ArticleInternational journal of environmental research and public health2021
An IoT Framework for Screening of COVID-19 Using Real-Time Data from Wearable Sensors.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 78 citations in OpenAlex.
- Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review.JMIR mHealth and uHealth · 2024Pooled it
- The role of blockchain to secure internet of medical things.Scientific reports · 2024Article
- Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity.Sensors (Basel, Switzerland) · 2023Article
- COVID-19 symptom identification using Deep Learning and hardware emulated systems.Engineering applications of artificial intelligence · 2023Article
- Chronic cough-the limitation and advances in assessment techniques.Journal of thoracic disease · 2022Review
- Smart Hand Sanitisers in the Workplace: A Survey of Attitudes towards an Internet of Things Technology.International journal of environmental research and public health · 2022Article
- Blockchain for Modern Applications: A Survey.Sensors (Basel, Switzerland) · 2022Review
- A physiological signal compression approach using optimized Spindle Convolutional Auto-encoder in mHealth applications.Biomedical signal processing and control · 2022Article
- Internet of Things Concept in the Context of the COVID-19 Pandemic: A Multi-Sensor Application Design.Sensors (Basel, Switzerland) · 2022Article
- Multichannel DenseNet Architecture for Classification of Mammographic Breast Density for Breast Cancer Detection.Frontiers in public health · 2022Article
- Advances in the Rapid Diagnostic of Viral Respiratory Tract Infections.Frontiers in cellular and infection microbiology · 2022Review
- Implementation of Combined Machine Learning with the Big Data Model in IoMT Systems for the Prediction of Network Resource Consumption and Improving the Data Delivery.Computational intelligence and neuroscience · 2022Article
- Ten public health strategies to control the Covid-19 pandemic: the Saudi Experience.IJID regions · 2021Review
- An Artificial Intelligence Approach for Expurgating Edible and Non-Edible Items.Frontiers in public health · 2021Article
- Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review.Journal of oral biology and craniofacial researchArticle
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Experts have predicted that COVID-19 may prevail for many months or even years before it can be completely eliminated. A major problem in its cure is its early screening and detection, which will decide on its treatment. Due to the fast contactless spreading of the virus, its screening is unusually difficult. Moreover, the results of COVID-19 tests may take up to 48 h. That is enough time for the virus to worsen the health of the affected person. The health community needs effective means for identification of the virus in the shortest possible time. In this study, we invent a medical device utilized consisting of composable sensors to monitor remotely and in real-time the health status of those who have symptoms of the coronavirus or those infected with it. The device comprises wearable medical sensors integrated using the Arduino hardware interfacing and a smartphone application. An IoT framework is deployed at the backend through which various devices can communicate in real-time. The medical device is applied to determine the patient's critical status of the effects of the coronavirus or its symptoms using heartbeat, cough, temperature and Oxygen concentration (SpO2) that are evaluated using our custom algorithm. Until now, it has been found that many coronavirus patients remain asymptomatic, but in case of known symptoms, a person can be quickly identified with our device. It also allows doctors to examine their patients without the need for physical direct contact with them to reduce the possibility of infection. Our solution uses rule-based decision-making based on the physiological data of a person obtained through sensors. These rules allow to classify a person as healthy or having a possibility of infection by the coronavirus. The advantage of using rules for patient's classification is that the rules can be updated as new findings emerge from time to time. In this article, we explain the details of the sensors, the smartphone application, and the associated IoT framework for real-time, remote screening of COVID-19.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.