Fog Computing for Personal Health Principles

Dimitrievski, Ace; Zdravevski, Eftim; Lameski, Petre; Goleva, Rossitza; Koceski, Saso; Trajkovik, Vladimir

doi:10.20544/aiit2018.p23

Cited by 1 publication

(2 citation statements)

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“…The cloud infrastructure can also facilitate scalability with the resource demand and cost optimization and can simplify deployments to other locations [ 31 , 32 ]. However, the module for data collection and basic processing should be implemented on an edge device [ 10 ] and then sent to the cloud. The sensors can have direct wireless internet connectivity and upload information directly to the cloud in a scenario where cost savings are the priority.…”

Section: Methodsmentioning

confidence: 99%

“…Symptoms include cough, fever, dyspnea, musculoskeletal symptoms (joint pain, fatigue), and gastrointestinal symptoms [ 8 ]. Based on our earlier research [ 9 , 10 , 11 ], we propose a method for non-invasive monitoring of sleep disturbances, as developing pneumonia could affect a person’s breathing and quality of sleep. To establish our assumption that at-home patient monitoring, specifically sleep monitoring, could detect the worsening of the situation of COVID-19 patients or establish if they present a higher risk, in this paper, we review the literature on the relations between COVID-19 and sleep, as well as technology-aided patient monitoring.…”

Section: Introductionmentioning

confidence: 99%

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Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks

Dimitrievski

Zdravevski

Lameski

et al. 2021

Sensors

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Pneumonia caused by COVID-19 is a severe health risk that sometimes leads to fatal outcomes. Due to constraints in medical care systems, technological solutions should be applied to diagnose, monitor, and alert about the disease’s progress for patients receiving care at home. Some sleep disturbances, such as obstructive sleep apnea syndrome, can increase the risk for COVID-19 patients. This paper proposes an approach to evaluating patients’ sleep quality with the aim of detecting sleep disturbances caused by pneumonia and other COVID-19-related pathologies. We describe a non-invasive sensor network that is used for sleep monitoring and evaluate the feasibility of an approach for training a machine learning model to detect possible COVID-19-related sleep disturbances. We also discuss a cloud-based approach for the implementation of the proposed system for processing the data streams. Based on the preliminary results, we conclude that sleep disturbances are detectable with affordable and non-invasive sensors.

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Section: Methodsmentioning

confidence: 99%