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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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10415018/ |
| _version_ | 1797311685599428608 |
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| author | Karisma Trinanda Putra Ahmad Zaki Arrayyan Nur Hayati Firdaus Cahya Damarjati Abu Bakar Hsing-Chung Chen |
| author_facet | Karisma Trinanda Putra Ahmad Zaki Arrayyan Nur Hayati Firdaus Cahya Damarjati Abu Bakar Hsing-Chung Chen |
| author_sort | Karisma Trinanda Putra |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-08T02:04:18Z |
| format | Article |
| id | doaj.art-f2da1a51a8674bd2994f742426df5085 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T02:04:18Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f2da1a51a8674bd2994f742426df50852024-02-14T00:01:43ZengIEEEIEEE Access2169-35362024-01-0112214372145210.1109/ACCESS.2024.335882710415018A Review on the Application of Internet of Medical Things in Wearable Personal Health Monitoring: A Cloud-Edge Artificial Intelligence ApproachKarisma Trinanda Putra0https://orcid.org/0000-0003-2887-3527Ahmad Zaki Arrayyan1Nur Hayati2https://orcid.org/0000-0003-0960-2182 Firdaus3https://orcid.org/0000-0003-4253-7415Cahya Damarjati4https://orcid.org/0000-0003-4389-6321Abu Bakar5Hsing-Chung Chen6https://orcid.org/0000-0003-2887-3527Department of Electrical Engineering, Faculty of Engineering, Universitas Muhammadiyah Yogyakarta, Bantul, IndonesiaDepartment of Electrical Engineering, Faculty of Engineering, Universitas Muhammadiyah Yogyakarta, Bantul, IndonesiaDepartment of Electrical Engineering, Faculty of Engineering, Universitas Muhammadiyah Yogyakarta, Bantul, IndonesiaDepartment of Electrical Engineering, Faculty of Industrial Technology, University Islam Indonesia, Sleman, IndonesiaDepartment of Information Technology, Faculty of Engineering, Universitas Muhammadiyah Yogyakarta, Bantul, IndonesiaDepartment of Oral Medicine, Faculty of Dentistry, Baiturrahmah University, Padang, IndonesiaDepartment of Computer Science and Information Engineering, College of Information and Electrical Engineering, Asia University, Taichung City, TaiwanThe 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.https://ieeexplore.ieee.org/document/10415018/Wearable Internet of Medical Thingscloud-edge AIedge federated learning |
| spellingShingle | Karisma Trinanda Putra Ahmad Zaki Arrayyan Nur Hayati Firdaus Cahya Damarjati Abu Bakar Hsing-Chung Chen A Review on the Application of Internet of Medical Things in Wearable Personal Health Monitoring: A Cloud-Edge Artificial Intelligence Approach IEEE Access Wearable Internet of Medical Things cloud-edge AI edge federated learning |
| title | A Review on the Application of Internet of Medical Things in Wearable Personal Health Monitoring: A Cloud-Edge Artificial Intelligence Approach |
| title_full | A Review on the Application of Internet of Medical Things in Wearable Personal Health Monitoring: A Cloud-Edge Artificial Intelligence Approach |
| title_fullStr | A Review on the Application of Internet of Medical Things in Wearable Personal Health Monitoring: A Cloud-Edge Artificial Intelligence Approach |
| title_full_unstemmed | A Review on the Application of Internet of Medical Things in Wearable Personal Health Monitoring: A Cloud-Edge Artificial Intelligence Approach |
| title_short | A Review on the Application of Internet of Medical Things in Wearable Personal Health Monitoring: A Cloud-Edge Artificial Intelligence Approach |
| title_sort | review on the application of internet of medical things in wearable personal health monitoring a cloud edge artificial intelligence approach |
| topic | Wearable Internet of Medical Things cloud-edge AI edge federated learning |
| url | https://ieeexplore.ieee.org/document/10415018/ |
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