The design of an m-Health monitoring system based on a cloud computing platform

Xu, Bin; Xu, Lida; Cai, Hongming; Jiang, Lihong; Luo, Yang; Gu, Yizhi

doi:10.1080/17517575.2015.1053416

Cited by 149 publications

(61 citation statements)

References 54 publications

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“…As reported in Table 1, and in Figure 1, the literature search identified 37 citations in PubMed and 2806 citations in Scopus applying ¡"Linked Open Data"[All Fields]¿ as a search string. Specifying the search strings according to the aim of the review, and the inclusion criteria ( Figure 1), 9 papers have been considered and examined [23][24][25][26][27][28][29][30][31].…”

Section: Resultsmentioning

confidence: 99%

Linked Open Data in Health and Clinical Care A Review of the Literature

Bonacina¹

2016

ejbi

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Background: In the range of Semantic Web, the idea of linking and sharing the resources generated by different authors, like ontologies, knowledge bases, or datasets, is referred to "Linked data". Then, an ambitious project within the "Linked Data" paradigm is the "Linking Open Data" community project.Results: Nine journal articles have been analysed according to the defined evaluation grid. In five out of nine papers, the main contributions are strategies and methodologies for the integration of systems, including bridging the information gap among forms for clinical research and the one for patient care. Then, in three papers the main contributions are the development of consistent triple stores according to the "Linked Data" paradigm. Finally, the last paper aims at building an open dataset for public health purposes. Conclusions: The review was able to answer the research questions, despite the limited number of included papers.

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Section: Resultsmentioning

confidence: 99%

Linked Open Data in Health and Clinical Care A Review of the Literature

Bonacina¹

2016

ejbi

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“…Many studies have been reported which utilized the cIoT model to remotely monitor patients [67], [68], [69] using: ECG android application [70] along with other helathcare data [71], FIWARE 5 platform [72], and wearable sensors (IoT) and body area network [73]. Additionally, other cIoT frameworks include: personnel altering system regarding lifestyle diseases from physical activity data [74], [75], collecting real-time patient data from wireless body area network [76], accumulating physiological and healthcare data smart clothing with IoT sensors [77], [78], selecting personalized treatment plan [79], providing personalized medical diagnosis [80], supporting the physically challenged with assistive devices [81].…”

Section: State-of-the-art Of Ioht Frameworkmentioning

confidence: 99%

Toward a Heterogeneous Mist, Fog, and Cloud-Based Framework for the Internet of Healthcare Things

Asif-Ur-Rahman

Afsana

Mahmud

et al. 2019

IEEE Internet Things J.

167

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Rapid developments in the fields of information and communication technology and microelectronics allowed seamless interconnection among various devices letting them to communicate with each other. This technological integration opened up new possibilities in many disciplines including healthcare and well-being. With the aim of reducing healthcare costs and providing improved and reliable services, several healthcare frameworks based on Internet of Healthcare Things (IoHT) have been developed. However, due to the critical and heterogeneous nature of healthcare data, maintaining high quality of service (QoS) -in terms of faster responsiveness and data-specific complex analytics -has always been the main challenge in designing such systems. Addressing these issues, this paper proposes a five-layered heterogeneous mist, fog, and cloud based IoHT framework capable of efficiently handling and routing (near-)real-time as well as offline/batch mode data. Also, by employing software defined networking and link adaptation based load balancing, the framework ensures optimal resource allocation and efficient resource utilization. The results, obtained by simulating the framework, indicate that the designed network via its various components can achieve high QoS, with reduced end-to-end latency and packet drop rate, which is essential for developing next generation e-healthcare systems.

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“…In patient wellbeing checking devices the present advancements are based on cloud. the networks of cloud enables restorative experts to be told as suitable, empowers information stockpiling and examination and gives generous healthcare computing features [19].…”

Section: Cloud-based Smart Health Monitoringmentioning

confidence: 99%

Smart Health Records

Lestari

Rahim

Nguyen

et al. 2019

IJEAT

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If there is no health record history of a patient then it will be very difficult for treating in different hospitals. Especially for travelers and in the case of emergency it can ruin the auspicious treatment and diagnosis. If the smart health records are based on Cloud then there is higher difficulties for protection and security, the open difficulties of interoperability and joining and also lack of every day support for the high accessibility of wellbeing history. The smart health records that are already exists can store only limited about of data of patients and they stored data only for specific hospital and do not support versatility of patients crosswise over various medical clinics. The technology that use better treatment for patients, improve quality of life for everyone and provide better diagnostic tools can be consider as smart health.

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The design of an m-Health monitoring system based on a cloud computing platform

Cited by 149 publications

References 54 publications

Linked Open Data in Health and Clinical Care A Review of the Literature

Linked Open Data in Health and Clinical Care A Review of the Literature

Toward a Heterogeneous Mist, Fog, and Cloud-Based Framework for the Internet of Healthcare Things

Smart Health Records

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