## 1 Introduction  
Recently, with the improvement in computing performance of mobile devices such as smartphones, there has been active research on AI-powered mobile applications utilizing artificial intelligence models. For example, various AI-based services are continuously being developed in fields such as education, real-time translation, and privacy protection [1; 2; 3]. In relation to AI-powered mobile applications, research aimed at improving communication between hearing and deaf individuals through the use of artificial intelligence has also been actively conducted [4; 5; 6; 7; 8; 9]. A notable example is the use of avatars in sign language services [10; 11; 12]. In contemporary society, mobile devices play a significant role in communication, information accessibility, and service usage. In this context, recognizing and translating sign language in mobile environments is crucial not only for deaf individuals but also for effectively facilitating smooth communication with hearing individuals. However, recognizing and translating sign language in the current mobile environment has limitations [13]. One major limitation is that mobile environments lack the computational power compared to server environments [14; 15], resulting in fewer image framesbeing extracted within the same time period. If the number of extracted frames is too small, the performance of the sign language recognition model may decrease. Additionally, accurately detecting the start and end points of a sign language utterance is critical for improving the accuracy of sign language recognition models. As seen in automatic speech recognition (ASR), the failure to accurately detect start and end points can severely degrade the performance of the recognition system [16]. The period before the start and after the end of a sign language utterance often involves no action being performed. Such segments do not adequately capture the unique characteristics of each sign language. Therefore, including these segments in the training of a sign language recognition model can reduce its accuracy. Addressing these challenges is essential for enhancing the performance and reliability of sign language recognition on mobile devices. To accurately detect the start and end points of a sign language utterance in a mobile environment, it is necessary to dynamically adjust the sampling rate based on the number of frames extracted, rather than relying on a fixed sampling rate, as proposed in previous research [17]. This is because when fewer frames are extracted during a sign language utterance, the movement changes between consecutive frames are greater compared to when more frames are extracted. Therefore, it is necessary to adjust the sampling rate to determine the start and end points of the sign language utterance based on the number of extracted frames. This approach can better accommodate the computational limitations of mobile environments compared to server environments. In previous research [17], the focus was on detecting the start and end points of sign language utterances by analyzing keypoint variations in sign language videos. This research was conducted in standard computing environments, such as personal computers or server computers. However, the method proposed in that research is not as suitable for resource-constrained mobile computing environments as it is for server environments. This is because mobile environments have varying computational performance across different devices, leading to differences in the number of sign language utterance image frames that can be obtained within the same time period. In practice, the average frame rate was measured to be \(17\) fps when extracting keypoint images during real-time sign language utterances on a Galaxy \(Z\) Flip4 (Snapdragon 8 Gen 2). Additionally, if multiple applications are running on a mobile device, the frame rate may decrease. Similarly, high memory usage on the mobile device can also cause a drop in frame rate [18].