# Multi-Session Electrocardiogram-Electromyogram Database for User Recognition
## 3 Measured Method of Multi-Session Biosignal Benchmarking DBs
### Multi-Biosignal DB Verification and Segmentation  
In order to minimize the variability in the sensor location while proceeding with the measurement protocol, hand gestures were performed when attaching the EMG sensor to confirm the location where the muscles were activated (moved). Additionally, in order to maintain the same force and duration, the DB construction procedure was explained to the subjects, which they had to learn. Information such as the duration and amplitude of ECG and EMG measured newly (Day 2) from the same subject was reviewed by comparing similarity with signals from other sessions (Day 1). However, due to technical problems, an incomplete signal was confirmed. Various types of incomplete signals were observed, including (1) Bluetooth communication instability between the measurement PC and Biopac MP160 during the gesture period, (2) interference between EMG and ECG sensors during the gesture period, (3) resistance in the connection cable between the sensor and the measurement device during the gesture period, and (4) destruction of the signal waveform due to body movement during the rest period. In CSU_MBDB1, data from 24 subjects  
Figure 4: Electrode positions for CSU_MBDB1.  
Figure 3: Signal measurement position experiment for electrode positions.  
are incomplete, meaning that data from 36 subjects can be used. In CSU_MBDB2, data from 42 subjects is incomplete, so data from 58 subjects can be used. Therefore, the final CSU_MBDB1 consists of the ECG and EMG signals obtained from 36 subjects, while CSU_MBDB2 consists of those obtained from 58 subjects.  
The ECG and EMG data underwent visual inspection, and division was carried out to ensure the preservation of the PQRST waveform in the ECG after the gesture was performed. Figure 5 shows the segmented ECG and EMG waveforms. Data segmentation occurred only for the segments longer than or equal to 0.5 s, considering both before/after data acquisition. The introduced benchmark DBs, CSU_MBDB1 and CSU_MBDB2, consist of raw signals, including R-peak positions and division points that facilitate the division process, as shown in Figure 5.  
Table 6 shows a summary description of the constructed CSU_MBDB1 and CSU_MBDB2. In contrast to Table 3, which summarizes existing multi-biosignal DBs, CSU_MBDB1 and CSU_MBDB2 were designed as multi-session data with a substantial number of subjects. An advantageous feature of CSU_MBDB1 and CSU_MBDB2 is their expandability, as they share three identical gestures, resulting in multi-session databases with a total of 94 subjects. This expansion allows for more subjects compared to the benchmark DBs of ECG (Table 1) and EMG (Table 2), excluding CSU_ECG and CSU_sEMG. The databases can be actively used for research on biosignal variability and user recognition.  
\begin{table}
\begin{tabular}{c c} \hline \hline
1. Clenching the fist & 2. Flexing the wrist \\ \hline
3. Extending the wrist upward & 4. Rotating the wrist 90 degrees to the left \\ \hline
5. Rotating the wrist 90 degrees to the right & 6. Raising the cell phone 90 degrees \\ \hline \hline \end{tabular}
\end{table}
Table 5: Six hand gestures from CSU_MBDB2.  
Figure 5: Examples of dividing the ECG and EMG waveforms in CSU_MBDB1 and CSU_MBDB2. (**a**) Examples of EMG_ch1 waveforms. (**b**) Examples of EMG_ch2 waveforms. (**c**) Examples of ECG waveforms.