## 5 Conclusions and Future Works  
In this paper, we proposed a dynamic sampling rate adjustment method designed to enhance the performance of sign language recognition in mobile environments. The proposed method addresses the challenges of varying computational capacities and frame extraction rates across different mobile devices. The primary objective of the proposed scheme is to dynamically adjust the sampling rate based on the real-time frame extraction rate during sign language utterances in mobile environments. By using the dynamic sampling rate, the proposed method improves the detection of the start and end points of sign language utterances. Consequently, it improves overall recognition accuracy compared to the previous fixed method. Based on the experimental results, the proposed dynamic sampling rate adjustment method significantly outperformed the previous fixed sampling rate method. Specifically, our approach achieved up to an 83.64% improvement in top-5 accuracy and a 66.54% enhancement in top-1 accuracy. These improvements were particularly pronounced in low-frame-rate scenarios. This shows the effectiveness of our method in environments with limited computational resources such as mobile computing environments. We expect that the proposed method can be widely applied not only to the field of sign language recognition in mobile environments but also to the preprocessing of various artificial intelligence services in mobile environments. This paper has the following limitations. First, the effectiveness of the proposed dynamic sampling rate method was verified in an experimental setting that simulated mobile computing environments by varying the frame rate. This approach was used because performing all sign language utterances directly on a mobile device would be very time-consuming. Therefore, further performance validation on actual mobile devices may be necessary for future research. In addition, the proposed method was applied only to the Video Swin Transformer model, and it is necessary to verify its performance by applying the dynamic sampling rate method to a broader range of AI models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multicolumn{5}{c}{5 fps} \\ \hline Original & 0.52 & 0.62 & 0.52 & 0.51 \\ Fixed & 0.11 & 0.59 & 0.11 & 0.17 \\ Dynamic & 0.77 & 0.81 & 0.77 & 0.76 \\ \hline \multicolumn{5}{c}{10 fps} \\ \hline Original & 0.84 & 0.86 & 0.84 & 0.83 \\ Fixed & 0.67 & 0.73 & 0.67 & 0.67 \\ Dynamic & 0.85 & 0.87 & 0.85 & 0.84 \\ \hline \multicolumn{5}{c}{15 fps} \\ \hline Original & 0.87 & 0.90 & 0.87 & 0.88 \\ Fixed & 0.70 & 0.73 & 0.70 & 0.69 \\ Dynamic & 0.88 & 0.89 & 0.88 & 0.87 \\ \hline \multicolumn{5}{c}{20 fps} \\ \hline Original & 0.83 & 0.90 & 0.83 & 0.85 \\ Fixed & 0.89 & 0.91 & 0.89 & 0.89 \\ Dynamic & 0.90 & 0.91 & 0.90 & 0.90 \\ \hline \multicolumn{5}{c}{25 fps} \\ \hline Original & 0.77 & 0.89 & 0.77 & 0.81 \\ Fixed & 0.92 & 0.93 & 0.92 & 0.92 \\ Dynamic & 0.92 & 0.93 & 0.92 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Confusion matrix results for top-1 (accuracy, precision, recall, F1-score) according to frame rates and methods. Our future research work includes the following. Firstly, expanding the research to include a wider range of mobile devices and operating conditions would provide more comprehensive insights into the robustness of the proposed method. Additionally, integrating machine learning models that can predict the optimal sampling rate based on device specifications and environmental factors could further enhance performance. Conceptualization, T.K. and B.K. ; methodology, T.K. and B.K. ; software, T.K. ; validation, T.K. and B.K. ; formal analysis, T.K. and B.K. ; investigation, T.K. and B.K. ; resources, T.K. and B.K. ; data curation, T.K. and B.K. ; writing--original draft preparation, T.K. and B.K. ; writing--review and editing, T.K. and B.K. ; visualization, T.K. and B.K. ; supervision, B.K. ; project administration, B.K. ; funding acquisition, B.K.