Personal Identification Using Long Short-Term Memory with Efficient Features of Electromyogram Biomedical Signals
This study focuses on personal identification using bidirectional long short-term memory (LSTM) with efficient features from electromyogram (EMG) biomedical signals. Personal identification is performed by comparing and analyzing features that can be stably identified and are not significantly affec...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/20/4192 |
| _version_ | 1797574058860085248 |
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| author | Yeong-Hyeon Byeon Keun-Chang Kwak |
| author_facet | Yeong-Hyeon Byeon Keun-Chang Kwak |
| author_sort | Yeong-Hyeon Byeon |
| collection | DOAJ |
| description | This study focuses on personal identification using bidirectional long short-term memory (LSTM) with efficient features from electromyogram (EMG) biomedical signals. Personal identification is performed by comparing and analyzing features that can be stably identified and are not significantly affected by noise. For this purpose, 13 efficient features, such as enhanced wavelength, zero crossing, and mean absolute value, were obtained from EMG signals. These features were extracted from segmented signals of a specific length. Then, the bidirectional LSTM was trained on the selected features as sequential data. The features were ranked based on their classification performance. Finally, the most effective features were selected, and the selected features were connected to achieve an improved classification rate. Two public EMG datasets were used to evaluate the proposed model. The first database was acquired from eight-channel Myo bands and was composed of EMG signals from 10 varying motions of 50 individuals. The total numbers of segments for the training and test sets were 30,000 and 20,000, respectively. The second dataset consisted of ten arm motions acquired from 40 individuals. A performance comparison of the dataset revealed that the proposed method exhibited good performance and efficiency compared to other well-known methods. |
| first_indexed | 2024-03-10T21:17:51Z |
| format | Article |
| id | doaj.art-b3271b5c5bfe4e9abcdf0734c0be3dd3 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T21:17:51Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-b3271b5c5bfe4e9abcdf0734c0be3dd32023-11-19T16:18:10ZengMDPI AGElectronics2079-92922023-10-011220419210.3390/electronics12204192Personal Identification Using Long Short-Term Memory with Efficient Features of Electromyogram Biomedical SignalsYeong-Hyeon Byeon0Keun-Chang Kwak1Interdisciplinary Program in IT-Bio Convergence System, Department of Electronics Engineering, Chosun University, Gwangju 61452, Republic of KoreaInterdisciplinary Program in IT-Bio Convergence System, Department of Electronics Engineering, Chosun University, Gwangju 61452, Republic of KoreaThis study focuses on personal identification using bidirectional long short-term memory (LSTM) with efficient features from electromyogram (EMG) biomedical signals. Personal identification is performed by comparing and analyzing features that can be stably identified and are not significantly affected by noise. For this purpose, 13 efficient features, such as enhanced wavelength, zero crossing, and mean absolute value, were obtained from EMG signals. These features were extracted from segmented signals of a specific length. Then, the bidirectional LSTM was trained on the selected features as sequential data. The features were ranked based on their classification performance. Finally, the most effective features were selected, and the selected features were connected to achieve an improved classification rate. Two public EMG datasets were used to evaluate the proposed model. The first database was acquired from eight-channel Myo bands and was composed of EMG signals from 10 varying motions of 50 individuals. The total numbers of segments for the training and test sets were 30,000 and 20,000, respectively. The second dataset consisted of ten arm motions acquired from 40 individuals. A performance comparison of the dataset revealed that the proposed method exhibited good performance and efficiency compared to other well-known methods.https://www.mdpi.com/2079-9292/12/20/4192biometricselectromyogramlong short-term memoryoptimization |
| spellingShingle | Yeong-Hyeon Byeon Keun-Chang Kwak Personal Identification Using Long Short-Term Memory with Efficient Features of Electromyogram Biomedical Signals Electronics biometrics electromyogram long short-term memory optimization |
| title | Personal Identification Using Long Short-Term Memory with Efficient Features of Electromyogram Biomedical Signals |
| title_full | Personal Identification Using Long Short-Term Memory with Efficient Features of Electromyogram Biomedical Signals |
| title_fullStr | Personal Identification Using Long Short-Term Memory with Efficient Features of Electromyogram Biomedical Signals |
| title_full_unstemmed | Personal Identification Using Long Short-Term Memory with Efficient Features of Electromyogram Biomedical Signals |
| title_short | Personal Identification Using Long Short-Term Memory with Efficient Features of Electromyogram Biomedical Signals |
| title_sort | personal identification using long short term memory with efficient features of electromyogram biomedical signals |
| topic | biometrics electromyogram long short-term memory optimization |
| url | https://www.mdpi.com/2079-9292/12/20/4192 |
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