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Stochastic Bioimpedance-Based Channel Model of the Human Body for Galvanic Coupling

Aaron Roopnarine

Aaron Roopnarine

Department of Electrical & Computer Engineering, The University of the West IndiesAugustine, Trinidad & Tobago
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Roopnarine, Aaron
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Sean A. Rocke

Sean A. Rocke

Department of Electrical & Computer Engineering, The University of the West IndiesAugustine, Trinidad & Tobago
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Rocke, Sean A.
  
27. Dez. 2021
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Online veröffentlicht: 27. Dez. 2021
Seitenbereich: 117 - 124
Eingereicht: 03. Juni 2021
DOI: https://doi.org/10.2478/joeb-2021-0014
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© 2021 Aaron Roopnarine and Sean A. Rocke, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1

Electrode configuration for galvanic coupling HBC.
Electrode configuration for galvanic coupling HBC.

Figure 2

The transmission line model for the i th body segment.
The transmission line model for the i th body segment.

Figure 3

Algorithm implemented in MATLAB to determine the resultant distributions for ⌈ for the different combinations of PDFs for R, G and B.
Algorithm implemented in MATLAB to determine the resultant distributions for ⌈ for the different combinations of PDFs for R, G and B.

Figure 4

Sample PDF derived for the magnitude of element A of the resultant cascade, ⌈, for test case A with ΔzL = 0.1mm.
Sample PDF derived for the magnitude of element A of the resultant cascade, ⌈, for test case A with ΔzL = 0.1mm.

Figure 5

Frequency of Distribution Names (Considering the Top 3 Best Fit Distributions) for the Cascaded ABCD Random Variable Matrix Elements Considering both Magnitude and Phase.
Frequency of Distribution Names (Considering the Top 3 Best Fit Distributions) for the Cascaded ABCD Random Variable Matrix Elements Considering both Magnitude and Phase.

The percentage of times each distribution fell within the top 3 best-fit candidates for each element of the cascaded random variable ABCD matrix, ⌈, BS-Birnbaum-Saunders_ GEV-Generalized Extreme Value, GP-Generalized Pareto_

Distribution Name A B C D
Magnitude Phase Magnitude Phase Magnitude Phase Magnitude Phase
Inverse Gaussian 15.19 2.22 13.7 1.48 12.22 2.22 12.96 2.22
BS 11.85 2.22 11.48 1.85 8.15 2.22 12.22 2.22
Lognormal 15.19 2.22 15.93 1.85 12.22 2.22 12.59 2.22
Rician 8.89 4.44 9.63 2.22 2.96 6.67 9.63 4.44
Normal 8.52 10 10 8.89 9.63 12.22 7.04 10
Nakagami 10 4.44 10.74 2.59 10.74 6.3 11.48 4.44
Gamma 3.7 0 1.48 0.74 5.19 0 5.93 0
Log-logistic 8.89 0 11.11 0 8.15 0 8.52 0
Weibull 4.44 0 4.44 0 4.44 0 4.44 0
GEV 6.67 8.89 5.56 7.78 6.67 8.89 6.67 8.89
GP 4.44 22.22 0 25.56 6.3 20 4.44 22.22
Logistic 1.11 5.56 1.48 4.81 2.59 4.44 1.85 5.19
T Location-Scale 1.11 3.33 4.44 2.22 0 3.7 2.22 3.33
Beta 0 0 0 0.37 10.74 0 0 0
Extreme Value 0 17.78 0 21.11 0 15.56 0 17.78
Rayleigh 0 16.67 0 18.52 0 15.56 0 17.04

The PDFs assumed for R, G and B_

PDF Test Case R G B
A N(2.38E + 03, 2.38E + 03) N(4.20E − 04, 2.40e − 04) N(2.85E + 07, 2.85E + 07)
B N(2.38E + 03, 4.76E + 03) N(4.20E − 04, 8.40E − 04) N(2.85E + 07, 5.70E + 07)
C N(2.38E + 03, 7.14E + 03) N(4.20E − 04, 1.26E − 03) N(2.85E + 07, 8.55E + 07)
D U(0, 2.38E + 03) U(0, 2.40e − 04) U(0, 2.85E + 07)
E U(0, 4.76E + 03) U(0, 8.40E − 04) U(0, 5.70E + 07)
F U(0, 7.14E + 03) U(0, 1.26E − 03) U(0, 8.55E + 07)
G Gamma(2.3810E + 03, 1) Gamma(4.2000E − 04, 1) Gamma(2.8501E + 07, 1)
H Gamma(1.1905E + 03, 2) Gamma(2.1000E − 04, 2) Gamma(1.4250E + 07, 2)
I Gamma(7.9365E + 02, 3) Gamma(1.4000E − 04, 3) Gamma(9.5002E + 06, 3)
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