## 4 Discussion  
In order to achieve accurate recognition of upper arm motions and to mitigate the impact of arm position effects on the performance of myoelectric interfaces during real-world use, we propose a network framework with an Inception feature extraction structure that combines the techniques of adversarial training in the domain adaptation domain. In this paper, we design the DAIDA to effectively extract and classify domain-invariant features across domains. Specifically, by optimizing the three components of the adversarial network, namely the feature extractor, label predictor and domain discriminator, the network can learn arm-position-specific features by aligning the feature distributions of different arm positions to reduce the impact of domain migration due to changes in arm position. The feature extractor learns discriminative features of sEMG signals through the deep network. The label predictor is used to distinguish between different tasks, while the domain discriminator is used to reduce the inter-domain variation in features. Thus, this framework effectively achieves better accuracy and adaptability in the recognition of upper arm motions. This study first demonstrates that the classification performance of the parallel structured network outperforms a network using ordinary convolutional layers in upper arm motion recognition (Table 4). The network structure proposed in this paper combines the feature maps output from the parallel convolutional kernels with different kernel sizes to construct feature maps with more comprehensive semantic information in order to improve the model's ability to recognize upper arm movements. More complex parallel structures look to exhibit better model performance while ensuring that the network size is appropriate. Therefore, in this study, the Inception C structure was chosen as the feature extraction module of the baseline model. The model performs well in recognizing upper arm motions under the three commonly used arm positions, as can be seen in Figure 6, with recognition accuracies greater than 88% for all of them. The average recognition accuracy was also greater than 90% when using all arm positions as inputs. There is a distinction between the recognition results at the three arm positions, which may be due to the different characteristics of the corresponding EMG data. In comparison to previous studies, [PERSON] et al. found that it was possible to classify eight upper-limb phantom limb movements and one no-movement category in transhumeral amputees with an average accuracy of 81.1% [31]. The inclusion of kinematic features increased the accuracy by an average of 4.8%. [PERSON] et al. successfully classified up to 14 phantom arm movements, achieving an average recognition accuracy of more than 80% when only six hand, wrist and elbow movements were considered [3]. In contrast to previous work, [PERSON] et al. successfully improved the identification accuracy of 15 motions of humeral amputation patients to more than 85% by using a higher-order spatial filter to obtain more informative high-density sEMG signals [32]. [PERSON] et al. found that the classification performance of fused sEMG and EEG signals was significantly better than that of single sEMG or electroencephalogram (EEG) signal sources [33]. The accuracy of this fusion method was as high as 87%. [PERSON] et al. found that eight discrete gestures of the upper arm could be recognized with a best classification accuracy of 81% [34]. While the number of movements recognized in previous studies is different from ours, it is worth noting that these works required the use of either high-density electrodes or information fusion as inputs to the classifier, which means that more complexity is introduced in practical use. Our study achieves effective recognition of commonly used motions while using routine EMG electrodes as input. Previous studies show that the arm position has a significant impact on gesture recognition [35].