# Study on Noise Reduction and Data Generation for sEMG Spectrogram Based User Recognition
## 2 Related Works
The time and frequency domain functions include the mean absolute value (MAV) for detecting muscle activity, the slope sign change (SSC) expressing the frequency domain characteristics of the EMG signal calculated in the time domain, and the root mean square (RMS) related to continuous force and contraction. In addition, there are waveform lengths (WL) indicating the waveform length with respect to time segment, variance (VAR) indicating the characteristic of force and the integrated EMG (IEMG) used as an onset detection index, and zero crossing (ZC), indicating the number of times the amplitude of the EMG signal crosses zero. Table 2 summarizes the formulas used for each feature extraction [15]. sEMG signals are continuous signals with features that change over time; therefore, it is difficult to locate a clear periodicity in them as it is difficult to repeat the same motion at a constant time and intensity. One of the most widely used feature extraction methods for the frequency domain is fast Fourier transform (FFT). The FFT transforms signals of a temporal domain into a frequency domain. However, the FFT is restricted in evaluating the frequency component for the desired time point because of temporal limitations [16]. STFT is used as a representative feature extraction method in the time-frequency domain [17]. The STFT is a method that compensates for the temporal limitations of the FFT; it selects the desired window length, partitions the time into short segments, and Fourier transforms each of the partitioned segments. Accordingly, because the STFT analyzes the frequency components based on the temporal domain, it has been proven to be more efficient as an analysis method than using temporal and frequency features as it analyzes time-frequency multidimensional features by applying sEMG signals, whose features change over time [7]. Methods used for sEMG signal classification include machine learning and deep learning approaches. Machine learning is a technique that trains a machine to perform a task on behalf of humans to achieve the desired result. Machine learning can be primarily categorized into supervised learning and unsupervised learning. In supervised learning, a model is trained with correct answers to obtain correct predictions; one of the most widely \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature Name** & **Formula** \\ \hline MAV & \(\frac{1}{N}\sum\limits_{n=1}^{N}|x_{n}|\) \\ \hline SC & \(\sum\limits_{n=2}^{N-1}[f(x_{n}-x_{n-1})\times(x_{n}-x_{n+1})]]\), \\  & \(f(x)=\left\{\begin{matrix}1,ifx\geq\text{threshold}\\ 0,\text{otherwise}\end{matrix}\right.\) \\ \hline RMS & \(\sqrt{\sum\limits_{n=1}^{N}\sum\limits_{n=1}^{N}x_{n}^{2}}\) \\ \hline WL & \(\sum\limits_{n=1}^{N-1}|x_{n+1}-x_{n}|\) \\ \hline VAR & \(\frac{1}{N-1}\sum\limits_{n=1}^{N}x_{n}^{2}\) \\ \hline IEMG & \(\sum\limits_{n=1}^{N}|x_{n}|\) \\ \hline ZC & \(\sum\limits_{n=1}^{N-1}[sgn(x_{n}\times x_{n+1})\cap|x_{n}-x_{n+1}|\geq\text{ threshold}]\), \\  & \(sgn(x)=\left\{\begin{matrix}1,ifx\geq\text{threshold}\\ 0,\text{otherwise}\end{matrix}\right.\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Formulas for temporal domain features. used supervised learning methods is the support vector machine [18]. Unsupervised learning is a method of obtaining similar patterns through clustering without using correct answers. One of the most widely used unsupervised learning methods is the k-nearest neighbor (KNN) algorithm. In addition to the KNN, other classifiers used include decision tree learning, random forest, principal component analysis [19], and linear discriminant analysis [20]. Deep learning, a subset of machine learning, is a technique that uses artificial neural networks as its basis; it attempts to solve the problem of weak training by deeply configuring neural networks. The most widely used deep learning networks include CNNs and long short-term memory (LSTM). LSTM is an algorithm that solves the long-term dependence problem of recurrent neural networks and is often used for data with temporal characteristics.