Smart Health Monitoring System based on IoT and Cloud Computing

Siam, Ali I.; Elazm, Atef Abou; El‐Bahnasawy, Nirmeen A.; Banby, Ghada El; El-Samie, Fathi E. Fathi E. Abd El-Samie Fathi E. Abd

doi:10.21608/mjeer.2019.76711

Cited by 28 publications

(10 citation statements)

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“…To the best of our knowledge, no application using fog computing architecture for remote pain monitoring has been proposed before. To validate the effectiveness of the proposed architecture, the results are compared with the cloud-based systems that use sEMG or ECG signals for pain detection and healthcare service [ 11 , 40 , 60 ]. Execution costs in cloud, latency, and network usage are the parameters that are observed during the comparison.…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, in [ 40 ], a cloud-based system for the remote monitoring of persistent vegetative state (PVS) patients using sEMG sensors was designed. In [ 60 ], a cloud-based health monitoring system was designed to monitor body temperature, oxygen saturation, and heart rate of patients. All these systems engage cloud servers for the processing and storage of biopotential data coming from patients.…”

Section: Resultsmentioning

confidence: 99%

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Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

Hassan

Ahmad

et al. 2020

Sensors

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The integration of medical signal processing capabilities and advanced sensors into Internet of Things (IoT) devices plays a key role in providing comfort and convenience to human lives. As the number of patients is increasing gradually, providing healthcare facilities to each patient, particularly to the patients located in remote regions, not only has become challenging but also results in several issues, such as: (i) increase in workload on paramedics, (ii) wastage of time, and (iii) accommodation of patients. Therefore, the design of smart healthcare systems has become an important area of research to overcome these above-mentioned issues. Several healthcare applications have been designed using wireless sensor networks (WSNs), cloud computing, and fog computing. Most of the e-healthcare applications are designed using the cloud computing paradigm. Cloud-based architecture introduces high latency while processing huge amounts of data, thus restricting the large-scale implementation of latency-sensitive e-healthcare applications. Fog computing architecture offers processing and storage resources near to the edge of the network, thus, designing e-healthcare applications using the fog computing paradigm is of interest to meet the low latency requirement of such applications. Patients that are minors or are in intensive care units (ICUs) are unable to self-report their pain conditions. The remote healthcare monitoring applications deploy IoT devices with bio-sensors capable of sensing surface electromyogram (sEMG) and electrocardiogram (ECG) signals to monitor the pain condition of such patients. In this article, fog computing architecture is proposed for deploying a remote pain monitoring system. The key motivation for adopting the fog paradigm in our proposed approach is to reduce latency and network consumption. To validate the effectiveness of the proposed approach in minimizing delay and network utilization, simulations were carried out in iFogSim and the results were compared with the cloud-based systems. The results of the simulations carried out in this research indicate that a reduction in both latency and network consumption can be achieved by adopting the proposed approach for implementing a remote pain monitoring system.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

Hassan

Ahmad

et al. 2020

Sensors

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“…An open-source heart-rate and SpO2 algorithm is included in a free firmware library provided with the sensor to calculate the amount of oxygen saturation and pulse rate. MAXREFDES117 implements the heart-rate/SpO2 sensor (MAX30102) commonly used for studies about human health (Telfer et al, 2017;Siam et al, 2019), an efficient low-power step-down converter (MAX1921), and an accurate level translator (MAX14595). The sensor typically operates at less than 5.5 mW, when used with its firmware, and its acquisition rate ranges between 25 and 100 samples per second depending on the embedded platform used.…”

Section: Methodsmentioning

confidence: 99%

Measuring oxygen saturation and pulse rate in dairy cows before and after machine milking using a low-cost pulse oximeter

Calcante

Tangorra²

2021

J Agricult Engineer

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The present study was aimed to measure the haemoglobin oxygen saturation and the pulse rate at teat level on dairy cows after and before milking, using a low cost pulse oximeter developed especially. The pulse oximeter has been tested during a three days of field test involving 18 Holstein Friesian cows raised in a commercial farm located in Northern Italy. The results highlighted that there is a significant difference both in haemoglobin oxygen saturation (SpO2) and pulse rate before and after milking considering the entire sample of animals. By dividing the sample according to the milking time (fast < 8 min and slow > 8 min), a significant difference between fast and slow cows has been observed for SpO2, whilst no difference has been noted considering the lactation stage (< 70 DIM and 71-140 DIM). About the pulse rate, on the contrary, milking time and lactation stage were not significantly different. This confirms that machine milking can create stress to the teat evoking circulatory impairment of its tissue and that pulse oximetry could be useful for detecting machine milking-induced alterations of teats. In perspective, the pulse oximeter could be used as a part of a monitoring system of the milking machine, enabling to change its operating parameters in order to minimize the mechanical stress on the teats.

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“…All of these data were collected and used to train a prototype machine learning model to predict what type of disease a patient might have. The Internet of things (IoT) system has enabled the development of an intelligent structure that enables physicians to monitor a patient’s health status in real time [ 13 , 14 , 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

New Method to Implement and Analysis of Medical System in Real Time

et al. 2022

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The use of information technology and technological medical devices has contributed significantly to the transformation of healthcare. Despite that, many problems have arisen in diagnosing or predicting diseases, either as a result of human errors or lack of accuracy of measurements. Therefore, this paper aims to provide an integrated health monitoring system to measure vital parameters and diagnose or predict disease. Through this work, the percentage of various gases in the blood through breathing is determined, vital parameters are measured and their effect on feelings is analyzed. A supervised learning model is configured to predict and diagnose based on biometric measurements. All results were compared with the results of the Omron device as a reference device. The results proved that the proposed design overcame many problems as it contributed to expanding the database of vital parameters and providing analysis on the effect of emotions on vital indicators. The accuracy of the measurements also reached 98.8% and the accuracy of diagnosing COVID-19 was 64%. The work also presents a user interface model for clinicians as well as for smartphones using the Internet of things.

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Smart Health Monitoring System based on IoT and Cloud Computing

Cited by 28 publications

References 0 publications

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

Measuring oxygen saturation and pulse rate in dairy cows before and after machine milking using a low-cost pulse oximeter

New Method to Implement and Analysis of Medical System in Real Time

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