A Novel Framework of Health Monitoring Systems

Gupta, Sonam; Goel, L.R.; Agarwal, Abhay Kumar

doi:10.4018/ijbdah.20210101.oa1

Cited by 6 publications

(5 citation statements)

References 3 publications

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“…An IoT-based health-monitoring system with temperature and pulse rate sensors was designed, and its deployment was explored [7]. With the help of this technology, clinicians can attain timely information on the health of their patients and respond quickly to anomalies [8].…”

Section: Review Of the Literaturementioning

confidence: 99%

Implementation of an Advanced Health-Monitoring System Capable of Real-Time Analysis and Alerting

Prayesi,

Azami,

Kushwah

et al. 2024

Cc 2023

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Section: Review Of the Literaturementioning

confidence: 99%

Implementation of an Advanced Health-Monitoring System Capable of Real-Time Analysis and Alerting

Prayesi,

Azami,

Kushwah

et al. 2024

Cc 2023

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show abstract

“…Sensor components and a wireless module can be used to create the monitoring system, which should be protected so that the message carrying the health information is not distorted. A patient monitoring system based on IoT is proposed in [22,23]. The Internet of Things is more than just a link between devices with embedded technology and the Internet's infrastructure.…”

Section: Related Workmentioning

confidence: 99%

Analyzing IoT big data in healthcare using Deep learning and Distributed Fog Computing

Iranpak

Shahbahrami

Shakeri

2022

Preprint

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High amounts of patient and healthcare data are generated daily on the Internet of Things (IoT). The processing time and analyzing the big data received from IoT devices, as well as providing the necessary accuracy for data classification, are considered as the most important challenges in IoT. Therefore, this study aims to improve the accuracy of data classification by implementing hybrid approaches of feature selection (FS) and deep learning (DL) within the fog computing infrastructure while reducing the response time (RT) and bandwidth usage. Also, the major goal is to analyze and classify the states of patients remotely through IoT, cloud computing and fog computing technologies. The information processing time is reduced by implementing Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA) in fog computing to select the prominent features, and the classification accuracy is improved through classifying new data by a deep neural network (DNN) model. The simulation results show that the accuracy of the classification and remote monitoring of patients is 98.54%, which is about 4.5% better than the case without using PSO-ICA algorithms, and it is also improved by about 10% on average compared to other methods such as Linear Regression (LR), K Nearest Neighbors (KNN), Neural Network (NN), and Bayesian Belief Net (BBN).

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“…The advantage of digital storage is that it is easy to recover and performs faster in case of an emergency. [19] Examines the IoT-based patient monitoring system. An IoT-based patient monitoring system is an alternative used to help patients with chronic diseases.…”

Section: Related Workmentioning

confidence: 99%

Remote patient monitoring and classifying using the internet of things platform combined with cloud computing

2021

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Many researchers have recently considered patients’ health and provided an optimal and appropriate solution. With the advent of technologies such as cloud computing, Internet of Things and 5G, information can be exchanged faster and more securely. The Internet of things (IoT) offers many opportunities in the field of e-health. This technology can improve health services and lead to various innovations in this regard. Using cloud computing and IoT in this process can significantly improve the monitoring of patients. Therefore, it is important to provide a useful method in the medical industry and computer science to monitor the status of patients using connected sensors. Thus, due to its optimal efficiency, speed, and accuracy of data processing and classification, the use of cloud computing to process the data collected from remote patient sensors and IoT platform has been suggested. In this paper, a prioritization system is used to prioritize sensitive information in IoT, and in cloud computing, LSTM deep neural network is applied to classify and monitor patients’ condition remotely, which can be considered as an important innovative aspect of this paper. Sensor data in the IoT platform is sent to the cloud with the help of the 5th generation Internet. The core of cloud computing uses the LSTM (long short-term memory) deep neural network algorithm. By simulating the proposed method and comparing the obtained results with other methods, it is observed that the accuracy of the proposed method is 97.13%, which has been improved by 10.41% in average over the other methods.

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A Novel Framework of Health Monitoring Systems

Cited by 6 publications

References 3 publications

Implementation of an Advanced Health-Monitoring System Capable of Real-Time Analysis and Alerting

Implementation of an Advanced Health-Monitoring System Capable of Real-Time Analysis and Alerting

Analyzing IoT big data in healthcare using Deep learning and Distributed Fog Computing

Remote patient monitoring and classifying using the internet of things platform combined with cloud computing

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