Biometric Personal Classification with Deep Learning Using EMG Signals

Yıl 2023, Cilt: 7 Sayı: 2, 156 - 161, 30.09.2023

Bekir Bilgin , Mehmet İsmail Gürsoy , Ahmet Alkan

https://doi.org/10.30516/bilgesci.1344337

Öz

Biometric person recognition systems are becoming increasingly important due to their use in places requiring high security. Since it includes the physical and behavioral characteristics of people, the iris structure, which is a traditional person recognition system, is more secure than methods such as fingerprints or speech. In this study, a deep learning-based person classification/recognition model is proposed. The Gesture Recognition and Biometrics ElectroMyogram (GrabMyo) dataset from the open access PhysioNet database was used. With the 28-channel EMG device, 10 people were asked to make a fist movement with their hand. During the fist movement, data were recorded with the EMG device from the arm and wrist for 5 seconds with a sampling frequency of 2048. The EMD method was chosen to determine the spectral properties of EMG signals. With the EMD method, 4 IMF signal vectors were obtained from the high frequency components of the EMG signals. The classification performance effect of the feature vector is increased by using statistical methods for each IMF signal vector. Feature vectors are classified with CNN and LSTM methods from deep learning algorithms. Accuracy, Precision, Sensitivity and F-Score parameters were used to determine the performance of the developed model. An accuracy value of 95.57% was obtained in the model developed with the CNN method. In the LSTM method, the accuracy value was 93.88%. It is explained that the deep learning model proposed in this study can be effectively used as a biometric person recognition system for person recognition or classification problems with the EMG signals obtained during the fist movement. In addition, it is predicted that the proposed model can be used effectively in the design of future person recognition systems.

Anahtar Kelimeler

EMG, Personal Classification, Empirical Mode Decomposition, CNN, LSTM

Kaynakça

• A. Raurale, S., McAllister, J., & Del Rincon, J. M. (2020). Real-Time Embedded EMG Signal Analysis for Wrist-Hand Pose Identification. IEEE Transactions on Signal Processing, 68, 2713–2723. https://doi.org/10.1109/TSP.2020.2985299
• Albaqami, H., Hassan, G. M., Subasi, A., & Datta, A. (2021). Automatic detection of abnormal EEG signals using wavelet feature extraction and gradient boosting decision tree. Biomedical Signal Processing and Control, 70, 102957. https://doi.org/10.1016/J.BSPC.2021.102957
• Fan, J., Jiang, X., Liu, X., Zhao, X., Ye, X., Dai, C., Akay, M., & Chen, W. (2022). Cancelable HD-SEMG Biometric Identification via Deep Feature Learning. IEEE Journal of Biomedical and Health Informatics, 26(4), 1782–1793. https://doi.org/10.1109/JBHI.2021.3115784
• Gaso, M. S., Cankurt, S., & Subasi, A. (2021). Electromyography Signal Classification Using Deep Learning. 2021 16th International Conference on Electronics Computer and Computation, ICECCO 2021. https://doi.org/10.1109/ICECCO53203.2021.9663803
• Gui, Q., Ruiz-Blondet, M. V., Laszlo, S., & Jin, Z. (2019). A survey on brain biometrics. ACM Computing Surveys, 51(6). https://doi.org/10.1145/3230632
• Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Snin, H. H., Zheng, Q., Yen, N. C., Tung, C. C., & Liu, H. H. (1998). The empirical mode decomposition and the Hubert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454(1971), 903–995. https://doi.org/10.1098/rspa.1998.0193
• Jamaluddin, F. N., Ibrahim, F., & Ahmad, S. A. (2023). A New Approach to Noninvasive-Prolonged Fatigue Identification Based on Surface EMG Time-Frequency and Wavelet Features. Journal of Healthcare Engineering, 2023, 13–16. https://doi.org/10.1155/2023/1951165
• Kang, P., Jiang, S., & Shull, P. B. (2023). Synthetic EMG Based on Adversarial Style Transfer can Effectively Attack Biometric-based Personal Identification Models. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 31, 2022.10.14.512221. https://doi.org/10.1109/TNSRE.2023.3303316
• Khan, M. U., Choudry, Z. A., Aziz, S., Naqvi, S. Z. H., Aymin, A., & Imtiaz, M. A. (2020). Biometric Authentication based on EMG Signals of Speech. 2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020, June, 2–6. https://doi.org/10.1109/ICECCE49384.2020.9179354
• Kim, J. S., Kim, M. G., & Pan, S. B. (2021). Two-step biometrics using electromyogram signal based on convolutional neural network-long short-term memory networks. Applied Sciences (Switzerland), 11(15). https://doi.org/10.3390/app11156824
• Kim, J. S., & Pan, S. B. (2017). A Study on EMG-based Biometrics. Journal of Internet Services and Information Security (JISIS), 7(2), 19–31. https://doi.org/http://dx.doi.org/10.22667/JISIS.2017.05.31.019
• Li, Q., Dong, P., & Zheng, J. (2020). Enhancing the security of pattern unlock with surface emg-based biometrics. Applied Sciences (Switzerland), 10(2). https://doi.org/10.3390/app10020541
• Lu, L., Mao, J., Wang, W., Ding, G., & Zhang, Z. (2020). A Study of Personal Recognition Method Based on EMG Signal. IEEE Transactions on Biomedical Circuits and Systems, 14(4), 681–691. https://doi.org/10.1109/TBCAS.2020.3005148
• Mishra, V. K., Bajaj, V., Kumar, A., & Singh, G. K. (2016). Analysis of ALS and normal EMG signals based on empirical mode decomposition. IET Science, Measurement and Technology, 10(8), 963–971. https://doi.org/10.1049/iet-smt.2016.0208
• Morikawa, S., Ito, S. I., Ito, M., & Fukumi, M. (2019). Personal authentication by lips EMG using dry electrode and CNN. 2018 IEEE International Conference on Internet of Things and Intelligence System, IOTAIS 2018, 180–183. https://doi.org/10.1109/IOTAIS.2018.8600859 Phinyomark, A., Limsakul, C., & Phukpattaranont, P. (2011). Application of wavelet analysis in EMG feature extraction for pattern classification. Measurement Science Review, 11(2), 45–52. https://doi.org/10.2478/v10048-011-0009-y Pradhan, A., He, J., & Jiang, N. (2022). Multi-day dataset of forearm and wrist electromyogram for hand gesture recognition and biometrics. Scientific Data, 9(1), 1–10. https://doi.org/10.1038/s41597-022-01836-y
• Ramírez-Arias, F. J., García-Guerrero, E. E., Tlelo-Cuautle, E., Colores-Vargas, J. M., García-Canseco, E., López-Bonilla, O. R., Galindo-Aldana, G. M., & Inzunza-González, E. (2022). Evaluation of Machine Learning Algorithms for Classification of EEG Signals. Technologies, 10(4), 79. https://doi.org/10.3390/technologies10040079
• Raurale, S. A., McAllister, J., & Rincon, J. M. Del. (2021). EMG Biometric Systems Based on Different Wrist-Hand Movements. IEEE Access, 9, 12256–12266. https://doi.org/10.1109/ACCESS.2021.3050704
• Shin, S., Jung, J., & Kim, Y. T. (2017). A study of an EMG-based authentication algorithm using an artificial neural network. Proceedings of IEEE Sensors, 2017-Decem, 1–3. https://doi.org/10.1109/ICSENS.2017.8234158
• Shin, S., Kang, M., Jung, J., & Kim, Y. T. (2021). Development of miniaturized wearable wristband type surface emg measurement system for biometric authentication. Electronics (Switzerland), 10(8). https://doi.org/10.3390/electronics10080923
• Shioji, R., Ito, S. I., Ito, M., & Fukumi, M. (2018). Personal authentication and hand motion recognition based on wrist EMG analysis by a convolutional neural network. 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018, 1172–1176. https://doi.org/10.1109/SCIS-ISIS.2018.00184
• Shioji, R., Ito, S., Ito, M., & Fukumi, M. (2017). Personal Authentication Based on Wrist EMG Analysis by a Convolutional Neural Network. 5th IIAE International Conference on Intelligent Systems and Image Processing, 12–18. https://doi.org/10.12792/icisip2017.006
• Taşar, B. (2022). Deep-BBiIdNet: Behavioral Biometric Identification Method Using Forearm Electromyography Signal. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-022-06909-z
• Venugopalan, S., Juefei-Xu, F., Cowley, B., & Savvides, M. (2015). Electromyograph and keystroke dynamics for spoof-resistant biometric authentication. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2015-Octob, 109–118. https://doi.org/10.1109/CVPRW.2015.7301326