The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
5G-Smart Diabetes: Toward Personalized Diabetes Diagnosis with Healthcare Big Data Clouds
Abstract
Recent advances in wireless networking and big data technologies, such as 5G networks, medical big data analytics, and the Internet of Things, along with recent developments in wearable computing and artificial intelligence, are enabling the development and implementation of innovative diabetes monitoring systems and applications. Due to the life-long and systematic harm suffered by diabetes patients, it is critical to design effective methods for the diagnosis and treatment of diabetes. Based on our comprehensive investigation, this article classifies those methods into Diabetes 1.0 and Diabetes 2.0, which exhibit deficiencies in terms of networking and intelligence. Thus, our goal is to design a sustainable, cost-effective, and intelligent diabetes diagnosis solution with personalized treatment. In this article, we first propose the 5G-Smart Diabetes system, which combines the state-of-the-art technologies such as wearable 2.0, machine learning, and big data to generate comprehensive sensing and analysis for patients suffering from diabetes. Then we present the data sharing mechanism and personalized data analysis model for 5G-Smart Diabetes. Finally, we build a 5G-Smart Diabetes testbed that includes smart clothing, smartphone, and big data clouds. The experimental results show that our system can effectively provide personalized diagnosis and treatment suggestions to patients.
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Cites
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Self-citationWearable 2.0: Enabling Human-Cloud Integration in Next Generation Healthcare Systems
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Self-citationCloud-Supported Cyber–Physical Localization Framework for Patients Monitoring
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Self-citationBig-Data Analytics for Cloud, IoT and Cognitive Computing
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5G-based smart healthcare network: architecture, taxonomy, challenges and future research directions
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An intelligent healthcare monitoring framework using wearable sensors and social networking data
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Metadata
- Authors
- Chen, M., Yang, J., Zhou, J., Hao, Y., Zhang, J., & Youn, C.-H.
- Venue
- IEEE Communications Magazine
- Published
- 2018-04
- Volume
- 56
- Issue
- 4
- Pages
- 16-23
- ISSN
- 0163-6804 (ISSN-L)
- 1558-1896
- OpenAlex
- W2796802878
- Cited by
- 266
- References
- 12
- FWCI
- 50.62
- Type
- journal article
- Language
- en
- Open access
- closed
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- Chen, M., Yang, J., Zhou, J., Hao, Y., Zhang, J., & Youn, C.-H. (2018). 5G-Smart Diabetes: Toward Personalized Diabetes Diagnosis with Healthcare Big Data Clouds. IEEE Communications Magazine. https://doi.org/10.1109/mcom.2018.1700788
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