A smart ontology-based IoT framework for remote patient monitoring

Sharma, Nonita; Mangla, Monika; Mohanty, Sachi Nandan; Gupta, Deepak; Tiwari, Prayag; Shorfuzzaman, Mohammad; Rawashdeh, Majdi

doi:10.1016/j.bspc.2021.102717

Cited by 88 publications

(27 citation statements)

References 31 publications

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“…Compared to existing studies, such as the studies of [12,19,22], reference [12] used an IoT-based health monitoring platform to track heart rate and body temperature, and sent email/SMS warnings when these readings were higher than the main value, and no biomedical fusion analysis was implemented. In reference [19], they extracted the time series data of various biological signals separately, and then used CNN to perform abnormality detections separately.…”

Section: Discussionmentioning

confidence: 99%

“…No fusion analysis of multi-dimensional signals was considered. In reference [22], they used the MDCA method to fuse a variety of biological signals. However, they did not perform pre/post-correlation analysis based on time series in achieving health monitoring.…”

Section: Discussionmentioning

confidence: 99%

“…It could be applied to ICU time series data and combined with static data to achieve high-performance intubation prediction. Sharma et al [22] proposed a model based on IoT for early detection of COVID-19 based on ontology method using sensory 1D biomedical signals such as ECG, PPG, temperature, and accelerometer. This model extracts the characteristics of the four biological signals and uses the MDCA method for fusion, and finally uses SVM and KNN for classification, which realizes the classification and monitoring of abnormal biological signals.…”

Section: Related Workmentioning

confidence: 99%

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Exploring an Efficient Remote Biomedical Signal Monitoring Framework for Personal Health in the COVID-19 Pandemic

Tang

et al. 2021

IJERPH

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Nowadays people are mostly focused on their work while ignoring their health which in turn is creating a drastic effect on their health in the long run. Remote health monitoring through telemedicine can help people discover potential health threats in time. In the COVID-19 pandemic, remote health monitoring can help obtain and analyze biomedical signals including human body temperature without direct body contact. This technique is of great significance to achieve safe and efficient health monitoring in the COVID-19 pandemic. Existing remote biomedical signal monitoring methods cannot effectively analyze the time series data. This paper designs a remote biomedical signal monitoring framework combining the Internet of Things (IoT), 5G communication and artificial intelligence techniques. In the constructed framework, IoT devices are used to collect biomedical signals at the perception layer. Subsequently, the biomedical signals are transmitted through the 5G network to the cloud server where the GRU-AE deep learning model is deployed. It is noteworthy that the proposed GRU-AE model can analyze multi-dimensional biomedical signals in time series. Finally, this paper conducts a 24-week monitoring experiment for 2000 subjects of different ages to obtain real data. Compared with the traditional biomedical signal monitoring method based on the AutoEncoder model, the GRU-AE model has better performance. The research has an important role in promoting the development of biomedical signal monitoring techniques, which can be effectively applied to some kinds of remote health monitoring scenario.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Exploring an Efficient Remote Biomedical Signal Monitoring Framework for Personal Health in the COVID-19 Pandemic

Tang

et al. 2021

IJERPH

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“…However, work on ontology-based insurance policy systems is limited. Sharma et al [11] proposed an ontology-based IoT framework for monitoring COVID-19 patients remotely. The main purpose of this system is to control the spread of a virus by providing information about COVID-19 and suspected infected patients.…”

Section: Medigap Supplemental Plansmentioning

confidence: 99%

A-SHIP: Ontology-Based Adaptive Sustainable Healthcare Insurance Policy

et al. 2022

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Healthcare has a significant impact on human capital anywhere. Countries usually allocate financial resources to manage healthcare, which might impose a substantial financial burden on the scope of healthcare coverage. Thus, the healthcare sector must provide the best possible services at the lowest cost. This significant challenge can only be achieved through applying appropriate policies and technologies, including those used by healthcare insurance policy providers. This paper proposes an innovative, customer-centric, sustainable healthcare insurance policy model. The main objective of this model is to sustain wellness by applying technologies to avoid illness and provide wellbeing for patients by empowering self-care remotely. The proposed solution uses an adaptive ontology-based knowledge management system to satisfy customers and market needs. The proposed system creates a customized policy that consists of various packages to match customers’ healthcare needs based on their health status. The system was tested and validated using a real dataset.

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“…In information and communication technology, an ontology is a conceptual framework that allows for the development of complete and rigorous conceptual frameworks that facilitate the flow of information and communication between diverse systems and organizations [8][9][10]. Internet of things (IoT) sensor data is infrequently used in research that employs ontologies [11][12][13][14] in conjunction with ITS, despite the fact that they are increasingly popular. The ontology can provide a semantic interpretation for IoT sensor data, which will improve driver safety and traffic flow overall.…”

Section: Introductionmentioning

confidence: 99%

Internet of Things-Based Intelligent Ontology Model for Safety Purpose Using Wireless Networks

Shakya

Sundar

Kushwaha

et al. 2022

Wireless Communications and Mobile Computing

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Intelligent transportation systems (ITS) are a newer trend in technology that improve the safety and performance in transportation systems. The exchange of information is considered as one of the key elements for ITS since it involves communication between vehicles and other components of ITS on road. On other hand, the data collections via Internet of Things (IoT) sensors play a major role for data collection and transmission between the vehicles and road segments in ITS. The data collection provides the current traffic and weather conditions that is considered necessary for driving in traffic. However, energy savings is one of the predominant objectives of electric vehicle (EV) while it is connected with ITS. In this research, we propose an ontology-based architecture for EVs using the data collected from IoT sensor network, which is intended to improve the overall driving experience. The system uses IoT sensor data to execute a range of activities in order to ensure the driver safety and comfort while on the road. The simulation is conducted on an Eclipse SUMO simulator, and the performance is reported. The results of simulation shows that the proposed intelligent model on making decisions along with weather and traffic conditions is reported efficient than existing ITS models.

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A smart ontology-based IoT framework for remote patient monitoring

Cited by 88 publications

References 31 publications

Exploring an Efficient Remote Biomedical Signal Monitoring Framework for Personal Health in the COVID-19 Pandemic

Exploring an Efficient Remote Biomedical Signal Monitoring Framework for Personal Health in the COVID-19 Pandemic

A-SHIP: Ontology-Based Adaptive Sustainable Healthcare Insurance Policy

Internet of Things-Based Intelligent Ontology Model for Safety Purpose Using Wireless Networks

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