Smart City Gnosys
Smart city article details
| Title | Smart Health Monitoring Using Deep Learning And Artificial Intelligence |
|---|---|
| ID_Doc | 51003 |
| Authors | Philip J.; Gandhimathi S.K.; Chalichalamala S.; Karnam B.; Chandanapalli S.B.; Chennupalli S. |
| Year | 2023 |
| Published | Revue d'Intelligence Artificielle, 37, 2 |
| DOI | http://dx.doi.org/10.18280/ria.370222 |
| Abstract | The genesis and spread of illnesses are a major concern in today's rapidly developing technological and evolutionary environment. The prevention and management of illnesses using technological means have emerged as one of the most pressing challenges facing the medical community. With today's hectic schedules, it's nearly impossible to stick to a healthy routine. The problems above can be fixed by using a smart health monitoring system. Two of the most rapidly emerging technologies are the Internet of Things (IoT) and artificial intelligence (AI). As more people relocate to urban areas, the idea of a "smart city" has become increasingly commonplace. Increased efficiency, decreased expenses, and a renewed emphasis on improving the quality of care provided to patients are central to the idea of a "smart city." There has to be a thorough familiarity with the various smart city frameworks before the Internet of Things (IoT) and artificial intelligence (AI) can be effectively used for remote healthcare monitoring (RHM) systems. Technologies, gadgets, systems, models, designs, use cases, and applications are all examples of frameworks. The RHM system, based on the Internet of Things, relies heavily on artificial intelligence (AI) and deep learning (DL) to analyse the data it collects. However, DL techniques are widely utilised for making analytical representations, and they are included in CDSS and other kinds of healthcare services. Patients are given personalised recommendations for therapy, lifestyle changes, and care plans by clinical decision support systems after each element is thoroughly analysed. Supporting healthcare applications, this technology may assess activities, etc. In light of this, this paper presents a survey that zeroes in on the best smart city applications for the Internet of Things in health. By analysing the most important monitoring applications across many models using appropriate IoT-based sensors, this research provides a comprehensive assessment of the technologies and systems involved in providing RHM services. Finally, this study makes a contribution to scientific understanding by identifying the primary constraints on this field of study and suggesting directions for further investigation. © 2023 Lavoisier. All rights reserved. |
| Author Keywords | clinical decision support systems; deep learning; Internet of Things; remote healthcare monitoring; smart city infrastructure |
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