A Context-Aware Interactive Health Care System Based on Ontology and Fuzzy Inference

Chiang, Tzu-Chiang; Liang, Wenhua

doi:10.1007/s10916-015-0287-2

Cited by 23 publications

(11 citation statements)

References 15 publications

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“…Such type of shortened and concise data helps the medical director to know about the patient without any risk and what type of vaccine or medicine should be developed for certain diseases. Eventually, the data stored and updated records are sending to the fog data service 23 Data redundancy …”

Section: Proposed Systemmentioning

confidence: 99%

“…In technological terms, the IoT refers to an air global neural network that communicates and interacts with environments, numerous machines, and objects to generate a huge amount of data. Thus, the generated data employ processing and controlling the things thereby portraying the life of humans more conveniently 23 . In the past few years, the problem based on global aging has created a troublesome errand and, in some cases, the elderly are subjected to high risk and even death.…”

Section: Introductionmentioning

confidence: 99%

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IOT and context‐aware learning‐based optimal neural network model for real‐time health monitoring

Kavitha

Ravikumar²

2020

Trans Emerging Tel Tech

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Recently, numerous context‐aware approaches are established to provide physiological information regarding the wellness and healthcare of each individual. While monitoring the health condition of the patient there occur delays in transferring data to the cloud. So to overcome such types of delays, numerous IoT sensors are developed to monitor, track, and sense the activities of the elder persons. This paper proposes four‐module architecture comprises of IoT module (IoT‐M), data pre‐processing module (DP‐M), context‐aware module (CA‐M) as well as decision‐making module (DM‐M) for storing and processing numerous cumulative sensor data. Here, an IoT comprises a concrete or substantial hardware ecological unit, that is, sensors and actuators. On the other hand, the context‐aware computational approach comprises of insubstantial software ecological unit in understanding and processing the context directly into an accomplishment through various IoT devices. The initial module or the IoT‐M comprises of sensors whereas in the DP‐M phase includes data collection phase, data storage phase, and data redundancy phase. The third phase or the CA‐M comprises two different types of layers namely fog layer, cloud layer. In addition to this, a context‐aware learning phase is also enumerated. In the final phase or the DM‐P phase, the feature extraction and classification is done by Back‐Propagation Neural Network along with the Adaptive grasshopper optimization algorithm so as to obtain a best optimal solution. Thus, an alarm or a notification is sent to the medical practitioner regarding the health condition of the patient with very less response time, high accuracy, and a high scalability rate. The evaluation results and discussions are made by comparing our proposed approach with several other approaches and the evaluation results reveal that the proposed framework provides better results with high accuracy, scalability, network latency, and low response time.

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Section: Proposed Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

IOT and context‐aware learning‐based optimal neural network model for real‐time health monitoring

Kavitha

Ravikumar²

2020

Trans Emerging Tel Tech

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“…In recent years, with the advance of ontology, related studies have revealed that ontologybased knowledge services have been developed in different areas, including personalized medicine [19], e-government [20], medicine [21,22], smart homes [23], the digital library [24,25], and so on.…”

Section: Related Workmentioning

confidence: 99%

The Impact on Citation Analysis Based on Ontology and Linked Data

Xiao¹,

Shi²,

Wang³

2018

Scientometrics

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Research focus: The aim of this chapter is to introduce a new citation analysis and service framework based on the semantic web technologies (e.g., ontology and linked data). Research methods: This research project is based on a review of relevant literature and a series of experimental results based on ontology and linked data. Motivation: Traditional citation analysis methods and tools are overly dependent on citation databases, and traditional citation information service may ignore the semantics of knowledge resources and lack ability to store and query data in a machine-readable mode. Findings: The findings underline that the new citation analysis and service system framework based on ontology and linked data are feasible, which can integrate information requirements and knowledge services, and provide users with more personalized and comprehensive services.

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“…The home is not only a single unit but also a component of a neighborhood under centralized management. In [ 29 ], an interactive health care system is proposed, aiming at enabling interaction between persons under care and incorporating the system in various living spaces, as based on motion-sensing interactions. Accordingly, flexible interactive services should be provided by different service providers.…”

Section: Related Workmentioning

confidence: 99%

A Semantic Approach with Decision Support for Safety Service in Smart Home Management

Huang

Zhu

et al. 2016

Sensors

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Research on smart homes (SHs) has increased significantly in recent years because of the convenience provided by having an assisted living environment. The functions of SHs as mentioned in previous studies, particularly safety services, are seldom discussed or mentioned. Thus, this study proposes a semantic approach with decision support for safety service in SH management. The focus of this contribution is to explore a context awareness and reasoning approach for risk recognition in SH that enables the proper decision support for flexible safety service provision. The framework of SH based on a wireless sensor network is described from the perspective of neighbourhood management. This approach is based on the integration of semantic knowledge in which a reasoner can make decisions about risk recognition and safety service. We present a management ontology for a SH and relevant monitoring contextual information, which considers its suitability in a pervasive computing environment and is service-oriented. We also propose a rule-based reasoning method to provide decision support through reasoning techniques and context-awareness. A system prototype is developed to evaluate the feasibility, time response and extendibility of the approach. The evaluation of our approach shows that it is more effective in daily risk event recognition. The decisions for service provision are shown to be accurate.

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A Context-Aware Interactive Health Care System Based on Ontology and Fuzzy Inference

Cited by 23 publications

References 15 publications

IOT and context‐aware learning‐based optimal neural network model for real‐time health monitoring

IOT and context‐aware learning‐based optimal neural network model for real‐time health monitoring

The Impact on Citation Analysis Based on Ontology and Linked Data

A Semantic Approach with Decision Support for Safety Service in Smart Home Management

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