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

• Published in: IEEE access Vol. 12; pp. 21437 - 21452
• Main Authors: Putra, Karisma Trinanda, Arrayyan, Ahmad Zaki, Hayati, Nur, Firdaus, Damarjati, Cahya, Bakar, Abu, Chen, Hsing-Chung
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3358827

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.