# Multi-Session Electrocardiogram-Electromyogram Database for User Recognition
## 4 User Recognition Method, Results, and Discussion  
A user recognition experiment was performed to confirm the usability of CSU_MBDB1 and CSU_MBDB2, the benchmark DBs constructed in this study. The experiment employed a pre-existing designed network [30], depicted in Figure 6. This designed network consists of two sub-networks, with each sub-network featuring six convolutional layers and two max-pooling layers. Within the convolutional layers, features are extracted by configuring 8, 16, and 32 filters of size [1 \(\times\) 3]. After the convolutional layer, batch normalization is performed. The pooling layer is a filter of size [1 \(\times\) 2] and reduces the feature dimension with padding 0 and stride [1,2]. Sub-network 1 utilizes as input data a size of [1 \(\times\) 5000], representing an ECG signal, while sub-network 2 uses an input data size of [1 \(\times\) 5000], representing an EMG signal. Both sub-networks share the weights of the convolutional layers during training. The features calculated from sub-network 1 and sub-network 2 are concatenated and input to the fully connected layer. The fully connected layer consists of 2048, 256, and 36 (number of classes) to recognize users. The experiment is conducted using a batch size of 256, epoch 100, learning rate of 0.001, and activation function rectified linear unit (ReLU). In the experiment, the multi-session signals are divided into Each Data and Cross Data. Each Data are used to confirm the variability in biosignal analysis. As shown in Figure 7, this method uses signals recorded in different sessions for both training and \begin{table}
\begin{tabular}{c|c c} \hline DB Name & CSU_MBDB1 & CSU_MBDB2 \\ Biosignal types & ECG, EMG & ECG, EMG \\ Session type & Multi & Multi \\ No. of & & \\ participants/construction & 60/36 & 100/58 \\ subjects & & \\ No. of channels & ECG 1 ch, EMG 2 ch & ECG 1 ch, EMG 2 ch \\ No. of gestures & 6 & 6 \\ Sampling rate & 2000 Hz & 2000 Hz \\ Remark & In the form of raw signals. The R-peak positions and the divide points that facilitate the signal division are provided. \\ \hline \end{tabular}
\end{table} 
Table 6: Description of CSU_MBDB1 and CSU_MBDB2. Figure 6: Networks used for user recognition. testing. Specifically, the Each Data method uses the ECG and EMG data recorded on Day 1 for training and ECG data recorded on Day 2 for testing. However, Cross Data are used to analyze the validity of the constructed DB. As shown in Figure 7, this method divides signals recorded in different sessions in a 70:30 ratio using them as training and test data, respectively. The Cross Data method uses 70% of the ECG and EMG signals recorded on Days 1 and 2 as training data and 30% of the ECG and EMG signals recorded on Days 1 and 2 as test data. The experiment was conducted with 10 and 36 people of CSU_MBDB1 and 10 and 58 people of CSU_MBDB2. To analyze hand gestures suitable for user recognition in the two constructed databases, the same number of subjects (10 subjects) was used. Table 7 shows the experimental results of Each Data and Cross Data using CSU_MBDB1. The experiment involved ECG and EMG data from 10 and 36 subjects. The results from the Each Data showed an accuracy of 45.11% for the 10-subject dataset and an accuracy of 35.28% for the data of 36 subjects. In the Cross Data experiment, the accuracy reached 63.42% for the 10-subject datasets and 45.37% for the 36-subject datasets. The experimental results indicate that the user recognition accuracy of the Cross Data method surpassed that of the Each Data method. The heightened performance of the Cross Data method, using biosignal data from two sessions, underscores the significance of biosignal variability, specifically in ECG and EMG signals. This underscores the necessity of constructing a large DB spanning multiple sessions to effectively analyze the variability for these biosignals. Table 8 shows the experimental results of Each Data and Cross Data using CSU_MBDB2. The experiment involved ECG and EMG data from 10 and 58 subjects.