Survey on Medical Imaging of Electrical Impedance Tomography (EIT) by Variable Current Pattern Methods

Adam, Edriss Eisa Babikir; Sathesh,

doi:10.36548/jismac.2021.2.002

Cited by 70 publications

(1 citation statement)

References 28 publications

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“…Electrical impedance tomography (EIT) is a non-invasive imaging technology, which restores internal impedance distribution by measuring the response information of the surface electrode pair around the object [ 12 ]. In the medical field, it is mainly used for imaging of the chest, brain, and abdominal regions [ 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Hand Gesture Recognition Using Electrical Impedance Tomography

Sun

Wang

et al. 2022

Sensors

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Electrical impedance tomography (EIT) has been applied in the field of human-computer interaction due to its advantages including the fact that it is non-invasive and has both low power consumption and a low cost. Previous work has focused on static gesture recognition based on EIT. Compared with static gestures, dynamic gestures are more informative and can achieve more functions in human-machine collaboration. In order to verify the feasibility of dynamic gesture recognition based on EIT, a traditional excitation drive pattern is optimized in this paper. The drive pattern of the fixed excitation electrode is tested for the first time to simplify the measurement process of the dynamic gesture. To improve the recognition accuracy of the dynamic gestures, a dual-channel feature extraction network combining a convolutional neural network (CNN) and gated recurrent unit (GRU), namely CG-SVM, is proposed. The new center distance loss is designed in order to simultaneously supervise the intra-class distance and inter-class distance. As a result, the discriminability of the confusing data is improved. With the new excitation drive pattern and classification network, the recognition accuracy of different interference data has increased by 2.7~14.2%. The new method has stronger robustness, and realizes the dynamic gesture recognition based on EIT for the first time.

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Section: Introductionmentioning

confidence: 99%

Dynamic Hand Gesture Recognition Using Electrical Impedance Tomography

Sun

Wang

et al. 2022

Sensors

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The role of low‐power wide‐area network technologies in Internet of Things: A systematic and comprehensive review

Mousavi

Khademzadeh

Rahmani

2021

Int J Communication

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Summary With the quick progress of wireless technologies, the Internet of Things (IoT) has been recognized as the main part of daily people's lives that makes their life more convenient by utilizing a diverse range of smart devices. Over the last decade, various useful and forthcoming application fields have been developed by taking advantage of the IoT concept. In this regard, LPWAN technologies such as LoRa, Sigfox, and NB‐IoT play an important role in advancing. These technologies are suitable wireless communication protocols for battery‐powered IoT objects that enable long‐distance communication in low‐power devices at a low operation cost. Some review papers have investigated the LPWAN technology in the IoT from different perspectives. However, the lack of detailed and systematic study outlining the role of these technologies in different IoT applications is very clear. Consequently, this gap inspired us to write the current study. The current works are classified into three main classes, including smart city, home automation, and smart healthcare. Besides, the involved models in the smart city group are categorized into five subgroups, including smart monitoring, smart metering, air pollution, smart agriculture, and smart parking. The selected works are reviewed, and their main features, including main idea, coverage range, sensor types, processing board, frequency band, communication protocols, power consumption analysis, and achievement, are specified. Generally, our main aim is to describe the challenging problems of applying LPWAN to various IoT applications, specify the efficient models, and recommend some hints for upcoming studies.

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