ArticleComputers in industry2017
Wearable IoT sensor based healthcare system for identifying and controlling chikungunya virus.
Article in Computers in industry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
25 citing papers in PubMed, 200 citations in OpenAlex.
- Article
- Development and field validation of a reverse transcription loop-mediated isothermal amplification assay (RT-LAMP) for the rapid detection of chikungunya virus in patient and mosquito samples.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2024Article
- Heart failure patients monitoring using IoT-based remote monitoring system.Scientific reports · 2023Article
- Remote Health Monitoring Systems for Elderly People: A Survey.Sensors (Basel, Switzerland) · 2023Review
- Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System.Sensors (Basel, Switzerland) · 2023Article
- Intelligent Healthcare: Integration of Emerging Technologies and Internet of Things for Humanity.Sensors (Basel, Switzerland) · 2023Review
- The application of wearable smart sensors for monitoring the vital signs of patients in epidemics: a systematic literature review.Journal of ambient intelligence and humanized computing · 2023Article
- Real-Time Remote Patient Monitoring and Alarming System for Noncommunicable Lifestyle Diseases.International journal of telemedicine and applications · 2023Article
- Role of artificial intelligence-internet of things (AI-IoT) based emerging technologies in the public health response to infectious diseases in Bangladesh.Parasite epidemiology and control · 2022Article
- Autonomous service for managing real time notification in detection of COVID-19 virus.Computers & electrical engineering : an international journal · 2022Article
- Artificial intelligence-inspired comprehensive framework for Covid-19 outbreak control.Artificial intelligence in medicine · 2022Article
- Artificial Intelligence-Based Cyber-Physical System for Severity Classification of Chikungunya Disease.IEEE journal of translational engineering in health and medicine · 2022Article
- A novel IoT-fog-cloud-based healthcare system for monitoring and predicting COVID-19 outspread.The Journal of supercomputing · 2022Article
- An Overview of Fog Computing and Edge Computing Security and Privacy Issues.Sensors (Basel, Switzerland) · 2021Article
- How artificial intelligence may help the Covid-19 pandemic: Pitfalls and lessons for the future.Reviews in medical virology · 2021Review
- A remote management system for control and surveillance of echinococcosis: design and implementation based on internet of things.Infectious diseases of poverty · 2021Article
- The use of mobile phones for the prevention and control of arboviral diseases: a scoping review.BMC public health · 2021Article
- IoT Platform for COVID-19 Prevention and Control: A Survey.IEEE access : practical innovations, open solutions · 2021Article
- Deep-LSTM ensemble framework to forecast Covid-19: an insight to the global pandemic.International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management · 2021Article
- A re-organizing biosurveillance framework based on fog and mobile edge computing.Multimedia tools and applications · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chikungunya is a vector borne disease that spreads quickly in geographically affected areas. Its outbreak results in acute illness that may lead to chronic phase. Chikungunya virus (CHV) diagnosis solutions are not easily accessible and affordable in developing countries. Also old approaches are very slow in identifying and controlling the spread of CHV outbreak. The sudden development and advancement of wearable internet of things (IoT) sensors, fog computing, mobile technology, cloud computing and better internet coverage have enhanced the quality of remote healthcare services. IoT assisted fog health monitoring system can be used to identify possibly infected users from CHV in an early phase of their illness so that the outbreak of CHV can be controlled. Fog computing provides many benefits such as low latency, minimum response time, high mobility, enhanced service quality, location awareness and notification service itself at the edge of the network. In this paper, IoT and fog based healthcare system is proposed to identify and control the outbreak of CHV. Fuzzy-C means (FCM) is used to diagnose the possibly infected users and immediately generate diagnostic and emergency alerts to users from fog layer. Furthermore on cloud server, social network analysis (SNA) is used to represent the state of CHV outbreak. Outbreak role index is calculated from SNA graph which represents the probability of any user to receive or spread the infection. It also generates warning alerts to government and healthcare agencies to control the outbreak of CHV in risk prone or infected regions. The experimental results highlight the advantages of using both fog computing and cloud computing services together for achieving network bandwidth efficiency, high quality of service and minimum response time in generation of real time notification as compared to a cloud only model.
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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.