Study on Noise Reduction and Data Generation for sEMG Spectrogram Based User Recognition

Abstract

With the spread of the modern media industry, harmful genre contents are indiscriminately disseminated to teenagers. The password identification method used to block sensational and violent genre content has become a problem that teenagers can easily steal. Therefore, a user identification method with less risk of theft and hacking is required. The surface EMG (sEMG) signal, which is an electrical signal generated inside the body and has individual features, is being studied as a next-generation user identification method. sEMG involves measuring an individual’s unique muscular strength activated over time as digital signals, thus giving it the advantage of generating different signal patterns. However, it is difficult to constantly and repeatedly acquire each motion signal and the number of repetitions for each motion is insufficient, thus there is a limit to improving user identification accuracy. In this paper, we propose a user identification system that solves the problem of insufficient data by applying the matching pursuit that enables signal generation to the sEMG signal from which the resting signal has been removed and improves classification accuracy by extracting STFT-based time–frequency features. As a result of the experiment, the user identification accuracy of the sEMG spectrogram with the resting state signal removed was 85.4%. In addition, when the training data were increased through data generation, the accuracy was improved, showing a user identification accuracy of 96.1%. Improved user recognition accuracy was confirmed when the training data of the sEMG signal from which the resting signal was removed were increased and multidimensional features including time–frequency were used.