<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study explores the practical application and impact of bioimpedance analysis in mobile devices for monitoring human health. The objective of the study is to propose a feasible application of non-invasive bioimpedance analysis by using the tetrapolar electrode connection method and the Cole–Cole model. Bioimpedance measurements and the calculation of electrical parameters are performed using ANSYS HFSS software for theoretical calculations and digital signal processing technology for real-time measurements using hardware devices. The study focuses on a model of the front arm, including tissues such as bone, fat, muscles, arteries and skin, with glucose concentrations as test cases. The simulated characteristic impedance with the ANSYS HFSS software package at 125 kHz varied from 315.8 Ω to 312.6 Ω, and the measured forearm characteristic impedance with hardware varied from 150.1 Ω to 151.3 Ω. The measured characteristic impedance when the heart is in systole and diastole also differed, with a difference of about 0.85% of the maximum impedance measured. The study demonstrates the potential of non-invasive bioimpedance analysis to address health issues such as obesity and heart disease. It also highlights its usefulness as a non-invasive alternative for measuring glucose concentration in diabetic patients to reduce the risk of infection. The findings indicate the feasibility of using bioimpedance analysis in mobile devices for health monitoring purposes.</p>
</abstract>

This study explores the practical application and impact of bioimpedance analysis in mobile devices for monitoring human health. The objective of the study is to propose a feasible application of non-invasive bioimpedance analysis by using the tetrapolar electrode connection method and the Cole–Cole model. Bioimpedance measurements and the calculation of electrical parameters are performed using ANSYS HFSS software for theoretical calculations and digital signal processing technology for real-time measurements using hardware devices. The study focuses on a model of the front arm, including tissues such as bone, fat, muscles, arteries and skin, with glucose concentrations as test cases. The simulated characteristic impedance with the ANSYS HFSS software package at 125 kHz varied from 315.8 Ω to 312.6 Ω, and the measured forearm characteristic impedance with hardware varied from 150.1 Ω to 151.3 Ω. The measured characteristic impedance when the heart is in systole and diastole also differed, with a difference of about 0.85% of the maximum impedance measured. The study demonstrates the potential of non-invasive bioimpedance analysis to address health issues such as obesity and heart disease. It also highlights its usefulness as a non-invasive alternative for measuring glucose concentration in diabetic patients to reduce the risk of infection. The findings indicate the feasibility of using bioimpedance analysis in mobile devices for health monitoring purposes.

Non-Invasive Analysis of the Bioelectrical Impedance of a Human Forearm

Acta Mechanica et Automatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Non-Invasive Analysis of the Bioelectrical Impedance of a Human Forearm"}

This study explores the practical application and impact of bioimpedance analysis in mobile devices for monitoring human health. The objective of the study is...

Non-Invasive Analysis of the Bioelectrical Impedance of a Human Forearm

Online veröffentlicht: 25. Juli 2024

Seitenbereich: 496 - 504

Eingereicht: 02. Apr. 2023

Akzeptiert: 03. Nov. 2023

DOI: https://doi.org/10.2478/ama-2024-0053

Schlüsselwörter
non-invasive, bioimpedance, digital signal processing, human tissues

© 2024 Darius Plonis et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Non-Invasive Analysis of the Bioelectrical Impedance of a Human Forearm

Online veröffentlicht: 25. Juli 2024

Seitenbereich: 496 - 504

Eingereicht: 02. Apr. 2023

Akzeptiert: 03. Nov. 2023

DOI: https://doi.org/10.2478/ama-2024-0053

Schlüsselwörternon-invasive, bioimpedance, digital signal processing, human tissues

© 2024 Darius Plonis et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
non-invasive, bioimpedance, digital signal processing, human tissues