i-Care: an IoMT Remote Monitoring System Of Physiological Pain in Pediatric Patients

Guzman-Sandoval, Veronica M.; Gaytán-Lugo, Laura S.; Santana-Mancilla, Pedro C.

doi:10.1109/enc53357.2021.9534829

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…Analyzing the behavior of cognitive data networks, especially wireless networks, is a great challenge. Several aspects must be considered regarding the environments where the networks are deployed: the number of devices (i.e., IoT devices [43][44][45]), the characteristics of the applications (i.e., smart environments [46][47][48]), and the transmission technologies [26,49,50], among others. The nonlinearity and complexity of data traffic flow in such networks, for example, are characterized by temporal and spatial correlation.…”

Section: Discussionmentioning

confidence: 99%

A Review of Deep Learning Applications for the Next Generation of Cognitive Networks

et al. 2022

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Intelligence capabilities will be the cornerstone in the development of next-generation cognitive networks. These capabilities allow them to observe network conditions, learn from them, and then, using prior knowledge gained, respond to its operating environment to optimize network performance. This study aims to offer an overview of the current state of the art related to the use of deep learning in applications for intelligent cognitive networks that can serve as a reference for future initiatives in this field. For this, a systematic literature review was carried out in three databases, and eligible articles were selected that focused on using deep learning to solve challenges presented by current cognitive networks. As a result, 14 articles were analyzed. The results showed that applying algorithms based on deep learning to optimize cognitive data networks has been approached from different perspectives in recent years and in an experimental way to test its technological feasibility. In addition, its implications for solving fundamental challenges in current wireless networks are discussed.

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Section: Discussionmentioning

confidence: 99%

A Review of Deep Learning Applications for the Next Generation of Cognitive Networks

et al. 2022

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“…Internet of Medical Things (IoMT) technology is a promising solution in the medical field. The IoMT connects various medical instruments, devices, or sensors to the internet, enabling real-time data collection on a user's physical condition and accurate diagnosis [9,10]. Additionally, integrating machine learning (ML) algorithms into this system has been proposed.…”

Section: Introductionmentioning

confidence: 99%

Predicting Abnormal Respiratory Patterns in Older Adults Using Supervised Machine Learning on Internet of Medical Things Respiratory Frequency Data

Santana-Mancilla,

Castrejón-Mejía,

Fajardo-Flores

et al. 2023

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Wearable Internet of Medical Things (IoMT) technology, designed for non-invasive respiratory monitoring, has demonstrated considerable promise in the early detection of severe diseases. This paper introduces the application of supervised machine learning techniques to predict respiratory abnormalities through frequency data analysis. The principal aim is to identify respiratory-related health risks in older adults using data collected from non-invasive wearable devices. This article presents the development, assessment, and comparison of three machine learning models, underscoring their potential for accurately predicting respiratory-related health issues in older adults. The convergence of wearable IoMT technology and machine learning holds immense potential for proactive and personalized healthcare among older adults, ultimately enhancing their quality of life.

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IoT-Enabled Pediatric Pain Care Leveraging Majority Vote-Based Transfer Learning Models

Gaikwad,

Upadhyay,

Manero

et al. 2024

2024 7th Conference on Cloud and Internet of Things (CIoT)

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i-Care: an IoMT Remote Monitoring System Of Physiological Pain in Pediatric Patients

Cited by 6 publications

References 22 publications

A Review of Deep Learning Applications for the Next Generation of Cognitive Networks

A Review of Deep Learning Applications for the Next Generation of Cognitive Networks

Predicting Abnormal Respiratory Patterns in Older Adults Using Supervised Machine Learning on Internet of Medical Things Respiratory Frequency Data

IoT-Enabled Pediatric Pain Care Leveraging Majority Vote-Based Transfer Learning Models

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