## 4 Testing Experiment
### Experimental Comparisons  
In this section, we perform a comprehensive comparison between the original YOLOv11 model and the improved YOLOv11-ECA model, focusing on the detection performance on the confusion matrix, normalized confusion matrix, PR curve, F1 curve, and validation set. Through detailed analyses, we find that the introduction of the ECA module significantly improves the performance of the model, especially in the detection of several key categories. Firstly, the comparison between the confusion matrix and the normalized confusion matrix shows that the improved model achieves significant improvements in most categories. For example, in the 'ashwa namaskara' and 'bhujangasana' categories, the number of correct classifications improves from 17 and 28 to 19 and 29, respectively, and the number of misclassified samples decreases by two and one, respectively. In addition, the normalized confusion matrix for the 'ashwa namaskara' category shows an improvement in its correct classification rate from 89% to 100% and a significant reduction in misclassification, indicating that the improved model reduces the number of samples that would otherwise be misclassified into other categories. In the confusion matrix plot, we show the comparison of the confusion matrix between the original model and the improved model on the validation set. Figure 3 shows the classification results for each category, visualizing the improvement in the correct classification rate. Comparing the PR curves, the improved model improves by 0.5% from 98.3% of the original model to 98.8% on [EMAIL_ADDRESS]. Specifically, the accuracy of the 'ashwa mukh' svanasana' category improves from 96.4% to 99.3%, and the 'ashwa namaskara' category also improves from 97.4% to 98.5%. Although the precision rate for the 'kumbhakasana' category slightly decreased, the precision rates for most categories were more consistent in the high-recall region, indicating that the improved model's ability to capture detailed features has been enhanced. Figure 4 shows the performance of the original model and the improved model on the PR curves, where it is clear that the improved model achieves a better balance for most categories. Figure 4: PR curve of original and improved models. (**a**) Original model PR curve. (**b**) Improved model PR curve. Figure 3: Normalized confusion matrices of original and improved models. (**a**) Original model normalized confusion matrix. (**b**) Improved model normalized confusion matrix. When comparing the F1 curves, both the original and improved models achieve a highest F1 score of 0.96, but the confidence threshold of the improved model is increased from 0.385 to 0.416, indicating that the model can maintain the same F1 score at a higher confidence level. The improved model is smoother in the high-confidence interval (confidence > 0.8) with less fluctuation in F1 scores, further demonstrating the ECA module's effectiveness in optimizing the model for determining positive and negative samples. Figure 5 shows the performance of the original model and the improved model on the F1 curve, with the improved model maintaining a higher F1 score across various confidence levels. Finally, a comparison of the detection performance of the validation set shows that the overall performance of the improved model is significantly improved. The overall [EMAIL_ADDRESS] of the original model is 97.7%, and the improved model improves to 98.6%. In particular, for the categories 'Pranamasana' and 'Adho Mukh Svanasana', [EMAIL_ADDRESS] improves from 0.977 to 0.995 and from 0.964 to 0.993, respectively, with high levels of precision and recall. However, the performance of the 'hasta utthanasana' and 'kumbhakasana' categories slightly decreases from 0.987 to 0.983 and from 0.991 to 0.968, respectively. Table 3 shows the comparison of the detection performance between the original model and the improved model on the validation set.