# Study on Noise Reduction and Data Generation for sEMG Spectrogram Based User Recognition
## 4 Experimental Methods and Results  
Ninapro DB2, a representative open database, was used to evaluate the reproducibility of the proposed sEMG spectrogram-based user identification performance. Table 3 summarizes the detailed composition of the database. A total of 40 subjects were used in the database, and Figure 11 illustrates the hand motions employed in this study. The hand motions included in Ninapro DB2 comprised movements of an entire arm or a hand. However, motions with large movements were not suitable for use as user identification passwords; hence, such motions were removed and the data were constructed using motions one to seven, which can be performed within the palm range. Each motion was repeated six times for 5 s with a 3 s break; the data were acquired at a sampling rate of 2000 Hz and 12 channels were used, including the forearm periphery, biceps, and triceps [9]. As the amount of data was insufficient because each motion was repeated only six times, the training and test data for each motion were initially composed of three data entries each when the data were partitioned by applying a ratio of 5:5. In other words, the total amount  
Figure 10: CNN structure. of data for one subject was 21 training data and 21 test data; hence, the total amount of data for 40 subjects was 840 training data and 840 test data. Noise, including resting state signals, was removed from the acquired sEMG signals; subsequently, the amount of data was increased using matching pursuit. The sEMG signals generated through the predefined number of repetitions in matching pursuit can be confirmed by measuring the similarity with the actual signals through cross-correlation. Table 4 summarizes the cross-correlation similarity between the actual sEMG signals and the sEMG signals generated through matching pursuit. It was confirmed that the signals were generated with a similarity between 90% and 99%. Accordingly, when the initial training dataset was increased by ten times, the data of each motion were composed of 33 training data and 3 test data; the amount of training data was increased for the data of 40 subjects through such data generation to acquire a composition ratio of 9240 training data and 840 test data. To analyze the sEMG spectrogram-based user identification performance, the window length, a parameter of the STFT, was set to 64, 128, 256, and 512, separately, and the sEMG spectrogram was generated by changing the time-frequency resolution. As shown in Figure 12, the highest identification accuracy of 85.4% was observed for the signals with resting state signals removed when using the window length of 256. Therefore, the window \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{8}{c}{**Number of Repetitive Generations**} \\ \cline{2-10}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline \multirow{8}{*}{Cross-correlation similarity} & 0.908 & 0.919 & 0.927 & 0.934 & 0.942 & 0.955 & 0.96 & 0.973 & 0.985 & 0.99 \\ \cline{2-10}  & 0.903 & 0.913 & 0.922 & 0.932 & 0.941 & 0.949 & 0.956 & 0.964 & 0.971 & 0.981 \\ \cline{2-10}  & 0.907 & 0.918 & 0.928 & 0.937 & 0.944 & 0.956 & 0.963 & 0.971 & 0.979 & 0.989 \\ \cline{2-10}  & 0.900 & 0.912 & 0.923 & 0.933 & 0.94 & 0.952 & 0.959 & 0.966 & 0.972 & 0.983 \\ \cline{2-10}  & 0.904 & 0.916 & 0.924 & 0.934 & 0.943 & 0.953 & 0.961 & 0.969 & 0.974 & 0.986 \\ \cline{2-10}  & 0.907 & 0.917 & 0.925 & 0.937 & 0.949 & 0.956 & 0.961 & 0.968 & 0.972 & 0.988 \\ \cline{2-10}  & 0.909 & 0.919 & 0.927 & 0.936 & 0.945 & 0.955 & 0.964 & 0.971 & 0.977 & 0.989 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Similarity of sEMG signals generated for one motion cycle. Figure 11: Seven hand motions of Ninapro DB2. length parameter having the most suitable time-frequency resolution for the sEMG signal was set to 256.