A Design of Real-Time Multilayered Smart Healthcare Monitoring Framework Using IoT

Jangra, Priyanka; Gupta, Monish

doi:10.1109/icias.2018.8540606

Cited by 31 publications

(9 citation statements)

References 13 publications

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“…Designing an IoMT-based smart network is very complex because of the below-mentioned challenges that influence the designing techniques at every edge [ 88 ]. The routing protocol will govern the exchange of data between routers and gives information, enabling route selection between nodes [ 89 , 90 ]. In a smart healthcare system, we collect very sensitive patient data using small and ultralow power IoMT devices [ 91 , 92 ].…”

Section: Challenges Within a Smart Healthcare System To Be Considered...mentioning

confidence: 99%

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Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Srivastava

Routray

Ahmad

et al. 2022

Computational Intelligence and Neuroscience

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Internet of Medical Thing (IoMT) is the most emerging era of the Internet of Thing (IoT), which is exponentially gaining researchers’ attention with every passing day because of its wide applicability in Smart Healthcare systems (SHS). Because of the current pandemic situation, it is highly risky for an individual to visit the doctor for every small problem. Hence, using IoMT devices, we can easily monitor our day-to-day health records, and thereby initial precautions can be taken on our own. IoMT is playing a crucial role within the healthcare industry to increase the accuracy, reliability, and productivity of electronic devices. This research work provides an overview of IoMT with emphasis on various enabling techniques used in smart healthcare systems (SHS), such as radio frequency identification (RFID), artificial intelligence (AI), and blockchain. We are providing a comparative analysis of various IoMT architectures proposed by several researchers. Also, we have defined various health domains of IoMT, including the analysis of different sensors with their application environment, merits, and demerits. In addition, we have figured out key protocol design challenges, which are to be considered during the implementation of an IoMT network-based smart healthcare system. Considering these challenges, we prepared a comparative study for different data collection techniques that can be used to maintain the accuracy of collected data. In addition, this research work also provides a comprehensive study for maintaining the energy efficiency of an AI-based IoMT framework based on various parameters, such as the amount of energy consumed, packet delivery ratio, battery lifetime, quality of service, power drain, network throughput, delay, and transmission rate. Finally, we have provided different correlation equations for finding the accuracy and efficiency within the IoMT-based healthcare system using artificial intelligence. We have compared different data collection algorithms graphically based on their accuracy and error rate. Similarly, different energy efficiency algorithms are also graphically compared based on their energy consumption and packet loss percentage. We have analyzed our references used in this study, which are graphically represented based on their distribution of publication year and publication avenue.

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Section: Challenges Within a Smart Healthcare System To Be Considered...mentioning

confidence: 99%

“…e routing protocol will govern the exchange of data between routers and gives information, enabling route selection between nodes [89,90]. In a smart healthcare system, we collect very sensitive patient data using small and ultralow power IoMT devices [91,92].…”

Section: Challenges Within a Smart Healthcarementioning

confidence: 99%

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Srivastava

Routray

Ahmad

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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“…The model consisted of three sensors that collected heart rate, temperature, and blood pressure data from the patients, raising alerts whenever it encountered abnormalities. In [5] normal Blood Pressure range was 80-120 mm Hg while the body temperature normal range was 36.5-37.5C and Heart Rate normal range was 60-100 beats/min. The model also used the data collected to analyze and predict chronic disorders using data mining techniques.…”

Section: Related Workmentioning

confidence: 91%

“…Studies focusing on children cases exist, for instance, Jangra and Gupta created a system for monitoring and recording patients data [5]. The model consisted of three sensors that collected heart rate, temperature, and blood pressure data from the patients, raising alerts whenever it encountered abnormalities.…”

Section: Related Workmentioning

confidence: 99%

Untitled

2021

AMS

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Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and categorize emergency cases for precise action. Current systems use manual examination resulting in delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for determining emergency cases, in pediatric section, specifically the triage section. It is later tested using hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform machine learning on the data by training a model and finally develop a Plotly Dash analytical application integrating the model for visualization near real-time.Findings show that emergency cases are detected using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The model indicates a 97% accuracy.In conclusion, children's emergency cases are detected in time using IoT gadgets and machine learning classification.

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“…Creating and managing a WSN requires wearable sensors and different healthcare monitoring systems. Merging all these elements helps us achieve smart healthcare [8,9].…”

Section: Introductionmentioning

confidence: 99%

Recent Advances on IoT-Assisted Wearable Sensor Systems for Healthcare Monitoring

et al. 2021

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IoT has played an essential role in many industries over the last few decades. Recent advancements in the healthcare industry have made it possible to make healthcare accessible to more people and improve their overall health. The next step in healthcare is to integrate it with IoT-assisted wearable sensor systems seamlessly. This review rigorously discusses the various IoT architectures, different methods of data processing, transfer, and computing paradigms. It compiles various communication technologies and the devices commonly used in IoT-assisted wearable sensor systems and deals with its various applications in healthcare and their advantages to the world. A comparative analysis of all the wearable technology in healthcare is also discussed with tabulation of various research and technology. This review also analyses all the problems commonly faced in IoT-assisted wearable sensor systems and the specific issues that need to be tackled to optimize these systems in healthcare and describes the various future implementations that can be made to the architecture and the technology to improve the healthcare industry.

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A Design of Real-Time Multilayered Smart Healthcare Monitoring Framework Using IoT

Cited by 31 publications

References 13 publications

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Untitled

Recent Advances on IoT-Assisted Wearable Sensor Systems for Healthcare Monitoring

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