Nearest Neighbor Classification of Binary Channel States for Secure Human Body Communication

Human body communication (HBC) is a short-range communication technique in which the human body is used as a data transmission channel. Although the HBC was originally proposed for connecting multiple mobile devices, it is also useful for quickly linking mobile and fixed devices. The latter feature can be applied to smart identification of persons. The HBC channel is formed by a capacitive coupling among the body, mobile and fixed devices, and earth. Consequently, if an outsider approaches the HBC system, that person inevitably becomes involved in the system because of the capacitive coupling. In this situation, a mobile device of the outsider accidentally transmits data signals and misidentification occurs. This is a serious problem in terms of communication system security. An effective approach for resolving this problem is to predict whether the outsider is involved in the HBC system based on signal information received by the fixed device. In this article, we present a method for correctly predicting the existence of the outsider involved in the HBC system based on channel gain features detected by the fixed device. As the problem can be viewed as a binary classification problem, we adopted ${k}$ -nearest neighbor method ( ${k}$ -NN), which is a supervised machine learning algorithm, for solving it. A key to correctly execute and evaluate ${k}$ -NN is to obtain the reliable channel gain data for training and evaluation. We utilized optical devices for correctly measuring the HBC channel gain and acquired 360 samples of the channel gain data. It was demonstrated that error-free classification was possible with the ${k}$ -NN classifier for the 360 samples. We obtained three key findings for reducing classification errors. First, ${k} =$ 1 is best for the ${k}$ -NN classifiers. Second, 1-norm is better than 2-norm for calculating error functions of ${k}$ -NN classifiers. Third, the preprocessing that includes partition and normalization of channel gain data is highly effective.