# Multi-Session Electrocardiogram-Electromyogram Database for User Recognition
## 5 Conclusions  
Various benchmarking biosignals DBs have been established for user recognition research using biosignals. However, these DBs encountered limitations as the variability in signals could not be adequately analyzed, either due to their construction based on a limited number of subjects or the recording of biosignals in single sessions. This study address this gap by introducing CSU_MBDB1 and CSU_MBDB2, constructed by recording biosignals from numerous subjects across multiple sessions. The participant count for constructing the DBs was 60 and 100, respectively, with sensors attached to both the left and right arms for recording ECG and EMG signals during the execution of six hand gestures. Despite technical challenges in the measurement protocol, the DBs ultimately comprised data from 36 and 58 subjects. Since these two benchmarking biosignal DBs share three identical hand gestures, they can be expanded into a combined DB covering 94 subjects. To analyze the usability of the constructed DBs, we conducted a user recognition experiment using a neural network designed in a previous study. Following the experiment, the user recognition accuracy stood at 66.39% (Cross Data and 10 subjects from CSU_MBDB2). This relatively lower accuracy is attributed to the use of a straightforward neural network without the removal of noise from the signals. The introduced DBs (CSU_MBDB1 and CSU_MBDB2) hold potential as benchmark DBs for user recognition research, given their recording of ECG and EMG signals in multiple sessions. By expanding the DB using three gestures from the multi-session DB for user recognition, we showed that user recognition experiments were possible with ECG and EMG from 94 subjects acquired simultaneously. Additionally, it was confirmed that performance improved when using a preprocessed signal (without noise) rather than a raw signal (with noise). There is a prospect of improving user recognition accuracy by implementing noise removal techniques on the constructed multi-session biosignal DBs and designing an optimal neural network. Lastly, by measuring ECG and EMG in multi-sessions, the nonlinear characteristics of biosignals that occur over time can be analyzed, and through this, user recognition research that is robust to volatility can be conducted. Future research endeavors will include investigating the variability analysis of the multi-session biosignal DBs and exploring techniques for noise removal from biosignals to further improve user recognition accuracy. Conceptualization, J.S.K. and E.B. ; methodology, J.S.K., J.L., Y.-H.B., K.-C.K., E.B. and S.P. ; software, J.S.K., J.L. and E.B. ; validation, J.S.K., C.H.S., J.M.K., E.B. and S.P. ; formal analysis, J.S.K., C.H.S., J.M.K. and E.B. ; writing--original draft preparation, J.S.K. and E.B. ; writing--review and editing, J.S.K., J.J., H.-S.C., K.-C.K., Y.T.K., E.B. and S.P. ; supervision, S.P. ; project administration, S.P. ; funding acquisition, S.P. All authors have read and agreed to the published version of the manuscript. This study was supported by the Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Education (No. NRF-2017R1A6A1A03015496). All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Chosun University Institutional Review Board (2-1041055-AB-N-01-2023-21). Informed Consent StatementInformed consent was obtained from all subjects involved in the study. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**DB Name**} & **Gesture** & **Gesture** & **Gesture** & **Gesture** & **Gesture** & **Gesture** \\  & **No. 1** & **No. 2** & **No. 3** & **No. 4** & **No. 5** & **No. 6** \\ \hline CSU\_MBDB1 & 0.4766 & 0.4555 & 0.4462 & 0.4524 & 0.4324 & 0.4718 \\ CSUMBDB2 & 0.4607 & 0.4492 & 0.4583 & 0.4574 & 0.4656 & 0.4580 \\ \hline \end{tabular}
\end{table}