Human Body Modelling for Wireless Body Area Network Optimization

Januszkiewicz, Łukasz; Hausman, Sławomir; Barba, Paolo Di

doi:10.23919/eucap48036.2020.9136084

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…Numerical models of the human body are widely used to design wireless systems that operate in this complex environment [ 20 ]. Depending on the exact application of such models, they differ in shape and internal structure [ 21 ]. They can be made of one material (homogeneous models) or from many materials of different electric properties (heterogeneous models).…”

Section: Methodsmentioning

confidence: 99%

Wireless Capsule Endoscope Localization with Phase Detection Algorithm and Adaptive Body Model

Januszkiewicz

2022

Sensors

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Wireless capsule endoscopes take and send photos of the human digestive tract, which are used for medical diagnosis. The capsule’s location enables exact identification of the regions with lesions. This can be carried out by analyzing the parameters of the electromagnetic wave received from the capsule. Because the human body is a complex heterogeneous environment that impacts the propagation of wireless signals, determining the distance between the transmitter and the receiver based on the received power level is challenging. An enhanced approach of identifying the location of endoscope capsules using a wireless signal phase detection algorithm is presented in this paper. For each capsule position, this technique uses adaptive estimation of human body model permittivity. This approach was tested using computer simulations in Remcom XFdtd software using a numerical, heterogeneous human body model, as well as measurements with physical phantom. The type of transmitting antenna employed in the capsule also has a significant impact on the suggested localization method’s accuracy. As a result, the helical antenna, which is smaller than the dipole, was chosen as the signal’s source. For both the numerical and physical phantom studies, the proposed technique with adaptive body model enhances localization accuracy by roughly 30%.

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Section: Methodsmentioning

confidence: 99%

Wireless Capsule Endoscope Localization with Phase Detection Algorithm and Adaptive Body Model

Januszkiewicz

2022

Sensors

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“…In this paper, we propose a convolutional neural network (CNN) for modeling the power density map in a very complex domain as the human body is. Since full-wave simulations are computationally expensive [ 5 ], the proposed method could be a promising solution to this problem.…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic Wave Absorption in the Human Head: A Virtual Sensor Based on a Deep-Learning Model

Barba

Januszkiewicz

Kawecki

et al. 2023

Sensors

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Determining the amount of electromagnetic wave energy absorbed by the human body is an important issue in the analysis of wireless systems. Typically, numerical methods based on Maxwell’s equations and numerical models of the body are used for this purpose. This approach is time-consuming, especially in the case of high frequencies, for which a fine discretization of the model should be used. In this paper, the surrogate model of electromagnetic wave absorption in human body, utilizing Deep-Learning, is proposed. In particular, a family of data from finite-difference time-domain analyses makes it possible to train a Convolutional Neural Network (CNN), in view of recovering the average and maximum power density in the cross-section region of the human head at the frequency of 3.5 GHz. The developed method allows for quick determination of the average and maximum power density for the area of the entire head and eyeball areas. The results obtained in this way are similar to those obtained by the method based on Maxwell’s equations.

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