IoT-Based Smart Health Monitoring System for COVID-19

Bhardwaj, Vaneeta; Joshi, Rajat; Gaur, Anshu

doi:10.1007/s42979-022-01015-1

Cited by 109 publications

(46 citation statements)

References 36 publications

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“…The main disadvantage of the system developed by [11], is that authors use a local server instead of the cloud, which is not recommended in an IoT system (storage and processing constraints). In a recent paper by [4], authors merge the application layer with the processing layer by exploiting the dashboard (data visualization) offered by the Thing Speak Cloud platform. The first problem is that the system does not respect the general architecture of the IoT.…”

Section: Resultsmentioning

confidence: 99%

Adopting the Internet of Things Technology to Remotely Monitor COVID-19 Patients

Saidi

Kacem

Tounsi

et al. 2022

Lecture Notes in Computer Science

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The coronavirus known as COVID-19 is the topic of the hour all over the world. This virus has invaded the world with its invariants, which are characterized by their rapid spread. COVID-19 has impacted the health of people and the economy of countries. For that, laboratories, researchers, and doctors are in a race against time to find a cure for this pandemic. To combat this virus, cutting-edge technologies such as artificial intelligence, cloud computing, and big data have been put in place. In our work, we use Internet of Things (IoT) technology. The use of IoT in an efficient way can lead to detecting infected people and avoiding being contaminated. In this paper, we are interested in the remote medical monitoring of patients who have tested positive for COVID-19. We propose a meta-modeling technique to model the IoT architecture. Then we implement two IoT solutions that permit the remote medical monitoring of patients infected with COVID-19 and the respect of social distancing by instantiating correct models that conform to the proposed meta-model in order to mitigate the COVID-19 outbreak.

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Section: Resultsmentioning

confidence: 99%

Adopting the Internet of Things Technology to Remotely Monitor COVID-19 Patients

Saidi

Kacem

Tounsi

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…However prediction accuracy was not enhanced. A smart health monitoring system was designed in [22] with Internet of Things (IoT) technology for monitoring blood pressure, heart rate, oxygen level, and temperature of a person. It was mainly employed for rural areas or villages where nearby clinics can be in touch with city hospitals about their patient health conditions.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Deep Learning for IoT Based COVID 19 Health Care Pollution Monitor

Sivakumar¹

2023

Intelligent Automation &Amp; Soft Computing

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Internet of things (IoT) has brought a greater transformation in healthcare sector thereby improving patient care, minimizing treatment costs. The present method employs classical mechanisms for extracting features and a regression model for prediction. These methods have failed to consider the pollution aspects involved during COVID 19 prediction. Utilizing Ensemble Deep Learning and Framingham Feature Extraction (FFE) techniques, a smart healthcare system is introduced for COVID-19 pandemic disease diagnosis. The Collected feature or data via predictive mechanisms to form pollution maps. Those maps are used to implement real-time countermeasures, such as storing the extracted data or feature in a Cloud server to minimize concentrations of air pollutants. Once integrated with patient management systems, this solution would minimize pollution emitted via patient's sensors by offering spaces in the cloud server when pollution thresholds are reached. Second, the Gini Index factor information gain technique eliminates unimportant and redundant attributes while selecting the most relevant, reducing computing overhead and optimizing system performance. Finally, the COVID-19 disease prognosis ensemble deep learningbased classifier is constructed. Experimental analysis is planned to measure the prediction accuracy, error, precision and recall for different numbers of patients. Experimental results show that prediction accuracy is improved by 8%, error rate was reduced by 47% and prediction time is minimized by 36% compared to existing methods.

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“…A smart environment has augmented capabilities, being equipped with interconnected wearable or non-wearable devices integrated in its infrastructure with the role of observing and monitoring the everyday life of its inhabitant and providing proactive services aimed at improving their quality of life. The devices and sensors from a smart environment are designed to detect and monitor biomedical parameters (e.g., heart rate, oxygen saturation, blood glucose) [ 19 ], mobility and movement [ 20 ], daily activities [ 21 ], urinary-related infection problems [ 22 ], falling detection [ 23 ], and so on.…”

Section: Related Workmentioning

confidence: 99%

Addressing Mild Cognitive Impairment and Boosting Wellness for the Elderly through Personalized Remote Monitoring

2022

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Mild cognitive impairment (MCI) may occur with old age and is associated with increased cognitive deterioration compared to what is normal. This may affect the person’s quality of life, health, and independence. In this ageing worldwide context, early diagnosis and personalized assistance for MCI therefore become crucial. This paper makes two important contributions: (1) a system (RO-SmartAgeing) to address MCI, which was developed for Romania; and (2) a set of criteria for evaluating its impact on remote health monitoring. The system aims to provide customized non-invasive remote monitoring, health assessment, and assistance for the elderly within a smart environment set up in their homes. Moreover, it includes multivariate AI-based predictive models that can detect the onset of MCI and its development towards dementia. It was built iteratively, following literature reviews and consultations with health specialists, and it is currently being tested in a simulated home environment. While its main strength is the potential to detect MCI early and follow its evolution, RO-SmartAgeing also supports elderly people in living independently, and it is safe, comfortable, low cost, and privacy protected. Moreover, it can be used by healthcare institutions to continuously monitor a patient’s vital signs, position, and activities, and to deliver reminders and alarms.

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IoT-Based Smart Health Monitoring System for COVID-19

Cited by 109 publications

References 36 publications

Adopting the Internet of Things Technology to Remotely Monitor COVID-19 Patients

Adopting the Internet of Things Technology to Remotely Monitor COVID-19 Patients

Ensemble Deep Learning for IoT Based COVID 19 Health Care Pollution Monitor

Addressing Mild Cognitive Impairment and Boosting Wellness for the Elderly through Personalized Remote Monitoring

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