# Multi-Session Electrocardiogram-Electromyogram Database for User Recognition
## 4 User Recognition Method, Results, and Discussion
In the Each Data experiment, the accuracy was 48.44% for the 10-subject datasets and 27.71% for the 58-subject datasets. Meanwhile, the Cross Data experiment yielded an accuracy of 66.39% for the 10-subject dataset and 49.42% for the 58-subject dataset. Similar to CSU_MBDB1, the experimental results showed that the user recognition accuracy of the Cross Data method in CSU_MBDB2 surpassed that of the Each Data method. In addition, it was confirmed that the three hand gestures performed in CSU_MBDB2 were suitable for user recognition compared to the three hand gestures performed in CSU_MBDB1 (excluding the three identical hand gestures). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Category** & **Round 1 (\%)** & **Round 2 (\%)** & **Round 3 (\%)** & **Mean (\%)** \\ \hline \multirow{3}{*}{Each Data} & 10 subjects & 46.62 & 43.67 & 45.05 & 45.11 \\  & 36 subjects & 34.18 & 36.29 & 35.37 & 35.28 \\ \cline{1-1}  & 10 subjects & 62.44 & 62.22 & 65.61 & 63.42 \\ \cline{1-1}  & 36 subjects & 46.01 & 45.59 & 44.52 & 45.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: User recognition result using CSU_MBDB1. Figure 7: Structures of Each Data and Cross Data. Figure 8 shows the data distribution using CNN features, referring to a previous study [31], using the ECG and EMG of 10 CSU_MBDB2 subjects. In Figure 8, it can be seen that the biosignals of subject 1, subject 2, subject 3, and subject 7 are well clustered. However, it can be seen that many feature areas overlap when the data distribution of four subjects (subject 5, subject 8, subject 9, subject 10) uses the CNN features from Figure 6. These results are a result of the variability in ECG and EMG constructed in a multi-session and indicate the need for research on feature extraction technology for user recognition using CSU_MBDB1 and CSU_MBDB2. Table 9 shows the experimental results of the extended DB using the same three hand gestures (CSU_MBDB1: gesture no. 1 and CSU_MBDB2: gesture no. 1; CSU_MBDB1: gesture no. 4 and CSU_MBDB2: gesture no. 2; CSU_MBDB1: gesture no. 6 and CSU_MBDB2: gesture no. 4) from CSU_MBDB1 and CSU_MBDB2. The experiment used ECG and EMG from 94 subjects as Cross Data. As a result of the experiment, 94 people were recognized with an accuracy of 27.05%. In addition, to show the development potential of the constructed DB, an experiment was conducted using filtering (noise removal), which is widely used in existing research. ECG noise was removed using BPF with a bandwidth of 0.5 to 40 Hz. EMG noise was removed using BPF with a bandwidth of 5 to 500 Hz. The experimental results showed an accuracy of 30.94%, confirming that the accuracy was improved by 3.89% compared to before removing noise (with noise). The user recognition experiment using the constructed biosignal DB showed a relatively low accuracy of 66.39%. Notably, the ECG signals displayed a significant amount of noise, as shown in Figure 9 (red box), due to the simultaneous measurement of ECG and EMG during gesture performance. It is crucial to note that the user recognition experiment in this study was conducted using raw signals without noise removal or relatively simple noise removal. The network employed for user recognition utilized a straightfor  
Figure 8: Three-dimensional plot of CNN features from 10 subjects (CSU_MBDB2). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Category**} & **Round 1 (\%)** & **Round 2 (\%)** & **Round 3 (\%)** & **Mean (\%)** \\ \hline \multirow{3}{*}{Each Data} & 10 subjects & 48.33 & 47.17 & 49.83 & 48.44 \\  & 58 subjects & 29.14 & 26.43 & 27.56 & 27.71 \\ \cline{1-1}  & 10 subjects & 67.67 & 65.17 & 66.33 & 66.39 \\ \cline{1-1}  & 58 subjects & 50.19 & 49.49 & 48.59 & 49.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: User recognition result using CSU_MBDB2. ward model, contributing to the observed low accuracy.