Electrical Engineering and Computer Science Department, University of California, Irvine, CA 92697, USA.
Bloomberg LP, New York, NY 10022, USA.
Sensors (Basel). 2020 Mar 5;20(5):1421. 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.
在本文中，我们提出并验证了利用体域内通信通道作为生物识别特征。我们结合了从五个主体和两个多层组织模拟材料的幻影中收集的实验测量数据，使用不同的机器学习算法进行测试和验证，以利用通道特征作为主体识别的生物识别特征。当我们将模型对从所有样本中收集的结果进行主体识别测试时，我们获得了 98.5%的准确率，同时具有 0.984 的精度和召回率。使用简单便携的设置，本工作展示了所提出的生物识别特征的可行性、可靠性和准确性，允许进行连续识别和验证。
