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Open AccessArticle

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

1
Electrical Engineering and Computer Science Department, University of California, Irvine, CA 92697, USA
2
Bloomberg LP, New York, NY 10022, USA
3
Computer, Electrical and Mathematical Science and Engineering Division (CEMSE), King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(5), 1421; https://doi.org/10.3390/s20051421
Received: 1 February 2020 / Revised: 2 March 2020 / Accepted: 3 March 2020 / Published: 5 March 2020
(This article belongs to the Special Issue Wireless Body Area Networks for Health Applications)
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. View Full-Text
Keywords: body area networks; channel gain/attenuation; channel modeling; galvanic coupling; intra-body communications; phantoms; tissue mimicking materials; ultralow power systems body area networks; channel gain/attenuation; channel modeling; galvanic coupling; intra-body communications; phantoms; tissue mimicking materials; ultralow power systems
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Khorshid, A.E.; Alquaydheb, I.N.; Kurdahi, F.; Jover, R.P.; Eltawil, A. Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning. Sensors 2020, 20, 1421.

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