Health monitoring using Internet of Things (IoT)

Saha, Himadri Nath; Auddy, Supratim; Pal, Subrata; Kumar, Shubham; Pandey, Sujata; Singh, Rocky; Singh, Amrendra K.; Sharan, Priyanshu; Ghosh, Debmalya; Saha, Sanhita

doi:10.1109/iemecon.2017.8079564

Cited by 56 publications

(10 citation statements)

References 12 publications

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“…Saha et al. [14] propose a system for monitoring the patient's health using IoT and cloud computing. The patient's pulse, heart rate, blood pressure, and temperature are selected as the main health indicators.…”

Section: Related Workmentioning

confidence: 99%

Internet of Things‐based healthcare system on patient demographic data in Health 4.0

Abdullayeva

2022

CAAI Trans on Intel Tech

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Recently, the spread of COVID‐19 virus infection and the increase of people number with chronic diseases have attracted great attention all over the world. The detection and control of such diseases based on patient demographic data are considered to be a major problem. The key issue in the solution to these problems is the development of methods and algorithms to forecast wellness and categorise patients according to their healthy and unhealthy states. In this paper, a comprehensive analysis of machine learning approaches in the field of diagnosing COVID‐19 has been conducted, and for the detection of chronic diseases in patients, to identify symptoms of COVID‐19 virus infection in advance, and control the situation a healthcare system has been proposed. The constructed system provides real‐time monitoring of chronic diseases and COVID‐19 virus infection in patients. The proposed system consists of five layers: IoT sensor layer, Data transmission layer, Fog layer, Cloud layer, the Application layer. The system architecture in the Fog layer uses machine learning and deep learning algorithms to diagnose patients' diseases, to generate and send diagnostic and emergency alerts to users. The classification module of the system's Fog layer categorises the patient's health status into healthy and unhealthy classes. In this module, to classify medical data the Decision Tree, Random Forest, SVM, Gradient Boosting, Logistic Regression algorithms are used. The COVID‐19 dataset is used to test the effectiveness of the methods. The best results from the comparative analysis of the methods are obtained from the Decision Tree, Random Forest, and Gradient Boosting algorithms, which are recognised data points with high accuracy and on the accuracy metric reached 1.0, 0.99, 1.0 values, respectively. The classification of the other two SVM and Logistic Regression algorithms provided the worst results, and the accuracy score of both classifiers obtained a 0.89 value.

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Section: Related Workmentioning

confidence: 99%

Internet of Things‐based healthcare system on patient demographic data in Health 4.0

Abdullayeva

2022

CAAI Trans on Intel Tech

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“…The use of artificial intelligence (AI) in the process of predicting the evolution of any viruses through genetic analysis and analysis of interactions between viruses with each other or with the surrounding environment. Application of fuzzy models in studying the behaviour of viruses and to anticipate what might happen in future (Saha et al, 2017).…”

Section: Artificial Intelligence (Ai) and Future Visionmentioning

confidence: 99%

A Vision to Face Covid-19 pandemic and Future Risks Through Artificial Intelligence

2022

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Coved-19 pandemic is spreading fear among the world in several aspects such as health, economic, international relations, political stability, and social stability. It emerged suddenly and attacked the world in a short period without warning. Details about the virus such as the source, symptoms, transmission, diagnosis and treatment are still incomplete. Subsequently, more than one million people have died and huge economic losses. In order to avoid this issue in future, this paper aims to focus on artificial intelligence in predicting and tracking viral pandemic Disease and to control similar future risks using artificial intelligence, algorithms and cognitive fission theory.

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“…Maintaining a close eye on victims while also allowing for secure contact is critical to their well-being. Saha et al [ 35 ] claims to have created IoT based health monitoring system Hassanalieragh et al [ 17 ]. To make use of the sensors, a sensor network that was interconnected with the internet was created.…”

Section: Related Workmentioning

confidence: 99%

Safeguards and weightless of electronic chain of command consolidated for virtual patient evaluation

Ahmed

Kannan²

2022

Multimed Tools Appl

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The Internet of Things (IoT), 5G cellular technology, and Cyber-Physical Systems (CPS) are enabling a wide range of IoT-based application cases that are both intelligent. As one of the most impactful applications of the Internet of Things (IoT), healthcare makes use of AAL (Ambient Assisted Living), mobile health (mHealth), and electronic health (eHealth). Spending on health is a significant portion of people’s income. Traditional medicine is prone to long delays, waste of money and effort, and even death. RVO (Remote Victim Observation) can be utilized to circumvent problems associated with traditional healthcare facilities because of IoT’s intelligence and predictive power. With the help of IoT-based RVO and wearable devices, sensor networks, and other digital infrastructure, we can detect oncoming situations before they become life-threatening or even fatal. IoT integration with healthcare units was demonstrated in order to build a trustworthy, available, and secure RVO system. Secure end to end communication, encryption of RFID data, and privacy protection are all part of the proposed solution. An android wearable watch (Biosensor | Body sensor), a server using REST framework, and a smartphone app to monitor and detect falls, blood pressure, and heart rate are all part of the system. As a bonus, the peace and quiet of this secluded location contribute to the attraction. Using this RVO could improve health care and quality of life, according to an empirical investigation.

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Health monitoring using Internet of Things (IoT)

Cited by 56 publications

References 12 publications

Internet of Things‐based healthcare system on patient demographic data in Health 4.0

Internet of Things‐based healthcare system on patient demographic data in Health 4.0

A Vision to Face Covid-19 pandemic and Future Risks Through Artificial Intelligence

Safeguards and weightless of electronic chain of command consolidated for virtual patient evaluation

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