Internet of Things in Healthcare: Architecture, Applications, Challenges, and Solutions

Naresh, Vankamamidi S.; Pericherla, Suryateja Satya; Murty, Pilla Sita Rama; Sivaranjani, R.

doi:10.32604/csse.2020.35.411

Cited by 63 publications

(30 citation statements)

References 10 publications

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“…There are three layers: (1) things layer, (2) fog layer, and (3) cloud layer. This is a modified version of the architecture present in [3]. In this architecture, healthcare experts can also communicate directly through the router between the Thing layer and Fog layer and through the local processing servers at the fog layer.…”

Section: Architecture For Iomtmentioning

confidence: 99%

Internet of Medical Things (IoMT): Overview, Emerging Technologies, and Case Studies

Razdan

Sharma

2021

IETE Technical Review

158

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In the Internet of Medical Things (IoMT), the Internet of Things (IoT) is integrated with medical devices, enabling improved patient comfort, cost-effective medical solutions, quick hospital treatments, and even more personalized healthcare. The paper first provides the introduction of IoMTs and then introduces an architecture of IoMTs. Later, it provides the current operations of the healthcare system and discusses the mapping of these operations into the architectural diagram. Further, several emerging technologies such as Physically Unclonable Functions (PUF), Blockchain, Artificial Intelligence (AI), and Software-Defined Networking (SDN) are envisioned as important technologies to overcome several challenges in e-healthcare such as security, privacy, accuracy, and performance. Finally, we provide three case studies for IoMT based on -(1) PUF-based Authentication, (2) AIenabled SDN Assisted e-healthcare, and (3) Blockchain Assisted Patient Centric System. The solutions presented in this paper may have a huge impact on the speed at which IoMT infrastructure can efficiently evolve with market evolution.

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Section: Architecture For Iomtmentioning

confidence: 99%

Internet of Medical Things (IoMT): Overview, Emerging Technologies, and Case Studies

Razdan

Sharma

2021

IETE Technical Review

158

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“…Saugumas ir pažeidžiamumas (Aceto et al, 2018(Aceto et al, , 2020Anawar et al, 2018;Badidi et al, 2020;Basir et al, 2019;da Silva ir de Aquino, 2018;Dang et al, 2019;de Moura Costa et al, 2020;Dong ir Yao, 2021;EscamillaAmbrosio et al, 2018;Gaber et al, 2019;Haouari et al, 2018;Hu et al, 2017;Ibrahim et al, 2018;Javadzadeh ir Rahmani, 2020;Kraemer et al, 2017;Naresh et al, 2020;Nazir et al, 2019;Saheb ir Izadi, 2019;Swamy ir Kota, 2020;Tayeb et al, 2017;Vilela et al, 2020) (Aceto et al, 2018;Anawar et al, 2018;Badidi et al, 2020;Basir et al, 2019;Behmanesh et al, 2020;da Silva ir de Aquino, 2018;Dang et al, 2019;de Moura Costa et al, 2020;de Prado et al, 2020;Dong ir Yao, 2021;EscamillaAmbrosio et al, 2018;Fu et al, 2020;Hartmann et al, 2019;Hu et al, 2017;Younan et al, 2020;Kaur et al, 2020a;Kraemer et al, 2017;McCann et al, 2018;Swamy ir Kota...…”

Section: Duomenų Apdorojimo Uždaviniaiunclassified

“…Jutikliai krašto sluoksnyje yra daugiau ar mažiau mai tinami baterijomis. Išeikvojus akumuliatorių, išsijungia ir jutiklių mazgai, kurie yra labai svarbūs sistemos darbui skubios sveikatos priežiūros atvejais (Naresh et al, 2020).…”

Section: Rūko Kompiuterijos E Sveikatos Srityje Problemos Ir Ateities Kryptysunclassified

Fog Computing Technologies for Patient Sensor Networks – Trends, Issues and Future Directions

Kazlauskas

2021

Science - Future of Lithuania

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Advances in sensors and internet of things promise broad opportunities in many areas and one of them is health care. There are many solutions to manage health care data based on cloud computing. However, high response latency, large volumes of data transferred and security are the main issues of such approach. Fog computing provides immediate response and ways to process large amounts of data using real time analytics which includes machine learning and AI. Fog computing has not yet fully matured and there are still many challenges when managing health care data. It was chosen to investigate the most relevant ehealth fog computing topics by analyzing review articles to explain the fog computing model and present the current trends – fog computing ehealth technology application environments, deployment cases, infrastructure technologies, data processing challenges, problems and future directions. 38 scientific review articles published in the last 5 years were selected for analysis, filtering the most significant works with Web of Science article search tool.

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“…e instructor is responsible for the movement guidance, health care [7][8][9] guidance, and health assessment [10,11] of sports activities and various exercises. Due to the rigorous involvement of sports instructors, athletes can master scientific exercise methods and correct techniques, which eventually and effectively improve the level of exercise and achieve sports training.…”

Section: Introductionmentioning

confidence: 99%

Auxiliary Decision Support Model of Sports Training Based on Association Rules

Zhang

Wang

et al. 2021

Mobile Information Systems

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In sports or fitness training, nonstandard movements will affect the training effect and even lead to sports injuries. However, the standard movements of various sports activities need professional guidance, so it is difficult to find out whether the movements are standard or not. In recent years, body pose estimation has become a hot topic in computer vision research. A deep learning model can effectively identify the human nodes and movement trajectory in pictures or videos and evaluate the movements of the target human body. However, the movement process is generally covered by others or the situation of nearby personnel, which leads to the deviation of the movement recognition of the human body and affects the evaluation of the movement. Thus, it is unable to effectively correct the wrong movement, but will mislead the training personnel. Therefore, this paper proposes a novel decision support model for sports training based on association rules. We use posterior probability settings to reveal the weights of the discriminative ability of attribute items and set the classification performance to reflect the weights of three measures to evaluate credit contribution. Thus, the learning threshold setting reflects the weight of the decision-making ability of sports training. Furthermore, compared with traditional association rules, attribute items, frequent item sets, and classification rules that can improve the decision-making performance of sports training are discovered, which complement the deficiencies of different measures. Finally, using the weighted voting strategy to fuse the decision-making information of the classification rules, we can effectively assist in sports training so that the coach can work out corresponding countermeasures and realize scientific management.

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Internet of Things in Healthcare: Architecture, Applications, Challenges, and Solutions

Cited by 63 publications

References 10 publications

Internet of Medical Things (IoMT): Overview, Emerging Technologies, and Case Studies

Internet of Medical Things (IoMT): Overview, Emerging Technologies, and Case Studies

Fog Computing Technologies for Patient Sensor Networks – Trends, Issues and Future Directions

Auxiliary Decision Support Model of Sports Training Based on Association Rules

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