Evidence map›Paper›PMID 39825696›Full record

ArticleBioelectromagnetics2025

Estimating Human Fat and Muscle Conductivity From 100 Hz to 1 MHz Using Measurements and Modelling.

Otto Kangasmaa, Ilkka Laakso, Gernot Schmid

Abstract read
In one paragraph

Article in Bioelectromagnetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Otto KangasmaaDepartment of Electrical Engineering and Automation, Aalto University, Espoo, Finland.ORCID http://orcid.org/0000-0002-7993-0010
Ilkka LaaksoDepartment of Electrical Engineering and Automation, Aalto University, Espoo, Finland.
Gernot SchmidSeibersdorf Labor GmbH, Seibersdorf, Austria.

Funding

This work was supported by the German Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) and on behalf of the Federal Office for Radiation Protection (BfS).
6 · The paper itself

Abstract

The electrical conductivity of human tissues is a major source of uncertainty when modelling the interactions between electromagnetic fields and the human body. The aim of this study is to estimate human tissue conductivities in vivo over the low-frequency range, from 30 Hz to 1 MHz. Noninvasive impedance measurements, medical imaging, and 3D surface scanning were performed on the forearms of ten volunteer test subjects. This data set was used to create subject-specific forearm models, numerically solve an electrostatic forward problem, after which the tissue conductivities could be estimated by solving a probabilistic inverse problem. The electrical conductivity of skeletal muscle was found to be highly anisotropic at frequencies below 10 kHz, with conductivities of 0.13 (95% credible interval (CrI): 0.10-0.16) S/m perpendicular and 0.56 (CrI: 0.52-0.60) S/m parallel to the muscle fibre direction. This anisotropy decreased with increasing frequency with these values being 0.65 (CrI: 0.48-1.00) S/m and 0.78 (CrI: 0.72-0.85) S/m at 1 MHz. The conductivity of subcutaneous fat was found to be almost constant across the considered frequency range, with values of 0.21 (CrI: 0.12-0.31) S/m and 0.22 (CrI: 0.07-0.37) S/m at 10 kHz and 1 MHz, respectively. Our study provides robust uncertainty bounds for human tissue conductivity values, which are crucial in the computational assessment of human electromagnetic field exposure. Additionally, our findings are applicable to other fields of modelling such as medical stimulation or measurement technologies.

Indexed as

Adipose TissueElectric ConductivityModels, BiologicalMuscle, SkeletalAdultAnisotropyElectric ImpedanceFemaleHumansMaleanisotropic musclebioimpedancelow frequencytissue electrical properties

Identifiers

PMID39825696
PMCPMC11742663

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.