IoT in E-Health, Assisted Living, and E-Wellness

Azim, Mohammad Abdul; Merry, Ethel; Gyalmo, Jigmey; Alom, Zulfikar

doi:10.4018/978-1-6684-3533-5.ch002

Cited by 2 publications

(2 citation statements)

References 46 publications

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“…prediction. In [9], technological developments are described for creating an architecture for safeguarding information from the health Internet of Things (HIoT) together with a safety analysis. Through a variety of use-case situations, this study provides an organizational structure for monitoring wellness indicators in individuals with impairments or chronic progressive illnesses.…”

Section: Related Workmentioning

confidence: 99%

“…To facilitate seamless communication of patient information to healthcare providers, the adoption of 5G and 6G technology is proposed, offering high-speed communication and improved network adaptability, such as bandwidth and capacity [7]. Despite the abundance of recent research proposing intelligent patient tracking systems, many do not address the critical aspects of accurate, timely assistance and the delay-associated parameters of measured parameters during transmission [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], and [19]. The problem statement revolves around enhancing the quality of life for individuals with diabetes through continuous monitoring, and utilizing modern technologies such as the IoT, communication tools, and AI.…”

Section: Introductionmentioning

confidence: 99%

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Diabetic Patient Real-Time Monitoring System Using Machine Learning

Emad Ali,

Imad Ali,

Hussein Morad

et al. 2024

IJCDS

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Continuous monitoring is critical to improving the quality of life of people with diabetes. Leveraging technologies such as the Internet of Things (IoT), modern communication tools, and artificial intelligence (AI) can contribute to reducing healthcare costs. The integration of various communication systems allows the provision of personalized and remote healthcare services. The increasing volume of healthcare data poses challenges in storage and processing. To overcome this challenge, this paper suggests intelligent medical architectures for intelligent e-health applications. To provide cutting-edge medical services, 5G and 6G technologies are necessary, since they can satisfy critical needs, including high bandwidth and energy efficiency. This work presents an intelligent machine learning (ML) using an ensemble learning-based real-time monitoring system for diabetes patients. Mobiles, detectors, and other intelligent gadgets are used as buildings to gather measurements of the body. Subsequently, the collected data undergoes a normalization procedure for preprocessing. Principal Component Analysis (PCA) is employed to extract features. The ranking of every feature in the dataset is then assessed using two feature selection (FS) techniques, namely information gain (IG) and chi-square (chi2), and the association between the features chosen by the FS methods is then found using Pearson's correlation method, which is one of the correlation methods that can be used to find the correlated between the selected features. For diagnostic purposes, the intelligent system employs data classification through an ensemble learning approach using XGBoost and Random Forest (RF) as base models, which is named (ENS XGRF). The final classification is determined by a hard voting mechanism in conjunction with particle swarm optimization (PASWOP). The simulation results underscore the superiority of the suggested approach in terms of accuracy when compared to alternative techniques.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diabetic Patient Real-Time Monitoring System Using Machine Learning

Emad Ali,

Imad Ali,

Hussein Morad

et al. 2024

IJCDS

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Real-Time Multiple Object Tracking for Safe Cooking Activities

Ngankam,

Dion,

Pigot

et al. 2023

Lecture Notes in Computer Science

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This work presents a real-time system for tracking multiple object in the context of meal preparation when using the Cognitive Orthosis for CoOKing (COOK). This system is called SafeCOOK. It aims to provide more capabilities to detect some dangerous situations that the current system does not consider. For example, it can locate a utensil or other kitchen object that has been left on the cooking surface of the stove while a meal is being prepared. This system uses a hybrid method based on YOLO and KCF to detect, track and drop cooking utensils as they enter and leave the cooking area, and is capable of monitoring an entire cooktop in real-time with a single camera. The software has been implemented on an embedded platform in the smart stove and has been added to it. The system produces good segmentation and tracking results at a frame rate of 1 to 4 frames per second, as demonstrated in extensive experiments using video sequences under different conditions.

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IoT in E-Health, Assisted Living, and E-Wellness

Cited by 2 publications

References 46 publications

Diabetic Patient Real-Time Monitoring System Using Machine Learning

Diabetic Patient Real-Time Monitoring System Using Machine Learning

Real-Time Multiple Object Tracking for Safe Cooking Activities

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