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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)1421
Issue number5
StatePublished - Mar 6 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported in part by the U.S. National Institute of Justice under 2016-R2-CX-0014.


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