## 4 Discussion
As shown in Figure 7, the average recognition accuracy for classifying arm positions using the baseline model is over 95%. This indicates that there is great variability in the motion signals for different arm positions, which is consistent with existing research [21; 36; 37; 38]. It is also illustrated that different arm elevation position recognition results are not obtained due to the dissimilarities in the EMG signals under each armposition. Therefore, we adopt DAIDA to alleviate this impact; i.e., after pre-training with data from a single arm position, migration can be completed following a simple calibration to make the EMG interface applicable to another arm position. From Figure 8, it can be observed that when a recognition model pretrained on the source domain arm position is retrained with target domain data (different from the source domain arm position) and tested on the source domain arm position (the same arm position as the source domain), the variation caused by different arm positions results in a maximum recognition accuracy of 44.03% \(\pm\) 12.26%. As can be observed from the first column of Figure 8, the recognition accuracy is improved by 29.02% \(\pm\) 9.18% compared to the direct use of CNN when tested on the source domain data (\(p\) < 0.05). When the recognition model pretrained on the source domain position is tested on the target domain arm position, the recognition accuracy can reach up to 91.25% \(\pm\) 6.59% at the test arm position. As can be observed from the second column of Figure 8, this is an improvement of 75.76% \(\pm\) 2.30% over the result obtained from testing using the CNN directly (\(p\) < 0.05). Using the whole dataset (three arm positions) as the source domain, the best recognition results were achieved when testing on the target domain data, with a recognition accuracy of 93.33% \(\pm\) 6.86%. When not tested on the target domain data, the recognition results declined significantly, with the highest recognition accuracy being 54.60% \(\pm\) 12.86%, which was still significantly higher than the direct use of CNNs. This meets the expected results while using only a small amount of data for retraining to achieve a performance that approximates the source domain model, meets the expected results and achieves the goal of recognizing different arm elevation positions [21]. When the recognition model trained on the source domain position is tested on data that are different from the source and target domains, the highest arm position recognition accuracy can be 41.99% \(\pm\) 10.98% in the test. As can be observed from the third column of Figure 8, this is an improvement of 29.08% \(\pm\) 8.56% over the result obtained from testing with the CNN directly (\(p\) < 0.05). This demonstrates the weak generalization ability of the provided domain adaptation model. A similar distribution of results was demonstrated in the experimental results for the transhumeral amputees. Regardless of whether the source domain data comprised a single arm position or all arm position data, better recognition results with a recognition  
Figure 8: Comparison of results using one single position for pretraining data under CNN and DAIDA. ‘DAIDA all positions’ means data from all positions were used for pretraining. Horizontal coordinates indicate test data for different arm positions. The characters x, y and z each represent one arm position, respectively (x, y, z \(\in\) (1, 2, 3) and x \(\
eq\) y \(\
eq\) z). * represents \(p\) < 0.05.  
accuracy greater than 90% were only shown when tested on the target domain. When not tested on the target domain, the recognition accuracy was less than 60%. This fits well with the use scenario of pattern recognition based on sEMG signals, i.e., motion recognition of translumeral amputees controlling a myoelectric prosthesis in different arm positions. The modeling framework proposed in this study meets the needs of myoelectric prosthesis motion recognition for translumeral amputees and solves the problem of performance degradation caused by arm position changes.