Biometric Personal Classification with Deep Learning Using EMG Signals

Yıl 2023, , 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

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