# Study on Noise Reduction and Data Generation for sEMG Spectrogram Based User Recognition
## 4 Experimental Methods and Results
As shown in Figure 13, when the sEMG spectrograms were generated using the window length of 256, the user identification accuracy for 40 subjects was 78.7% for the raw signals before resting state signal removal and 85.4% for the signals after resting state signal removal, indicating a 6.7% improvement after removing the static rest signal that did not contain muscle information. Furthermore, the amount of training data increased through matching pursuit after the resting state signals were removed to address the insufficient data problem of the sEMG database. The sEMG signals were generated with a similarity of 90-99% for the signals of each motion; the user identification performance was evaluated after augmenting the amount of data training data by 10 times from the existing insufficient data. When the number of insufficient training data was increased by 10 times, the sEMG spectrogram improved by more than 10%, showing 96.1% of user recognition performance, and it was confirmed that when the number of training data was increased by 10 times or more, it converged to a constant value. Figure 12: Identification performance based on changes in window length. Figure 13: Identification performance based on preprocessing. For performance comparison, the user identification performance was compared using the DenseNet201 neural network, which is composed of a deep neural network, and the MobileNet-v2 neural network, which can be applied to a mobile environment by reducing the computational cost and model size. Figure 14 illustrates the user identification performance using the raw signals and the user identification performance after noise removal and data generation. In this paper, the number of learning data is increased tenfold by removing the resting signal included in the sEMG signal and applying the matching pursuit. In addition, the time-frequency resolution of STFT suitable for sEMG signals was set to 256 through an experiment and converted into an sEMG spectrogram, and then the deep learning-based CNN was directly constructed to confirm the user identification accuracy. The batch size of CNN was set to 128, maxEpochs to 150, and filter size to 3 \(\times\) 3. As a result of the experiment, when the method proposed in this paper was applied, the user recognition accuracy was 96.1%. Accuracy was improved by 22.4% compared with before increasing the training data, and accuracy by 17.4% was improved compared with when a one-dimensional signal was used as an input. When the user recognition accuracy was checked using MobileNet-v2 and DenseNet201, as well as the directly constructed CNN, it was confirmed that the user recognition accuracy was improved in the directly constructed CNN. In addition, the identification accuracy was compared with the previous study using the same Ninapro DB2. [PERSON] [7] and [PERSON] [17] conducted pattern identification by converting a one-dimensional sEMG signal into a spectrogram. [PERSON] compared and analyzed the accuracy according to the feature extraction method and confirmed the identification accuracy of 77% using the spectrogram-based SVM. It was demonstrated that sEMG signals can be efficiently analyzed when the spectrogram, a multidimensional feature extraction method with time-frequency information, is applied rather than the time-domain feature extraction method, which is a one-dimensional analysis method. In addition, [PERSON] compared and analyzed the accuracy according to the classifier and confirmed the identification  
Figure 14: User identification performance comparison. accuracy of 79.4% using the spectrogram-based CNN-LSTM. The deep learning CNN-LSTM network showed higher identification accuracy than the existing machine learning SVM. In this paper, it was confirmed that the user recognition accuracy was improved to 96.1% of identification accuracy when the learning data were increased by applying the matching pursuit that can generate a signal to the sEMG signal from which the resting signal was removed, and the STFT-based time-frequency feature was used.