2021

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Section: A Taxonomy Of Machine Learning Techniques For Big Data Analymentioning

confidence: 99%

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

et al. 2021

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“…Similarly, an ICU-based real-time IoT surveillance system has been suggested by [5,6] have developed a monitoring system for electrocardiography (ECG), blood pressure, heartbeat, and body temperature. They broadcasted the corresponding information to the health organizations through Global System for Mobiles (GSM), Global Positioning System (GPS), and online mediums in case of unexpected behaviors, allowing for quick intervention to rescue patients.…”

Section: Introductionmentioning

confidence: 99%

A Health Monitoring System Using IoT-Based Android Mobile Application

Alruwaili

2023

CSSE

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Numerous types of research on healthcare monitoring systems have been conducted for calculating heart rate, ECG, nasal/oral airflow, temperature, light sensor, and fall detection sensor. Different researchers have done different work in the field of health monitoring with sensor networks. Different researchers used built-in apps, such as some used a small number of parameters, while some other studies used more than one microcontroller and used senders and receivers among the microcontrollers to communicate, and outdated tools for study development. While no efficient, cheap, and updated work is proposed in the field of sensor-based health monitoring systems. Therefore, this study developed an android-based mobile system that can remotely monitor electrocardiograms (ECGs), pulse oximetry, heart rate, and body temperature. The microcontroller's Wi-Fi device is used to manage wireless data transport. The findings of the patient are saved on the Firebase server for further usage in the mobile app. The performance of the proposed device is tested on ten numbers of different patients age-wise in terms of beats per minute (BPM), ECG, Temperature, and SpO 2 . This system uses temperature, pulse, ECG, blood pressure, and eye blink sensors. This device makes the usage of a tiny pulse sensor that has been designed to provide an accurate and optimal readout of the pulse rate and a temperature sensor is also included. With the help of an MCU, our system measures the pulse rate in beats per minute (bpm), blood oxygen level temperature measurements, and ECG readings and communicates this information to the Firebase server.To check the performance of the proposed system first, the BPM parameter was checked on the cardiac monitor. Then, the proposed model is tested on different patients age-wise. The simulation result shows that the BPM reading is not much different than the BPM of the cardiac monitor. According to the simulation findings, the proposed model achieved the best performance as compared to commercially available devices.

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“…Finally, on the architecture front, Prajapati et al [34] proposed an IoT-based healthcare system that can help with fast communication, identify emergencies, and initiate communication with healthcare staff to initiate proactive and quick treatment. Abdelgawad et al [35] proposed the customized healthcare IoT architecture to collect the sensor data and transmit it to the central cloud for further processing and analysis.…”

Section: Related Work a Clinical Decision Support Systems (Cdss)mentioning

confidence: 99%

Design and Simulation of an Edge Compute Architecture for IoT-Based Clinical Decision Support System

Kumar,

Rajaram

2024

IEEE Access

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Clinical Decision Support Systems (CDSS) have revolutionized healthcare by leveraging modern technologies such as internet of things (IoT), artificial intelligence (AI), predictive analysis, nanomedicine, and virtual & augmented reality. IoT-based CDSS is of interest in particular. In a hospital scenario, a patient's vital signs like heart rate, blood pressure, respiration rate, ECG, EEG etc. are monitored with the use of embedded sensor devices, also called smart medical devices. These devices collect real-time data which is relayed to a compute device where several algorithms are employed to perform computations on said data to arrive at a prognosis e.g. real-time onset of hypotension can be detected by running predictive algorithms on real-time blood pressure data. The computation in IoT-based CDSS is done predominantly on the cloud, wherein the real-time data collected is relayed to a centralized cloud server. However, latency is a major drawback in a cloud-based monitoring system. Increased latency is of greater concern in healthcare applications as the decision-making process is time-sensitive. Edge computing can potentially overcome this drawback, wherein computation is done on edge-network devices rather than the cloud. While edge computing for IoT-based CDSS has been explored in literature, there are gaps in their implementations. A majority of literature dealing with edge computing for IoT-based healthcare only demonstrates a single application and does not address the varying data acquisition rates for different vital signs. Each prognosis or diagnosis requires different subsets of vital signs, and the underlying algorithm uses different sizes of data e.g. detecting arrhythmia requires processing of ECG data which is a time series data, and detecting cardiovascular disease requires blood pressure, cholesterol and certain habits of the patient which are mostly single points of data. This paper explores the use of edge computing in CDSS, quantifies its performance with respect to number of devices, sense time interval or intertransmission rate, and the size of data, and proposes a unified IoT edge gateway architecture to combine multiple patterns of data and computation algorithms to achieve reduced latency and network utilization. Simulation results show that edge computing reduces the latency of decision by approximately 87 times, and the network utilization by 1.5 times. The results show the efficacy of edge computing for implementing IoT-based CDSS and also demonstrate scalability with regard to the number of devices and the size and intertransmission rate of data. INDEX TERMSEdge computing , smart healthcare, vital sign monitoring, Internet of things, Clinical Decision support system. I. INTRODUCTIONC LINICAL Decision Support Systems (CDSS) are used in healthcare to provide stakeholders of the healthcare system including doctors, nursing staff, and patients with information necessary to make efficient decisions [1]. A traditional CDSS is a software system which aids in clinicaldecision-making wherei...

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An Intelligent Real Time IoT Based System (IRTBS) for Monitoring ICU Patient

Cited by 15 publications

References 16 publications

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

A Health Monitoring System Using IoT-Based Android Mobile Application

Design and Simulation of an Edge Compute Architecture for IoT-Based Clinical Decision Support System

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