## 4 Model Training and Experimental Results
### Experiment Based on sEMG Signals from 8-Channel Wireless Armband  
This paper presents the integration of the inception-attention module and the ACmix module to develop the InRes-ACNet model as a gesture recognition framework. Initially, a multi-scale inception-attention module is constructed and integrated into the ResNet50 model. Subsequently, the self-attention mechanism, the ACmix module, is incorporated into ResNet50, resulting in the formulation of the InRes-ACNet gesture prediction model. For online prediction, the InRes-ACNet model is employed for gesture recognition tasks. Experimenters donned an sEMG armband and executed eight types of gestures on a manipulator grasping mode control platform (Figure 12). For each gesture, 480 muscle signal segments were extracted and transformed into 480 sEMG grayscale images, yielding a dataset size of 3840 for each experimenter. To assess the model's robustness, ten diverse experimenters, including seven males and three females, were selected. Each experimenter replicated the specified actions to capture muscle electrical signals. The dataset from each experimenter was utilized to train the model, allocating 60% of the data for training, 20% for validation, and 20% for testing. For the test database, twenty groups of data corresponding to different gestures were randomly selected, totaling 160 data groups, and multiple rounds of testing were conducted on the outcomes. A subset of the test results is displayed in Figure 13.  
Figure 11: Boxplot of recognition accuracy for different datasets. Figure 12: Experimental scene diagram. The test results from four experimenters, as depicted in Figure 13, demonstrate that the InRes-ACNet model delivers commendable recognition performance in gesture recognition tasks. Given that the test data are randomly selected, this underscores the robustness of the InRes-ACNet. By integrating both the attention mechanism and a multi-scale structure, the InRes-ACNet showcases a high recognition accuracy and stability in human gesture recognition challenges. The average recognition accuracy across ten experimenters is 88.37%. To elaborate on the results obtained from our model, we have selected four illustrative examples. These examples depict the gesture tests conducted by four experimenters. Figure 13 presents the gesture predictions for Experimenter 1, with an average recognition accuracy of 84.63%. Notably, there are a considerable number of deviations from the target category, such as the circle, indicating that the predicted gestures significantly diverge from the actual gestures. This is especially true for Gesture 2 (spherical grasping), which deviates markedly from its actual form. The figure illustrates that each gesture is prone to misidentification, such as Gesture 5 (two-finger pinch) and Gesture 6 (side pinch). Figure 13 displays the gesture predictions for Experimenter 2, achieving an average recognition accuracy of 90.63%. Most of the predicted category tags align closely with the actual category line, indicating more precise gesture recognition. Figure 13 reveals the gesture predictions for Experimenter 3, with an average recognition accuracy of 92.96%. The minimal deviation from the actual category (i.e., circles) suggests highly accurate gesture recognition results. Figure 13d showcases the gesture predictions for Experimenter 4, with an average recognition accuracy of 87.32%. Despite the recognition errors, particularly for Gesture 8 (thumb extension grip), the findings indicate variability in recognition accuracy across different experiments. Figure 13: Comparison of test results for four experiments (to demonstrate the robustness of the model, we randomly selected some data and performed gesture recognition with different experimenters, obtaining comparative results. (**a**) is the gesture predictions for experimenter 1; (**b**) is the gesture predictions for experimenter 2; (**c**) is the gesture predictions for experimenter 3; (**d**) is the gesture predictions for experimenter 4).