# Study on Noise Reduction and Data Generation for sEMG Spectrogram Based User Recognition
## 2 Related Works  
Techniques applied to existing user identification systems using sEMG signals are analyzed in this section. Figure 3 illustrates the user identification system using sEMG signals. First, sEMG signals are acquired independently or by using open databases to construct data. Subsequently, data preprocessing and normalization are conducted to remove noise in the data signals. The preprocessed signals are passed subjected to feature extraction and then the user identification performance is evaluated using a classifier. For user identification, sEMG data are first constructed. sEMG data consist of muscle and motion data to be used. As summarized in Table 1, there are Ninapro DB2 and sEMG Basic Hand Movement Upatras databases for hand or wrist movement. Ninapro DB2 was composed by acquiring movements of fingers and wrists and holding movements from 40 subjects, and sEMG Basic Hand Movement Upatras is a database composed by acquiring daily hand holding movements from five subjects. The database for finger movement is [PERSON]'s sEMG, and it is a database constructed by acquiring data from eight subjects by performing finger movements. Finally, the EMG dataset in lower limb dataset using the leg muscles was constructed by acquiring the sEMG signals generated when performing leg movements from 22 subjects [8; 9; 10; 11]. However, the problem regarding insufficient data in sEMG DBs (an insufficient number of acquired subjects or motion repetitions) has been continuously problematic. Although multiple DBs can be synthesized to address this issue, limitations exist when combining multiple DBs into one, because each sEMG DB employs different motions, muscle channels, number of motion repetitions, and motion durations as initial acquisition conditions. Furthermore, when increasing the amount of data by segregating the sEMG signals of one motion cycle into predefined windows, it is difficult to identify the same motion signal because the frequency component changes over time even for the same motion [12]. The constructed sEMG DB must undergo preprocessing as it contains noise generated from various environments. The noise that should be removed includes power line noise generated from the measuring device, modulated waves from 60 Hz band, white noise from broadband, noise occurring from differences in performance and functions of disposable electrodes, noise caused by physiological interference, and noise caused by characteristics of muscle tissues. Preprocessing should be conducted to remove such noises. First, they were identified through frequency analysis and then removed by using various filters, such as high-pass, low-pass, band-pass, Butterworth [13], and notch filters [14]. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Database Name** & **Channel** & **Individuals** & **Motion** & **Remarks** \\ \hline sEMG Basic & & & & & Duration for each motion: 6 s. \\ Hand Movement & 2 ch & 5 subjects & 6 hand motions & & Repetitions for each motion: 30 times. \\ \hline EMG (Dr. [PERSON] & 2 ch & 8 subjects & 10 finger motions & & Duration for each motion: 5 s. \\  & Khushaba) & 2 ch & 8 subjects & 10 finger motions & & Repetitions for each motion: 6 times. \\ \hline Ninapro DB2 & 12 ch & 40 subjects & 49 hand and wrist motions & & Duration for each motion: 5 s. \\  & & & & wrist motions & & Repetitions for each motion: 6 times. \\ \hline Benchmark & & & & & Repetitions for each motion: 3 times. \\ Datasets for Bilateral & 7 ch & 10 subjects & 3 leg motions & & Repetitions for each motion: 3 times. \\ Lower-Limb & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: sEMG open databases. Figure 3: EMG signal-based user identification system. After that, features are extracted from the preprocessed EMG signal. Feature extraction can be mainly classified into time domain, frequency domain, and time-frequency domain.