# Study on Noise Reduction and Data Generation for sEMG Spectrogram Based User Recognition
## 3 Proposed sEMG Spectrogram-Based User Identification
### User Identification Using sEMG Spectrogram  
In this study, multidimensional features were extracted from the preprocessed and normalized 1D sEMG signals by applying the STFT, a time-frequency feature extraction method. The STFT is a method that compensates for the disadvantages of the existing FFT, a frequency-domain feature extraction method; it enables the extraction of multidimensional features, including time and frequency, by analyzing the frequency components at a desired time point for signals that change over time [22]. The application of the STFT is expressed as shown in Equation (2). \[\mathcal{X}(\mathcal{R},\omega)=\int\limits_{-\infty}^{\infty}x(\ell)\omega( \ell-\mathcal{R})e^{-i\omega t}\,\mathcal{\alpha}\ell=s \tag{2}\]  
Figure 7: 12-channel synthesis using temporal domain. The spectrogram transformation is performed based on the FFT length using the input signal \(\mathcal{X}(\ell)\) and the window function \(\omega(\ell)\), where \(\mathcal{R}\) denotes the window length, \(\omega\) the angular frequency, and s the spectrogram value. Hence, the frequency information over time can be included by applying Equation (1) to 1D sEMG signals, such that multidimensional features containing time-frequency information can be extracted. In the process, the temporal resolution was enhanced by applying a 50% overlap, because both the time and frequency resolutions cannot be improved simultaneously. Figure 8 illustrates the result of transforming the extracted multidimensional features into the sEMG spectrogram. The transformed 2D sEMG spectrogram images were derived by adjusting the window length \(\mathcal{R}\) to verify the time-frequency resolution based on the change in the window length \(\mathcal{R}\), which is a parameter used in the STFT. The minimum value of the window length \(\mathcal{R}\) was set to 64, and it was increased by two-fold increments until the maximum length of 512 was attained. The change in the frequency resolution increases and the time resolution decreases based on the increases in the window length as illustrated by the spectrograms shown in Figure 9 [23]. In the final user identification, a typical deep learning CNN based on a 2D image input was employed. The constructed CNN comprised three convolutional layers, two max pooling layers, two fully connected layers, and a ReLu activation function. The final classification was proceeded by the softmax function. Figure 10 illustrates the overall network structure. The filter size of the CNN's convolutional layers was set to 3 \(\times\) 3, whereas those of the pooling layers and stride were set to 2 \(\times\) 2 and 2, respectively. The maximum number of iterations was set to 150, the initial weight of each layer was set to random, and the user was identified in the output layer by the softmax function in the final step [21]. Figure 8: Transformation of sEMG signal into spectrogram. Figure 9: sEMG spectrograms based on changes in STFT window length. Furthermore, DenseNet201, which is designed with a deep neural network, and MobileNet-v2 network, which can be applied to limited environments without a high-performance computer, were used in addition to the CNN constructed in this study to compare the identification performance. DenseNet201 was designed with a densely connected CNN structure. This model improves the flow between layers by connecting the feature maps of the previous layer with the inputs of the subsequent layer. Such a structure is applied to supplement the problem of information loss that occurs as the information on input passes through multiple layers with the increase in the depth of the network. Therefore, DenseNet201 can improve the vanishing gradient, strengthen feature propagation, encourage feature reuse, and reduce the number of parameters [24]. MobileNet is a network designed based on studies regarding lightweight networks that can be used in a limited environment without requiring a high-performance computer.