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

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 5; p. 1421
• Main Authors: Khorshid, Ahmed E., Alquaydheb, Ibrahim N., Kurdahi, Fadi, Jover, Roger Piqueras, Eltawil, Ahmed
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 05.03.2020; MDPI
• Subjects: Accuracy; Algorithms; Biometric Identification - methods; Biometrics; body area networks; channel gain/attenuation; channel modeling; Communication channels; Electrodes; galvanic coupling; Humans; Identification; intra-body communications; Machine Learning; Manikins; phantoms; Reproducibility of Results; Sensors; tissue mimicking materials; Transmitters; ultralow power systems; Wireless Technology; intra-body communications; phantoms; channel modeling; galvanic coupling; ultralow power systems; channel gain/attenuation; tissue mimicking materials; body area networks
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20051421

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.