Fog-assisted personalized healthcare-support system for remote patients with diabetes
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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].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [252]
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The emergence of AI-based wearable sensors for digital health technology: a review
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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
- Licence
- www.springer.com/tdm (tdm)
- 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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