ArticleBiomedical signal processing and control2020
An IoT-based framework for early identification and monitoring of COVID-19 cases.
Article in Biomedical signal processing and control, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 3 of them syntheses that pooled it.
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Who cites it
59 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Smart city healthcare delivery innovations: a systematic review of essential technologies and indicators for developing nations.BMC health services research · 2023Pooled it
- A systematic review and knowledge mapping on ICT-based remote and automatic COVID-19 patient monitoring and care.BMC health services research · 2023Pooled it
- A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis.Frontiers in public health · 2022Pooled it
- Pediatrics 4.0: the Transformative Impacts of the Latest Industrial Revolution on Pediatrics.Health care analysis : HCA : journal of health philosophy and policy · 2026Article
- I-Brainer: Artificial intelligence/Internet of Things (AI/IoT)-Powered Detection of Brain CancerCurrent medical imaging · 2025Article
- A Systematic Review of Internet-Based Remote Patient Monitoring Systems for Chronic Disease Management in Asian.Patient preference and adherence · 2025Review
- Article
- A tree-based explainable AI model for early detection of Covid-19 using physiological data.BMC medical informatics and decision making · 2024Article
- IoT-based COVID-19 detection using recalling-enhanced recurrent neural network optimized with golden eagle optimization algorithm.Medical & biological engineering & computing · 2024Article
- Greenway of Digital Health Technology During COVID-19 Crisis: Bibliometric Analysis, Challenges, and Future Perspective.Advances in experimental medicine and biology · 2024Review
- Modern technologies and solutions to enhance surveillance and response systems for emerging zoonotic diseases.Science in One Health · 2024Review
- Article
- Wearable Devices and Explainable Unsupervised Learning for COVID-19 Detection and Monitoring.Diagnostics (Basel, Switzerland) · 2023Article
- Recent advances in wearable sensors and data analytics for continuous monitoring and analysis of biomarkers and symptoms related to COVID-19.Biophysics reviews · 2023Review
- Fog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring.Medical & biological engineering & computing · 2023Article
- Towards an ML-based semantic IoT for pandemic management: A survey of enabling technologies for COVID-19.Neurocomputing · 2023Article
- COVICT: an IoT based architecture for COVID-19 detection and contact tracing.Journal of ambient intelligence and humanized computing · 2023Article
- Computer-aided methods for combating Covid-19 in prevention, detection, and service provision approaches.Neural computing & applications · 2023Review
- Designing humanitarian logistics network for managing epidemic outbreaks in disasters using Internet-of-Things. A case study: An earthquake in Salas-e-Babajani city.Computers & industrial engineering · 2023Article
- Survival study on deep learning techniques for IoT enabled smart healthcare system.Health and technology · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The world has been facing the challenge of COVID-19 since the end of 2019. It is expected that the world will need to battle the COVID-19 pandemic with precautious measures, until an effective vaccine is developed. This paper proposes a real-time COVID-19 detection and monitoring system. The proposed system would employ an Internet of Things (IoTs) framework to collect real-time symptom data from users to early identify suspected coronaviruses cases, to monitor the treatment response of those who have already recovered from the virus, and to understand the nature of the virus by collecting and analyzing relevant data. The framework consists of five main components: Symptom Data Collection and Uploading (using wearable sensors), Quarantine/Isolation Center, Data Analysis Center (that uses machine learning algorithms), Health Physicians, and Cloud Infrastructure. To quickly identify potential coronaviruses cases from this real-time symptom data, this work proposes eight machine learning algorithms, namely Support Vector Machine (SVM), Neural Network, Naïve Bayes, K-Nearest Neighbor (K-NN), Decision Table, Decision Stump, OneR, and ZeroR. An experiment was conducted to test these eight algorithms on a real COVID-19 symptom dataset, after selecting the relevant symptoms. The results show that five of these eight algorithms achieved an accuracy of more than 90 %. Based on these results we believe that real-time symptom data would allow these five algorithms to provide effective and accurate identification of potential cases of COVID-19, and the framework would then document the treatment response for each patient who has contracted the virus.
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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.