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Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0

Driven by technological advances from Industry 4.0, Healthcare 4.0 synthesizes medical sensors, artificial intelligence (AI), big data, the Internet of things (IoT), machine learning, and augmented reality (AR) to transform the healthcare sector. Healthcare 4.0 creates a smart health network by conn...

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Detalles Bibliográficos
Autores principales: He, Lawrence, Eastburn, Mark, Smirk, James, Zhao, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10303589/
https://www.ncbi.nlm.nih.gov/pubmed/37420917
http://dx.doi.org/10.3390/s23125754
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author He, Lawrence
Eastburn, Mark
Smirk, James
Zhao, Hong
author_facet He, Lawrence
Eastburn, Mark
Smirk, James
Zhao, Hong
author_sort He, Lawrence
collection PubMed
description Driven by technological advances from Industry 4.0, Healthcare 4.0 synthesizes medical sensors, artificial intelligence (AI), big data, the Internet of things (IoT), machine learning, and augmented reality (AR) to transform the healthcare sector. Healthcare 4.0 creates a smart health network by connecting patients, medical devices, hospitals, clinics, medical suppliers, and other healthcare-related components. Body chemical sensor and biosensor networks (BSNs) provide the necessary platform for Healthcare 4.0 to collect various medical data from patients. BSN is the foundation of Healthcare 4.0 in raw data detection and information collecting. This paper proposes a BSN architecture with chemical sensors and biosensors to detect and communicate physiological measurements of human bodies. These measurement data help healthcare professionals to monitor patient vital signs and other medical conditions. The collected data facilitates disease diagnosis and injury detection at an early stage. Our work further formulates the problem of sensor deployment in BSNs as a mathematical model. This model includes parameter and constraint sets to describe patient body characteristics, BSN sensor features, as well as biomedical readout requirements. The proposed model’s performance is evaluated by multiple sets of simulations on different parts of the human body. Simulations are designed to represent typical BSN applications in Healthcare 4.0. Simulation results demonstrate the impact of various biofactors and measurement time on sensor selections and readout performance.
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spelling pubmed-103035892023-06-29 Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0 He, Lawrence Eastburn, Mark Smirk, James Zhao, Hong Sensors (Basel) Article Driven by technological advances from Industry 4.0, Healthcare 4.0 synthesizes medical sensors, artificial intelligence (AI), big data, the Internet of things (IoT), machine learning, and augmented reality (AR) to transform the healthcare sector. Healthcare 4.0 creates a smart health network by connecting patients, medical devices, hospitals, clinics, medical suppliers, and other healthcare-related components. Body chemical sensor and biosensor networks (BSNs) provide the necessary platform for Healthcare 4.0 to collect various medical data from patients. BSN is the foundation of Healthcare 4.0 in raw data detection and information collecting. This paper proposes a BSN architecture with chemical sensors and biosensors to detect and communicate physiological measurements of human bodies. These measurement data help healthcare professionals to monitor patient vital signs and other medical conditions. The collected data facilitates disease diagnosis and injury detection at an early stage. Our work further formulates the problem of sensor deployment in BSNs as a mathematical model. This model includes parameter and constraint sets to describe patient body characteristics, BSN sensor features, as well as biomedical readout requirements. The proposed model’s performance is evaluated by multiple sets of simulations on different parts of the human body. Simulations are designed to represent typical BSN applications in Healthcare 4.0. Simulation results demonstrate the impact of various biofactors and measurement time on sensor selections and readout performance. MDPI 2023-06-20 /pmc/articles/PMC10303589/ /pubmed/37420917 http://dx.doi.org/10.3390/s23125754 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 Article
He, Lawrence
Eastburn, Mark
Smirk, James
Zhao, Hong
Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0
title Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0
title_full Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0
title_fullStr Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0
title_full_unstemmed Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0
title_short Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0
title_sort smart chemical sensor and biosensor networks for healthcare 4.0
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10303589/
https://www.ncbi.nlm.nih.gov/pubmed/37420917
http://dx.doi.org/10.3390/s23125754
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