## 4 Results and Discussion  
The publicly available EMG signal dataset Ninapro DB2 was used to verify the performance of the two-step biometrics method using EMG signals based on the CNN-LSTM network proposed herein. The Ninapro DB2 comprises hand gestures performed by a subject, and hand gestures can be divided into three groups (groups 1-3). The data used in the experiment were EMG signals of seventeen gestures measured within group 1, and the experiment was conducted with twenty subjects. EMG signal measurements were collected at twelve channels using Delsys Trigno Wireless EMG equipment and measured at a 2000 Hz sampling rate. When measuring the EMG data, each movement lasted for five seconds, and a three seconds rest period was performed between movements. The subjects were asked to move the right hand repeatedly while measuring the EMG data and each movement was repeated six times [29]. Experimental performance was evaluated using accuracy, recall, and precision, and the above evaluation indicators can be calculated as true positive (\(TP\)), true negative (\(TN\)), false positive (\(FP\)), and false negative (\(FN\)). Accuracy refers to the performance of when the model's true label predicts true and false label predicts false, which is an evaluation index representing the most intuitive performance of the model. The method to obtain accuracy is shown in Equation (5). Recall refers to the ratio of the model's true label predicts true or false, which can be calculated through \(TP\) and \(FN\) as shown in Equation (6). Precision refers to the actual ratio of true classifications among all classifications by the trained model, which can be calculated through \(TP\) and \(FP\) as shown in Equation (7). \[\text{accuracy}=\
icefrac{{(TP+TN)}}{{(TP+TN+FP+FN)}} \tag{5}\]  
\[\text{recall}=\
icefrac{{TP}}{{(TP+FN)}} \tag{6}\]  
\[\text{precision}=\
icefrac{{TP}}{{(TP+FP)}} \tag{7}\]  
The data used in the two-step biometrics experiment were recorded from each gesture repeated six times by twenty subjects. However, there is a problem that the amount of training and test data were insufficient when using an LSTM network for gesture recognition and a CNN-LSTM network for single biometrics. Therefore, three-fold cross-validation (each subject used data from four training sets and two test sets) was performed for gesture recognition (1:N comparison) and single biometrics (1:N comparison). The results of the gesture recognition experiment using time domain features and LSTM network are as shown in Table 3. As a result of the experiment, the basic gestures of the seventeen hands showed an average of 83.91% accuracy, 91.26% recall, and 91.15% precision performance. In terms of the recognition performance by gesture, the 16 th gesture was the best classified at 99.62%, and the 11 th gesture was the worst classified. The 11 th gesture was most often misclassified as the 9 th gesture, indicating similar hand gestures. The results of comparative experiments with conventional gesture recognition studies using EMG signals are shown in Table 4. The EMG signal has a characteristic that shows different waveforms for each gesture when the same subject performs different gestures. Therefore, the performance of gesture recognition using the EMG degraded as the number of classification gesture classes increased. In this paper, we conducted more hand gesture classification experiments than other related works by utilizing seventeen gestures. In \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fold** & **Accuracy (\%)** & **Recall (\%)** & **Precision (\%)** \\ \hline
1 & 80.54 & 89.83 & 88.62 \\
2 & 89.17 & 93.96 & 94.6 \\
3 & 82.01 & 90 & 90.24 \\ Average & 83.91 & 91.26 & 91.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Gesture recognition experiment results using the time domain features and LSTM. addition, the data size used in the experiment was 60 ea, which is smaller than in related works (except [4]).