This study proposed and validated 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 percent 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 enables continuous identification and verification. (publisher abstract modified)
Downloads
Similar Publications
- Differentiation of Regioisomeric N-Substituted Meta-Chlorophenylpiperazine Derivatives
- I studied ShotSpotter in Chicago and Kansas City – Here’s What People in Detroit and the More Than 167 Other Cities and Towns Using This Technology Should Know
- AI R&D to Support Community Supervision: Integrated Dynamic Risk Assessment for Community Supervision (IDRACS), Final Report