## 4 Model Training and Experimental Results
### Experiment Based on Publicly Available Dataset
#### 4.1.3 Individual Variability Analysis  
The primary objective of this research is to assess the model's performance when applied to various datasets, with a particular focus on evaluating its recognition performance across different gestures of different individuals. The experiment involved four datasets: NinaPro DB1 E1, NinaPro DB1 E2, NinaPro DB5 E1, and NinaPro DB5 E2. Figure 9 illustrates the validation effect of using the InRes-ACNet model on the NinaPro DB1 E1 dataset, which encompasses 12 different gestures. It presents the validation loss values and recognition accuracy for five subjects across 50 validation cycles (epochs). In the initial stages of validation, the loss values for all subjects decreased rapidly, while the accuracy rates increased significantly, nearing their peak levels. This indicates that the model exhibits a fast convergence rate and robust learning capabilities. Throughout the validation process, despite minor fluctuations, the loss and accuracy curves generally remained stable, demonstrating the recognition stability of the InRes-ACNet model. Figure 10 illustrates the validation effect of employing the InRes-ACNet model on the NinaPro DB1 E2 dataset, encompassing 17 different gestures. It presents the validation loss values and recognition accuracy for five subjects across 50 validation cycles (epochs). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method Author** & **Model** & **Recognition Acc** \\ \hline [PERSON] & SVM [7] & 69.45\% \\ [PERSON] & Random forests [12] & 75.36\% \\ [PERSON] & MyoCNN [23] & 78.25\% \\ [PERSON] & VGGNet [12] & 81.12\% \\ [PERSON] & MSCNet [36] & 83.24\% \\ This paper & InRes-ACNet & 86.46\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Gesture recognition results of different models. Figure 9: Validation loss and accuracy for NinaPro DB1 E1. (**a**) Validation loss for NinaPro DB1 E1. (**b**) Validation accuracy for NinaPro DB1 E1. At the initial stage of validation, the loss value decreased rapidly, while the accuracy rate ascended to 95%, indicating the model's rapid learning capability and convergence speed. Despite minor fluctuations in loss values and accuracy between the 40 th and 50 th epochs, the model demonstrated stability and high accuracy throughout the entire gesture recognition process. The variability experiment results for the datasets NinaPro DB1 E1, NinaPro DB1 E2, NinaPro DB5 E1, and NinaPro DB5 E2 are presented in Table 3, with the boxplot of recognition accuracy for these four datasets depicted in Figure 11. This boxplot illustrates the distribution of recognition accuracy for each dataset, including statistical measures such as the quartile, median, and mean. The NinaPro DB1 E1 and E2 datasets cover 12 and 17 gestures, respectively. As indicated in Table 3, the average recognition accuracy for these datasets was 87.94% and 86.00%, respectively, with individual variances of 8.04% and 6.25%. The boxplot reveals that the median recognition accuracy closely aligns with the mean, showing a relatively balanced distribution without extreme outliers or significant skewness. For the NinaPro DB5 E1 and E2 datasets, which cover 12 and 17 gestures respectively, the average recognition accuracy was noted as 87.04% and 85.39%, respectively, with individual differences of 8.45% and 8.16%. The boxplot indicates that, despite the quartiles' wide range, the median remains close to the mean, suggesting that the model maintains a consistent performance even in more complex gesture recognition tasks. Overall, the results demonstrate the model's consistent performance across different datasets in gesture recognition tasks. Overall, the InRes-ACNet model achieves a high accuracy rate in various gesture recognition tasks, demonstrating its effectiveness in capturing and utilizing key features  
Figure 10: Validation loss and accuracy for NinaPro DB1 E2. (**a**) Validation loss for NinaPro DB1 E2. (**b**) Validation accuracy for NinaPro DB1 E2.