Cited by 105IoT and Edge/Fog ComputingInternet of Things and AIArtificial Intelligence in Healthcare

Fog-assisted personalized healthcare-support system for remote patients with diabetes

Devarajan, Malathi, Subramaniyaswamy, V., Vijayakumar, V., Ravi, Logesh·2019·Journal of Ambient Intelligence and Humanized Computing

Integrity signals

Registry

Crossref · 10.1007/s12652-019-01291-5

Cites

66 of 66 references verified0 citation errors · 0 not checkable

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  • Only a few studies have focused on the relationship between biofluid signals and human health parameters, such as cortisol sensing and stress, glucose sensing and diabetes, or physiological data related to Parkinson's disease.

    “Only a few studies have focused on this, such as one recent work on cortisol sensing and its relation to human stress levels [129] and glucose sensing and its relation to diabetes levels in patients [252], or physiological data related to Parkinson’s disease [253].”

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Authors
Devarajan, M., Subramaniyaswamy, V., Vijayakumar, V., & Ravi, L.
Affiliations
  • SASTRA University (IN)
  • Vellore Institute of Technology University (IN)
Corresponding author
Ravi, Logesh
Venue
Journal of Ambient Intelligence and Humanized Computing
Published
2019-04-16
Volume
10
Issue
10
Pages
3747-3760
ISSN
  • 1868-5137 (ISSN-L)
  • 1868-5145
OpenAlex
W2937982961
Cited by
105
References
66
Citations by year
2026: 2 · 2025: 8 · 2024: 16 · 2023: 16 · 2022: 16 · 2021: 24 · 2020: 20 · 2019: 3
FWCI
8.62
Type
journal article
Language
en
Open access
closed
Locations
Journal of Ambient Intelligence and Humanized Computing (publishedVersion)
Funders
SASTRA University
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
  • Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
diabetes mellitus, blood glucose monitoring, fog computing, Internet of Things, personalized healthcare, energy efficiency, diabetes risk prediction, emergency alerting, remote patient monitoring

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