## 3 Datasets and Angular Features
### Shooting Data of Japanese Sign Language  
We created the Japanese sign language videos used in this study by inviting sign language speakers to record the finger-spelling actions. We present the details of the filming below. * A total of 46 Japanese syllables and specified words were recorded. * Five letters with the same consonant were filmed in sequence. * The sign language speaker was photographed from the waist up in the frame. * The recording was taken with the subject facing forward, and the left-right tilt was kept within 15 degrees. * A green screen background was used for the filming. * The images were taken without masks while the speaker was speaking. * Filming was conducted in Full HD (1920 \(\times\) 1080) quality at 60 frames per second, and the data were compiled in mp4 format. * Each of the 46 different letters was photographed 6 times. In this study, we categorized the filmed videos as spelling videos (Figure 1), which comprise a series of finger-spelling videos for the syllables '& |a|' to '& |n|', and word videos, which depict several words in finger spelling. We invited one male and one female for filming. The filming team recorded three takes of each person for the spelling videos and one take for the word videos. We filmed the spelling videos in three different sessions. First time, participants used no special posture to enable natural fingerspelling movements,second time, filming was conducted with sleeves rolled up to prevent overlapping of body and finger spelling, and third time, filming maintained the same position, without the need for rolled-up sleeves. We conducted the shooting for the word videos in the same posture used in the third session of the spelling videos. Table 1 shows the final composition of the videos. The table shows that the average value for the first five-character video of the woman is smaller than the other data. The reason for this is that the woman was told to \"go as fast as usual\" during the first shooting. After the second shooting, the value became larger because the female subject made a sign language movement a little slower for the shooting. The average value  
By utilizing the extracted coordinate data, we obtained 20 angles for each finger joint, as illustrated in Figure 2a, and 20 features that indicate the overall tilt of the hand, as displayed in Figure 2b, employing cosine values. The datasets in our experiments were classified into three types based on the various combinations of these angular features. 3. Since feature data is acquired frame by frame, the size of the input data varied with the length of the video. To address this, we applied an interpolation method, commonly used in object detection techniques, to standardize the size of the uneven input data extracted from the videos. These results generated a two-dimensional matrix with a uniform number of frames. 4. From this standardized two-dimensional matrix, we extracted multiple random frames, which reduced the frame count and created a smaller two-dimensional matrix. This process, when repeated, allowed us to augment the data, resulting in 324 data points per finger-spelling character. Two types of angular features were used in this study. The first type focuses on three points centered on the joint of interest and determines the angle of each finger joint, as shown in Figure 2a. The second type evaluates the overall tilt of the hand, as shown in Figure 2b. As shown in Figure 2b, the angle was determined using a reference vector and the tilt from it. In both cases, we calculated the vector from the coordinates of each joint to the adjacent joint and computed its cosine value to measure the opening of each joint. In Figure 3 and Equation (1), we can see the calculation method and the positional relationship of the coordinates. To obtain the angle shown in red in Figure 3, the coordinates of the three points obtained by MediaPipe are (x1, y1) (x2, y2) (x3, y3), as shown in the figure. (xn, yn) indicate the x-coordinate and y-coordinate of each joint obtained by Mediapipe, respectively.