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

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.

Keywords

• body area networks
• channel gain/attenuation
• channel modeling
• galvanic coupling
• intra-body communications
• phantoms
• tissue mimicking materials
• ultralow power systems