Flexible RFID tag with circular CSRR for body-area applications

Morais, Samuel Medeiros Araujo; Serres, Alexandre; Araujo, Jessyca Iasmyn Lucena; Albuquerque, Camila Caroline Rodrigues de; Silva, Carolina C. P. e; Ferreira, Marina L.; Serres, Georgina K. F.; Pereira, Helder A.; Oliveira, Alexandre M. de

doi:10.1109/inscit.2019.8868320

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…RFID is a contactless technique for the automatic identification of targets using radiofrequency with 2-way data communication in various zones identified by their unique names [ 37 , 38 ]. RFID consists of 3 parts, namely, the reader, database management system, and radio frequency electronic tag [ 39 ].…”

Section: Related Workmentioning

confidence: 99%

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Srivastava

Routray

Ahmad

et al. 2022

Computational Intelligence and Neuroscience

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Internet of Medical Thing (IoMT) is the most emerging era of the Internet of Thing (IoT), which is exponentially gaining researchers’ attention with every passing day because of its wide applicability in Smart Healthcare systems (SHS). Because of the current pandemic situation, it is highly risky for an individual to visit the doctor for every small problem. Hence, using IoMT devices, we can easily monitor our day-to-day health records, and thereby initial precautions can be taken on our own. IoMT is playing a crucial role within the healthcare industry to increase the accuracy, reliability, and productivity of electronic devices. This research work provides an overview of IoMT with emphasis on various enabling techniques used in smart healthcare systems (SHS), such as radio frequency identification (RFID), artificial intelligence (AI), and blockchain. We are providing a comparative analysis of various IoMT architectures proposed by several researchers. Also, we have defined various health domains of IoMT, including the analysis of different sensors with their application environment, merits, and demerits. In addition, we have figured out key protocol design challenges, which are to be considered during the implementation of an IoMT network-based smart healthcare system. Considering these challenges, we prepared a comparative study for different data collection techniques that can be used to maintain the accuracy of collected data. In addition, this research work also provides a comprehensive study for maintaining the energy efficiency of an AI-based IoMT framework based on various parameters, such as the amount of energy consumed, packet delivery ratio, battery lifetime, quality of service, power drain, network throughput, delay, and transmission rate. Finally, we have provided different correlation equations for finding the accuracy and efficiency within the IoMT-based healthcare system using artificial intelligence. We have compared different data collection algorithms graphically based on their accuracy and error rate. Similarly, different energy efficiency algorithms are also graphically compared based on their energy consumption and packet loss percentage. We have analyzed our references used in this study, which are graphically represented based on their distribution of publication year and publication avenue.

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Section: Related Workmentioning

confidence: 99%

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Srivastava

Routray

Ahmad

et al. 2022

Computational Intelligence and Neuroscience

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“…The lowcost ink-jet printed RFID tag antenna proposed in [12] can monitor the patient's medication dose, saving a lot of time and resources. In [13], considering the degradation of the reading range of the RFID tag when it is close to human tissue, a flexible RFID tag is proposed to expand its reading range. In real life, RFID tags will inevitably lose data and reduce recognition accuracy due to signal loss, tag breakage, channel conflict and other problems.…”

Section: B Technologies Of Iomt 1) Radio Frequency Identification (Rmentioning

confidence: 99%

Edge-Cloud Computing and Artificial Intelligence in Internet of Medical Things: Architecture, Technology and Application

et al. 2020

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With the booming development of medical informatization and the ubiquitous connections in the fifth generation mobile communication technology (5G) era, the heterogeneity and explosive growth of medical data have brought huge challenges to data access, security and privacy, as well as information processing in Internet of Medical Things (IoMT). This article provides a comprehensive review of how to realize the timely processing and analysis of medical big data and the sinking of high-quality medical resources under the constraints of the existing medical environment and medical-related equipment. We mainly focus on the advantages brought by the cloud computing, edge computing and artificial intelligence technologies to the IoMT. We also explore how to rationalize the use of medical resources and the security and privacy of medical data, so that high-quality medical services can be provided to patients. Finally, we discuss the current challenges and possible future research directions in the edge-cloud computing and artificial intelligence related IoMT. INDEX TERMS Internet of medical things (IoMT), deep learning, edge of computing, computation offloading.

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“…Besides that, for its potential use on body, the tag must be designed in a planar form on flexible substrates. The body proximity causes absorption, diffraction and scattering of electromagnetic waves, which then changes in the antenna impedance, resonant frequency and read range [7]. Such performance variation may be alleviated by the integration of metamaterial elements in improving the tag antenna's performance [8].…”

Section: Introductionmentioning

confidence: 99%

A Flexible and Compact Metamaterial UHF RID Tag for Remote Sensing in Human Health

Fazilah

Jusoh

Sabapathy

et al. 2020

2020 International Conference on UK-China Emerging Technologies (UCET)

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This paper presents a miniaturized UHF RFID tag antenna with increased gain using meander line techniques and metamaterial (MTM). The designed tag operates in the UHF RFID frequency band ranging from 860 to 960 MHz. It comprises of meandered lines with two hexagonal split ring resonators (H-SRRs) MTM cells. It is designed on a photo paper as its substrate which is 0.27 mm thick, with a dielectric constant of 3.2 and loss tangent of 0.05. Next, an RFID tag (NXP SL3S1213 UCODE G2iL chip) with an impedance of 23-j224 Ω is integrated with the proposed antenna to assess its performance in terms of reflection coefficient, antenna gain and maximum reading range. The overall size of the tag is 92 mm x 26 mm.

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Flexible RFID tag with circular CSRR for body-area applications

Cited by 3 publications

References 18 publications

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Edge-Cloud Computing and Artificial Intelligence in Internet of Medical Things: Architecture, Technology and Application

A Flexible and Compact Metamaterial UHF RID Tag for Remote Sensing in Human Health

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