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

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...

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Autores principales: Shumba, Angela-Tafadzwa, Montanaro, Teodoro, Sergi, Ilaria, Bramanti, Alessia, Ciccarelli, Michele, Rispoli, Antonella, Carrizzo, Albino, De Vittorio, Massimo, Patrono, Luigi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422393/
https://www.ncbi.nlm.nih.gov/pubmed/37571678
http://dx.doi.org/10.3390/s23156896
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author Shumba, Angela-Tafadzwa
Montanaro, Teodoro
Sergi, Ilaria
Bramanti, Alessia
Ciccarelli, Michele
Rispoli, Antonella
Carrizzo, Albino
De Vittorio, Massimo
Patrono, Luigi
author_facet Shumba, Angela-Tafadzwa
Montanaro, Teodoro
Sergi, Ilaria
Bramanti, Alessia
Ciccarelli, Michele
Rispoli, Antonella
Carrizzo, Albino
De Vittorio, Massimo
Patrono, Luigi
author_sort Shumba, Angela-Tafadzwa
collection PubMed
description 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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spelling pubmed-104223932023-08-13 Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects Shumba, Angela-Tafadzwa Montanaro, Teodoro Sergi, Ilaria Bramanti, Alessia Ciccarelli, Michele Rispoli, Antonella Carrizzo, Albino De Vittorio, Massimo Patrono, Luigi Sensors (Basel) Systematic Review 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. MDPI 2023-08-03 /pmc/articles/PMC10422393/ /pubmed/37571678 http://dx.doi.org/10.3390/s23156896 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Systematic Review
Shumba, Angela-Tafadzwa
Montanaro, Teodoro
Sergi, Ilaria
Bramanti, Alessia
Ciccarelli, Michele
Rispoli, Antonella
Carrizzo, Albino
De Vittorio, Massimo
Patrono, Luigi
Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects
title Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects
title_full Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects
title_fullStr Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects
title_full_unstemmed Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects
title_short Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects
title_sort wearable technologies and ai at the far edge for chronic heart failure prevention and management: a systematic review and prospects
topic Systematic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422393/
https://www.ncbi.nlm.nih.gov/pubmed/37571678
http://dx.doi.org/10.3390/s23156896
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