Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning.

Ahmed E Khorshid, Ibrahim N Alquaydheb, Fadi Kurdahi, Roger Piqueras Jover, Ahmed Eltawil

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Reproducible benchmark of wavelet-enhanced intrabody communication biometric identification.Scientific reports · 2026
Article

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

5 authors.

Ahmed E KhorshidElectrical Engineering and Computer Science Department, University of California, Irvine, CA 92697, USA.

Ibrahim N AlquaydhebElectrical Engineering and Computer Science Department, University of California, Irvine, CA 92697, USA.

Fadi KurdahiElectrical Engineering and Computer Science Department, University of California, Irvine, CA 92697, USA.

Roger Piqueras JoverBloomberg LP, New York, NY 10022, USA.

Ahmed EltawilElectrical Engineering and Computer Science Department, University of California, Irvine, CA 92697, USA.

Funding

National Institute of Justice 2016-R2-CX-0014

6 · The paper itself

Abstract

In this paper, we propose and validate using the Intra-body communications channel as a biometric identity. Combining experimental measurements collected from five subjects and two multi-layer tissue mimicking materials' phantoms, different machine learning algorithms were used and compared to test and validate using the channel characteristics and features as a biometric identity for subject identification. An accuracy of 98.5% was achieved, together with a precision and recall of 0.984 and 0.984, respectively, when testing the models against subject identification over results collected from the total samples. Using a simple and portable setup, this work shows the feasibility, reliability, and accuracy of the proposed biometric identity, which allows for continuous identification and verification.

Indexed as

Machine LearningManikinsAlgorithmsBiometric IdentificationHumansReproducibility of ResultsWireless Technologybody area networkschannel gain/attenuationchannel modelinggalvanic couplingintra-body communicationsphantomstissue mimicking materialsultralow power systems

Identifiers

PMID32150911

DOI10.3390/s20051421

PMCPMC7085539

What OpenQuestion holds

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Registered trials

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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.