## 2 Materials and Methods
### Measurement  
Since it is difficult to accurately determine the operation timing of the 4 DEYE and VICON systems, the minimum and maximum areas of each data point were analyzed at the same time and compared with each other. In addition, after selecting one representative indicator of 4 DEYE and VICON, the minimum and maximum areas of the indicator were applied to all indicators. Up-sampling was performed to match the same 100 frames of 4 DEYE and VICON using MATLAB because of the difference in the number of image frames between 4 DEYE and VICON. The correlation between the two data types was obtained and the data were analyzed according to the correlation with the graph image; the measurement process is shown in Figure 2a.  
Figure 2: _Cont._  
Figure 2: Scheme of (**a**) analysis procedure and images of matching, (**b**) VICON, (**c**) 4 DEYE, (**d**) Reference point for calculating hip angle, (**e**) Reference point for calculating knee angle, and (**f**) Reference point for calculating trunk angle.  
The analysis methods used for the 4 DYE and VICON systems were slightly different, resulting in some differences in angle values. This is because the operation time of 4 DYE and VICON must be set manually, not automatically, resulting in experimental errors. After the VICON device was activated, the processed data was standardized to have 100 frames. However, the data that were not processed after operation showed some significant differences in angle value compared with 4 DYE. The images for matching VICON and 4 DYE are shown in Figure 2b,c.  
Hip flexion measures the angle of the hip-joint vector (the line connecting the hips and knees) compared to the upper-body-center line (the line connecting the center of both shoulders and the center of both hip joints on the z-axis). The picture above shows the right side as an example, so for convenience, it is assumed that the upper-body-center line has been moved parallel to the right. Therefore, hip flexion measures the angle of the thigh vector (line connecting numbers 18 and 19 in Figure 2d) compared to the upper-body-center line moved parallel to the right. Knee flexion measures the angle between the thigh vector (the line connecting numbers 18 and 19 in Figure 2e) and the calf vector (the line connecting 19 and 20 in the picture). Trunk rotation measures the angle between the y-axis (left and right reference lines) and the straight line connecting both shoulders (line connecting numbers 6 and 12 in Figure 2f) on the horizontal plane (x-y plane).  
Currently, the mark-based VICON system is widely used for motion analysis (ex, clinical field) and is used as a standard analysis system for human motion analysis [15; 16] because it does not require a separate wired device, and the weight of the marker is light. However, the marker must be artificially attached to each subject, the preparation for research is complicated, and there are many spatial constraints because the marker is very sensitive to light [17]. In addition, the device system is expensive due to the use of expensive infrared cameras [18; 19].  
In general, the RGB sensor systems combines a 3D reconstruction algorithm based on a deep learning multi-view 2D image to estimate a 3D posture. RGB-D sensors for quantitative evaluation or motion analysis can be a good alternative to existing sensors [20], and commercial RGB sensor devices with motion analysis describes 4 DYE(r). Existing deep-learning-based posture estimation models are OpenPose made by Carnegie Mellon University and MediaPipe made by the Google company. There are two approaches: one is a bottom-up approach that first recognizes the human segment on the image and then finds and groups the main parts of each individual based on the detection results. The other is a top-down approach that first recognizes the main parts of the entire image and then constitutes the human segment for each individual. In addition, the latest computer vision technology can analyze movement by integrating and converting multiple 2D images into 3D [21].