User identification system based on 2D CQT spectrogram of EMG with adaptive frequency resolution adjustment
Abstract 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 conv...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-51791-4 |
| _version_ | 1827377311293374464 |
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| author | Jae Myung Kim Gyuho Choi Sungbum Pan |
| author_facet | Jae Myung Kim Gyuho Choi Sungbum Pan |
| author_sort | Jae Myung Kim |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-08T12:37:12Z |
| format | Article |
| id | doaj.art-31f1bea2d70e45b0bfc6740485019afa |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T12:37:12Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-31f1bea2d70e45b0bfc6740485019afa2024-01-21T12:21:13ZengNature PortfolioScientific Reports2045-23222024-01-011411810.1038/s41598-024-51791-4User identification system based on 2D CQT spectrogram of EMG with adaptive frequency resolution adjustmentJae Myung Kim0Gyuho Choi1Sungbum Pan2Department of Electronics Engineering, Chosun UniversityDepartment of Artificial Intelligence Engineering, Chosun UniversityDepartment of Electronics Engineering, Chosun UniversityAbstract 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.https://doi.org/10.1038/s41598-024-51791-4 |
| spellingShingle | Jae Myung Kim Gyuho Choi Sungbum Pan User identification system based on 2D CQT spectrogram of EMG with adaptive frequency resolution adjustment Scientific Reports |
| title | User identification system based on 2D CQT spectrogram of EMG with adaptive frequency resolution adjustment |
| title_full | User identification system based on 2D CQT spectrogram of EMG with adaptive frequency resolution adjustment |
| title_fullStr | User identification system based on 2D CQT spectrogram of EMG with adaptive frequency resolution adjustment |
| title_full_unstemmed | User identification system based on 2D CQT spectrogram of EMG with adaptive frequency resolution adjustment |
| title_short | User identification system based on 2D CQT spectrogram of EMG with adaptive frequency resolution adjustment |
| title_sort | user identification system based on 2d cqt spectrogram of emg with adaptive frequency resolution adjustment |
| url | https://doi.org/10.1038/s41598-024-51791-4 |
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