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Cited by 74IoT and Edge/Fog ComputingInternet of Things and AINon-Invasive Vital Sign Monitoring

Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects

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Shumba, Angela-Tafadzwa, Montanaro, Teodoro, Sergi, Ilaria, Bramanti, Alessia, Ciccarelli, Michele, Rispoli, Antonella, Carrizzo, Albino, De Vittorio, Massimo +1 more·2023·Sensors
DOI 10.3390/s23156896PMC PMC10422393
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Abstract

Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors.

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  • Model-compression techniques such as quantization, pruning, and knowledge distillation can reduce model size and computational requirements, potentially enabling faster and more energy-efficient inference on low-power edge devices.

    “Model-compression techniques such as quantization, pruning, and knowledge distillation can reduce model size and computational requirements, potentially enabling faster and more energy-efficient inference on low-power edge devices [65].”

    From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [65]

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Authors
Shumba, A.-T., Montanaro, T., Sergi, I., Bramanti, A., Ciccarelli, M., Rispoli, A., Carrizzo, A., De Vittorio, M., & Patrono, L.
Affiliations
  • University of Salento (IT)
  • University of Salerno (IT)
  • Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy
Corresponding author
Patrono, Luigi
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2023-08-03
Volume
23
Issue
15
Pages
6896
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s23156896
PMC
PMC10422393
OpenAlex
W4385543906
Cited by
74
References
87
Citations by year
2026: 25 · 2025: 26 · 2024: 21 · 2023: 2
FWCI
2.63
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • AIRUS (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Italian Ministry of Health, Italian Health Operational Plan (F84D22000270001)
  • MUR, Ministero dell`Universita e della Ricerca (F84D22000270001)
  • IDAS-Innovazione Digitale in Ambito Salute (F84D22000270001)
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
  • Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
Keywords
chronic heart failure, wearable devices, edge computing, edge AI, Internet of Things, remote patient monitoring, personalized healthcare, health data privacy, disease prevention and management
MeSH terms
Wearable Electronic Devices, Artificial Intelligence, Awareness, Chronic Disease, Heart Failure, Humans

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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