## 4 Performance Evaluations
### Experimental Environments  
#### 4.1.1 Sign Language Recognition Model  
To evaluate the performance of our dynamic sampling rate adjustment method, we used the Video Swin Transformer model [27]. This model stands out as a cutting-edge architecture in video recognition, celebrated for its efficiency and accuracy in processing video sequences. Since sign language recognition involves inferring actions from video data, the Video Swin Transformer model is highly suitable for sign language recognition and classification tasks. Specifically, we utilized the swin3d_b model with pretrained \begin{table}
\begin{tabular}{l l} \hline \hline
**Components** & **Specifications** \\ \hline CPU & AMD Ryzen 9 7950X3D, 16 cores \\ \hline RAM & Samsung DDR4 64 GB \(\times\) 2 \\ \hline GPU & ZOTAC GeForce RTX 4090 \(\times\) 2, 24 GB GDDR6X each \\ \hline OS & Ubuntu 22.04.2 LTS \\ \hline CUDA Version & 12.2 \\ \hline Python & 3.10.11 \\ \hline MediaPipe & 0.10.11 \\ \hline OpenCV-Python & 4.7.0.72 \\ \hline Torch & 2.0.1 \\ \hline Torchvision & 0.15.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hardware and software configuration for experimental environment. Figure 1: An example of the service flow using the proposed dynamic sampling rate adjustment method in a mobile computing environment. weights torchvision.models.video.Swin3D_B_Weights.KINETICS400_IMAGENET22K_V1 from PyTorch. The overall architecture of the Video Swin Transformer used in the performance evaluations is shown in Figure 2. The final output layer was modified to predict 510 classes to match the sign language dataset used for training and validation. The Video Swin Transformer is specifically designed to capture both spatial and temporal information from video frames, making it highly suitable for tasks such as sign language recognition. It achieves this by segmenting video frames into nonoverlapping windows and applying self-attention mechanisms within each window. This methodology enables the model to focus effectively on relevant segments of the frames, facilitating the learning of intricate patterns associated with sign language movements. Key features of the Video Swin Transformer include the following:  
* Hierarchical structure: The model employs a hierarchical design that processes video data at multiple scales. This allows it to capture both fine-grained details and broader contextual information. * Shifted window partitioning: Unlike traditional transformer models, the Video Swin Transformer uses a shifted window partitioning scheme. This approach enables connections between windows, facilitating the flow of information across different regions of the video frames. * Relative position bias: The model incorporates relative position encoding, which helps maintain spatial relationships between different parts of the input, crucial for understanding the spatial configuration of sign language gestures. * 3D patch embedding: Video frames are divided into 3D patches, preserving both spatial and temporal information. This approach is particularly beneficial for capturing the motion dynamics in sign language. * Efficient computation: By limiting self-attention computation to local windows, the Video Swin Transformer achieves linear computational complexity with respect to input size, making it more efficient than global attention mechanisms. As shown in Figure 2, in our experimental setup, the Video Swin Transformer processed sequences of images extracted from videos by first extracting keypoints of sign language utterances from these images. This approach allowed us to investigate the effectiveness of our dynamic sampling rate adjustment method in maintaining high recognition accuracy across different frame rates, reflecting real-world variability, such as background image information, in video capture conditions. Table 5 shows the hyperparameters and their settings to train the sign language recognition model. The loss function used was LabelSmoothingCrossEntropy, which is suitable for classification purposes.