Automated Internet of Medical Things (IoMT) Based Healthcare Monitoring System

Velmurugan, S.; Sivakumar, P.; Sampson, Jenyfal

doi:10.1007/978-3-030-55833-8_7

Cited by 10 publications

(2 citation statements)

References 22 publications

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“…Recently, we have witnessed a rapid evolution in the fields of the Internet of Medical Things (IoMT), data processing and fusion, and telemedicine [ 25 ]. The field of IoMT has opened new opportunities for patient monitoring and remote service provision, particularly in older adults with chronic diseases [ 26 , 27 ]. However, the rise of the IoMT has also brought new challenges in terms of, for example, privacy, security, and computing capabilities [ 28 ].…”

Section: Research Gapmentioning

confidence: 99%

A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data

Vaccari

Orani

Paglialonga

et al. 2021

Sensors

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The application of machine learning and artificial intelligence techniques in the medical world is growing, with a range of purposes: from the identification and prediction of possible diseases to patient monitoring and clinical decision support systems. Furthermore, the widespread use of remote monitoring medical devices, under the umbrella of the “Internet of Medical Things” (IoMT), has simplified the retrieval of patient information as they allow continuous monitoring and direct access to data by healthcare providers. However, due to possible issues in real-world settings, such as loss of connectivity, irregular use, misuse, or poor adherence to a monitoring program, the data collected might not be sufficient to implement accurate algorithms. For this reason, data augmentation techniques can be used to create synthetic datasets sufficiently large to train machine learning models. In this work, we apply the concept of generative adversarial networks (GANs) to perform a data augmentation from patient data obtained through IoMT sensors for Chronic Obstructive Pulmonary Disease (COPD) monitoring. We also apply an explainable AI algorithm to demonstrate the accuracy of the synthetic data by comparing it to the real data recorded by the sensors. The results obtained demonstrate how synthetic datasets created through a well-structured GAN are comparable with a real dataset, as validated by a novel approach based on machine learning.

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Section: Research Gapmentioning

confidence: 99%

A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data

Vaccari

Orani

Paglialonga

et al. 2021

Sensors

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“…As a result of the earlier described advancements with combining g smart sensors, actuators, and data analytics in an IoT environment for real-time and continuous healthcare brings great promise to the healthcare industry. The field, called the Internet of Health Things (IoHT) [12], [13] or the Internet of Medical Things (IoMT) [14], [15] offers the potential of transforming the healthcare paradigm. The method which is pursued in research and practice can be divided into several areas.…”

Section: Introductionmentioning

confidence: 99%

Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects

Mahmoud¹,

Islam

Shehata

et al. 2021

IEEE Access

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The significant increase in the number of individuals with chronic ailments (including the elderly and disabled) has dictated an urgent need for an innovative model for healthcare systems. The evolved model will be more personalized and less reliant on traditional brick-and-mortar healthcare institutions such as hospitals, nursing homes, and long-term healthcare centers. The smart healthcare system is a topic of recently growing interest and has become increasingly required due to major developments in modern technologies, especially in artificial intelligence (AI) and machine learning (ML). This paper is aimed to discuss the current state-of-the-art smart healthcare systems highlighting major areas like wearable and smartphone devices for health monitoring, machine learning for disease diagnosis, and the assistive frameworks, including social robots developed for the ambient assisted living environment. Additionally, the paper demonstrates software integration architectures that are very significant to create smart healthcare systems, integrating seamlessly the benefit of data analytics and other tools of AI. The explained developed systems focus on several facets: the contribution of each developed framework, the detailed working procedure, the performance as outcomes, and the comparative merits and limitations. The current research challenges with potential future directions are addressed to highlight the drawbacks of existing systems and the possible methods to introduce novel frameworks, respectively. This review aims at providing comprehensive insights into the recent developments of smart healthcare systems to equip experts to contribute to the field.

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Attaining an IoMT-based health monitoring and prediction: a hybrid hierarchical deep learning model and metaheuristic algorithm

Shukla,

Alqahtani,

Dwivedi

et al. 2023

Neural Comput & Applic

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Automated Internet of Medical Things (IoMT) Based Healthcare Monitoring System

Cited by 10 publications

References 22 publications

A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data

A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data

Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects

Attaining an IoMT-based health monitoring and prediction: a hybrid hierarchical deep learning model and metaheuristic algorithm

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