@Article{cmc.2022.025213,
AUTHOR = {Jae Myung Kim, Gyu Ho Choi, Min-Gu Kim, Sung Bum Pan},
TITLE = {User Recognition System Based on Spectrogram Image Conversion Using EMG Signals},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {72},
YEAR = {2022},
NUMBER = {1},
PAGES = {1213--1227},
URL = {http://www.techscience.com/cmc/v72n1/46931},
ISSN = {1546-2226},
ABSTRACT = {Recently, user recognition methods to authenticate personal identity has attracted significant attention especially with increased availability of various internet of things (IoT) services through fifth-generation technology (5G) based mobile devices. The EMG signals generated inside the body with unique individual characteristics are being studied as a part of next-generation user recognition methods. However, there is a limitation when applying EMG signals to user recognition systems as the same operation needs to be repeated while maintaining a constant strength of muscle over time. Hence, it is necessary to conduct research on multidimensional feature transformation that includes changes in frequency features over time. In this paper, we propose a user recognition system that applies EMG signals to the short-time fourier transform (STFT), and converts the signals into EMG spectrogram images while adjusting the time-frequency resolution to extract multidimensional features. The proposed system is composed of a data pre-processing and normalization process, spectrogram image conversion process, and final classification process. The experimental results revealed that the proposed EMG spectrogram image-based user recognition system has a 95.4% accuracy performance, which is 13% higher than the EMG signal-based system. Such a user recognition accuracy improvement was achieved by using multidimensional features, in the time-frequency domain.},
DOI = {10.32604/cmc.2022.025213}
}