# Scoring of Human Body-Balance Ability on Wobble Board Based on the Geometric Solution
## 6 Discussion
### The Advantages and Disadvantages of Our Method  
Our results for scoring the human body-balance ability on the wobble board, based on the geometric solution, are different according to the chosen pose-estimation algorithms, which extracted the key points of the body.  
We compared the results with two of the most popular HPE models, VideoPose3D and BlazePose GHUM. Figure 11 shows the results of VideoPose3D and BlazePose GHUM for the comparison of three-dimensional human-pose-estimation performance and accuracy. Seventeen double-colored circles represent the results of VideoPose3D, and seventeen red-colored circles represent the results of BlazePose GHUM in white-colored circles. The test was performed under the condition of the same hardware using a 5 s video with 640 \(\times\) 480 dimensions and 30 frames per second (FPS). Two HPE models used 20 min for video processing with a good accuracy result.  
Table 3 shows the results of the pose-estimation quality of the BlazePose GHUM model used in MediaPipe Pose. The result was evaluated through three different validation datasets: Yoga, Dance, and HIIT. The results confirmed that the used model in this study showed high performance with high accuracy. Furthermore, the human pose to keep the balance on the wobble board was similar to the Yoga pose. Thus, there was no severe problem in extracting the key points based on the BlazePose GHUM model.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Methods** & **Pros** & **Cons** \\ \hline \multirow{2}{*}{OpenCV [35]} & – many tutorials to instruct the setting up and employing the models & – difficult to customize \\  & – useful built-in tools for processing and & – the real-time performance \\  & analyzing the content of images’ computer- & is most likely poor \\ \hline \multirow{2}{*}{OpenPose [36]} & – combined full-body keypoints detection & – only non-commercial use \\  & – provide the flexibility of choosing source & – real-time performance is \\  & images from camera fields, webcams, and others & poor if only on the CPU \\ \hline \multirow{2}{*}{PoseNet [37]} & – runs on lightweight devices such as a & – medium accuracy \\  & browser or mobile device & – be able to estimate a single pose or multiple poses & – be deficient for some of the pose estimation applications \\  & – inference time is fast & – no real-time performance, mostly on the CPU \\ \hline \multirow{2}{*}{AlphaPose [39]} & – can detect both single- and multi-person & – only non-commercial use \\  & poses in images or videos & – mostly does not perform \\  & – pose tracking & in real time on CPU \\  & – high accuracy & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The Pros and cons of five kinds of human-pose-estimation (HPE) libraries and models.  
Our method offers many advantages in cost, portability, reduced set-up time, and ease of understanding without any expertise. These originate from the use of off-the-shelf, low-cost hardware. The scoring algorithm described here is the new method with PPP to score the human balance on the wobble balance board. The user can understand balance ability and the balance improvement after a balance-training cycle from this balance score. According to calculating the max_a, the balance ability of the human body on the wobble balance board was affected by factors such as the height of the user (the COM position) and the foot position. Besides that, the hardware used here has certain limitations that can be listed. The balance board can only support limited weight. The limitations of RGB cameras also affect the key body points for extracting, for example, the camera's improper lighting and focal length. Objective factors such as the user's clothes and the user's position can adversely influence the measurement.