A Review on the Application of Internet of Medical Things in Wearable Personal Health Monitoring: A Cloud-Edge Artificial Intelligence Approach

The advent of the fifth-generation mobile communication technology (5G) era has catalyzed significant advancements in medical diagnosis delivery, primarily driven by the surge in medical data from wearable Internet of Medical Things (IoMT) devices. Nonetheless, the IoMT paradigm grapples with challe...

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Veröffentlicht in:IEEE access 2024, Vol.12, p.21437-21452
Hauptverfasser: Putra, Karisma Trinanda, Arrayyan, Ahmad Zaki, Hayati, Nur, Firdaus, Damarjati, Cahya, Bakar, Abu, Chen, Hsing-Chung
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container_issue
container_start_page 21437
container_title IEEE access
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creator Putra, Karisma Trinanda
Arrayyan, Ahmad Zaki
Hayati, Nur
Firdaus
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Bakar, Abu
Chen, Hsing-Chung
description The advent of the fifth-generation mobile communication technology (5G) era has catalyzed significant advancements in medical diagnosis delivery, primarily driven by the surge in medical data from wearable Internet of Medical Things (IoMT) devices. Nonetheless, the IoMT paradigm grapples with challenges related to data security, privacy, constrained computational capabilities at the edge, and an inadequate architecture for handling traditionally error-prone data. In this context, our research offers: (1) an exhaustive review of large-scale medical data propelled by IoMT, (2) an exploration of the prevailing cloud-edge Artificial Intelligence (AI) framework tailored for IoMT, and (3) an insight into the application of Edge Federated Learning (EFL) in bolstering medical big data analytics to yield secure and superior diagnostic outcomes. We place a particular emphasis on the proliferation of IoMT wearable devices that incessantly stream medical data, either from patients or healthcare institutions, to centralized repositories. Furthermore, we introduce a federated cloud-edge AI blueprint designed to position computational resources proximate to the edge network, facilitating real-time diagnostic feedback to patients. We conclude by delineating prospective research trajectories in enhancing IoMT through AI integration.
doi_str_mv 10.1109/ACCESS.2024.3358827
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subjects 5G mobile communication
Artificial intelligence
Big Data
Biomedical monitoring
Cloud computing
cloud-edge AI
Edge computing
edge federated learning
Federated learning
Internet of Medical Things
Machine learning
Medical diagnostic imaging
Medical electronics
Medical services
Sensors
Wearable computers
Wearable Internet of Medical Things
Wearable technology
title A Review on the Application of Internet of Medical Things in Wearable Personal Health Monitoring: A Cloud-Edge Artificial Intelligence Approach
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