Department of Electronics Engineering, Chosun University, Gwangju, 61452, Republic of Korea.
Department of Artificial Intelligence Engineering, Chosun University, Gwangju, 61452, Republic of Korea.
Sci Rep. 2024 Jan 16;14(1):1340. doi: 10.1038/s41598-024-51791-4.
User identification systems based on electromyogram (EMG) signals, generated inside the body in different signal patterns and exhibiting individual characteristics based on muscle development and activity, are being actively researched. However, nonlinear and abnormal signals constrain conventional user identification using EMG signals in improving accuracy by using the 1-D feature from each time and frequency domain. Therefore, multidimensional features containing time-frequency information extracted from EMG signals have attracted much attention to improving identification accuracy. We propose a user identification system using constant Q transform (CQT) based 2D features whose time-frequency resolution is customized according to EMG signals. The proposed user identification system comprises data preprocessing, CQT-based 2D image conversion, convolutional feature extraction, and classification by convolutional neural network (CNN). The experimental results showed that the accuracy of the proposed user identification system using CQT-based 2D spectrograms was 97.5%, an improvement of 15.4% and 2.1% compared to the accuracy of 1D features and short-time Fourier transform (STFT) based user identification, respectively.
基于肌电图（EMG）信号的用户识别系统正在被积极研究。这些信号在体内产生，呈现出不同的信号模式，并基于肌肉发育和活动表现出个体特征。然而，非线性和异常信号限制了传统的使用 EMG 信号的用户识别，因为它通过使用每个时间和频域的一维特征来提高准确性。因此，从 EMG 信号中提取的包含时频信息的多维特征引起了人们的极大关注，以提高识别准确性。我们提出了一种使用基于恒定 Q 变换（CQT）的二维特征的用户识别系统，其时频分辨率根据 EMG 信号进行定制。所提出的用户识别系统包括数据预处理、基于 CQT 的二维图像转换、卷积特征提取以及卷积神经网络（CNN）分类。实验结果表明，使用基于 CQT 的二维频谱图的用户识别系统的准确率为 97.5%，与一维特征和基于短时傅里叶变换（STFT）的用户识别的准确率相比，分别提高了 15.4%和 2.1%。
