## 4 Performance Evaluations
### Experimental Results
#### 4.2.2 Impact of FPS on Sign Language Recognition Accuracy  
The detection of the start and end points of sign utterances plays a crucial role in the accuracy of sign language recognition models, and this detection is significantly influenced by the frames per second (fps) of the video input. Figure 6 shows examples of how different fps affect the quality of captured keypoint images. Figure 6a shows frames extracted at 5 fps, while Figure 6b shows frames extracted at 25 fps. Both were sourced from an original 30 fps video depicting the sign for the number '0'.  
In a fixed sampling rate scenario, where the sampling rate was set to 5, keypoint information is extracted every five frames to detect the start and end points of the sign language utterance. This is because the sign language recognition model was optimized for 30 fps environments. However, when applied to a 5 fps environment, this Fixed Method becomes suboptimal.  
Table 7 shows a comparison of the number of extracted images, keypoint extraction frequency, and recognition accuracy according to fps. In a 5 fps environment, during a 5-s sign utterance, only 25 frames can be extracted and made available. This scarcity results in a higher likelihood of capturing irrelevant frames that do not adequately represent the beginning or end of the gesture, thereby reducing recognition accuracy. Therefore, in summary, because the amount of keypoint movement change between each frame is much larger, it is advisable to adjust the sampling rate to a shorter interval. As shown in Figure 6, the amount of change in each keypoint between consecutive frames is greater at 5 fps than at 25 fps.  
Table 8 provides confusion matrix results for top-1 (accuracy, precision, recall, F1-score) according to frame rates and methods. As shown in Table 8, the performance of the Dynamic Method is significantly higher than the Fixed Method in lower-fps environments. In the 5 fps environment, the Dynamic Method significantly outperforms both the Original and Fixed Methods with higher precision (0.81), recall (0.77), and F1-score (0.76), while the Fixed method particularly struggles with low recall (0.11), highlighting its limitations in such environments. In the 10 fps environment, all methods show improvement, yet the Dynamic Method maintains superior performance with high precision (0.87), recall (0.85), and F1-score (0.84). The Original Method also performs well but trails slightly in F1-score (0.83). In the 15 fps environment, the Dynamic Method continues to excel with balanced precision (0.89) and recall (0.88), resulting in a strong F1-score of 0.87, while the Fixed Method, although improved, remains behind in recall (0.70). In the 20 fps and 25 fps environments, the performance of the Dynamic Method is further improved, consistently achieving the highest F1-scores of 0.90 and 0.92, compared to the Fixed and Original Methods, respectively.  
As shown in Table 8, the superior performance of the Dynamic Method, particularly in low-fps environments, can be attributed to its ability to adaptively adjust the sampling rate based on real-time frame rates. Therefore, the Dynamic Method can accurately capture the start and end points of sign language utterances even when fewer frames are available. This adaptability allows the Dynamic Method to maintain high precision and recall by efficiently managing the trade-off between temporal resolution and computational resources, which is especially crucial in mobile environments where computational power is limited. Based on all of the experimental results, the Dynamic Method proves to be a reliable solution for sign language recognition in real-time applications, particularly in environments with challenging operational conditions and varying frame rates.  
Figure 6: Examples of how different fps affect the quality of captured keypoint images.