## 4 Testing Experiment
### Experimental Comparisons
From the table, it can be seen that the improved model achieves significant improvement in most of the categories, especially in the categories of 'adho mukh svanasana' and 'pranamasana' where [EMAIL_ADDRESS] improves from 0.964 and 0.977 to 0.993 and 0.995 respectively, indicating that the improved model significantly improves the detection ability in these categories. Meanwhile, the 'ashwa sanchalanasana' and 'bhujangasana' categories also show a more stable performance improvement, further validating the effectiveness of the ECA module. According to the results in Table 4, the performance of each detection model on the validation set varies. From the table, it can be seen that YOLOv11-ECA performs well in the precision and recall [EMAIL_ADDRESS] metrics, especially outperforming other models in recall and [EMAIL_ADDRESS], which shows its strong ability in the detection task. In contrast, the Transformer and YOLO series models also perform well, especially YOLOv6 and YOLOv10, which are close to 0.96 in precision and recall, but their overall performance is slightly inferior to that of YOLOv11-ECA. Figure 5: F1 scores of original and improved models. (**a**) Original model F1 scores. (**b**) Improved model F1 scores. It is important to note that the selection of baseline models in this study was primarily based on considerations of model lightweightness and practical application scenarios. Given that the task focuses on action posture classification based on camera input, which demands high detection speed and real-time performance, the YOLO series was selected as the main comparative framework. In addition, a Transformer-based model with relatively higher performance but greater computational complexity was introduced as a reference. Pixel-level segmentation models such as SAM2 were not included, as they are primarily designed for high-precision segmentation tasks, entail substantial computational costs, and are not well-suited for the requirements of this study. Considering detection efficiency, inference speed, and the alignment with the task objectives, the chosen comparative models are representative and appropriate, effectively validating the performance advantages of the proposed YOLOv11-ECA model. In summary, by introducing the ECA module, the improved model achieves significant performance gains in several categories, especially in the three key metrics of precision, recall and [EMAIL_ADDRESS]. Although there is a slight decrease in performance in some categories, the overall performance of the improved model shows higher accuracy and stability in most categories. This makes the improved model more suitable for real-world application scenarios, especially in dynamic environments where highly accurate recognition is required. Figure 6 shows some of the recognition results of the YOLOv11-ECA model on the validation set. The model is able to accurately identify multiple targets and performs well in distinguishing between different action categories. At the same time, the model maintains high detection confidence in complex backgrounds. For example, most of the detection results shown in the figure have a confidence level higher than 0.8. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** \\ \hline CNN & 0.778 & 0.689 & 0.780 \\ Transformer & 0.893 & 0.927 & 0.934 \\ YOLOv6 & 0.958 & 0.963 & 0.968 \\ YOLOv10 & 0.955 & 0.961 & 0.971 \\ YOLOv11 & 0.960 & 0.978 & 0.977 \\ YOLOv11-ECA & 0.935 & **0.987** & **0.986** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of overall performance of each detection model on the validation set.