New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Internet of Medical Things for Smart Cities

Kamruzzaman, M. M.; Alrashdi, Ibrahim; Alqazzaz, Ali

doi:10.1155/2022/2950699

Cited by 48 publications

(20 citation statements)

References 50 publications

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“…Kamruzzaman, et al [ 26 ] reviewed the application of Internet-of-Medical-Things (IoMT), AI and Edge computing for healthcare delivery in ubiquitous environments. They posited that the aforementioned technologies could assist in the proper monitoring and management of healthcare systems.…”

Section: Related Workmentioning

confidence: 99%

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

et al. 2022

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In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.

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Section: Related Workmentioning

confidence: 99%

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

et al. 2022

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“…Smart solutions for Internet of Healthcare Things (IoHT) [ 1 ], also known as Healthcare Internet of Things [ 2 ], Internet of Medical Things [ 3 ], or Medical Internet of Things [ 4 ], systems have extensively emerged since the Industry 4.0 revolution [ 5 ], making use of digital devices, in particular wearable sensors and smart handheld devices. In the new phase of the industrial revolution, termed Industry 5.0, collaborative interaction between machines and people is coming back to the forefront [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition Based on Embedded Sensor Data Fusion for the Internet of Healthcare Things

et al. 2022

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Nowadays, the emerging information technologies in smart handheld devices are motivating the research community to make use of embedded sensors in such devices for healthcare purposes. In particular, inertial measurement sensors such as accelerometers and gyroscopes embedded in smartphones and smartwatches can provide sensory data fusion for human activities and gestures. Thus, the concepts of the Internet of Healthcare Things (IoHT) paradigm can be applied to handle such sensory data and maximize the benefits of collecting and analyzing them. The application areas contain but are not restricted to the rehabilitation of elderly people, fall detection, smoking control, sportive exercises, and monitoring of daily life activities. In this work, a public dataset collected using two smartphones (in pocket and wrist positions) is considered for IoHT applications. Three-dimensional inertia signals of thirteen timestamped human activities such as Walking, Walking Upstairs, Walking Downstairs, Writing, Smoking, and others are registered. Here, an efficient human activity recognition (HAR) model is presented based on efficient handcrafted features and Random Forest as a classifier. Simulation results ensure the superiority of the applied model over others introduced in the literature for the same dataset. Moreover, different approaches to evaluating such models are considered, as well as implementation issues. The accuracy of the current model reaches 98.7% on average. The current model performance is also verified using the WISDM v1 dataset.

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“…In healthcare, the integration of medical hardware devices, smart systems, and apps with cloud storage and computing by using the Internet connection is referred to as the Internet of Medical Things (IoMT) [ 1 ]. After the IoT was introduced, it drastically reduced the number of unnecessary hospital visits by connecting patients to their doctors and allowed medical data to be sent over a secure network from anywhere.…”

Section: Introductionmentioning

confidence: 99%

A Secure Framework toward IoMT‐Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

Islam

Kaushal

Amin

et al. 2022

Contrast Media & Molecular Imaging

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The abnormal growth of the skin cells is known as skin cancer. It is one of the main problems in the dermatology area. Skin lesions or malignancies have been a source of worry for many individuals in recent years. Irrespective of the skin tone, there exist three major classes of skin lesions, i.e., basal cell carcinoma, squamous cell carcinoma, and melanoma. The early diagnosis of these lesions is equally important for human life. In the proposed work, a secure IoMT-Assisted framework is introduced that can help the patients to do the initial screening of skin lesions remotely. The initially proposed approach uses an IoMT-based data collection device which is accessible by patients to capture skin lesions images. Next, the captured skin sample is encrypted and sent to the collected image toward cloud storage. Later, the received sample image is classified into appropriate class labels using an ensemble classifier. In the proposed framework, four CNN models were ensemble i.e., VGG-16, DenseNet-201, Inception-V3, and Efficient-B7. The framework has experimented with the “HAM10000” dataset having 7 different kinds of skin lesions data. Although DenseNet-201 performed well, the ensemble model provides the highest accuracy with 87.22 percent as well as its test loss/error is lower than others with 0.4131. Moreover, the ensemble model’s classification ability is much higher with an AUC score of 0.9745. Moreover, A recommendation team has been assigned to assess the sample of the patient as well as suggest the patient according to classified results by the CAD.

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New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Internet of Medical Things for Smart Cities

Cited by 48 publications

References 50 publications

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

Human Activity Recognition Based on Embedded Sensor Data Fusion for the Internet of Healthcare Things

A Secure Framework toward IoMT‐Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

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