## 2 Materials and Methods
### Hitting Pose Analysis  
Evaluating the test player's performance in terms of the trajectory and landing point of the ball is necessary. However, even if the test player can hit the ball onto the table using an improper technique, it can be detrimental to their long-term training in proper table tennis skills. Therefore, the correctness and technicality of the test player's movements are also important evaluation metrics for scoring their table tennis performance. Currently, there are two main approaches to motion capture: sensor-based and vision-based solutions. Sensor-based solutions rely on specialized sensors and devices to capture and track the movements of the test player. In contrast, vision-based solutions utilize computer vision techniques to extract motion information from video or image sequences. These methods involve the use of cameras or depth sensors, such as Kinetic [29], to capture the movements of the test player. Both sensor-based and vision-based approaches have their advantages and limitations. Since vision-based solutions offer a more flexible and non-intrusive setup than sensor-based solutions, we chose vision-based solutions for our proposed method. Our proposed framework is shown in Figure 3. We divided the presentation into two parts to discuss the details of this framework. The first part of our proposed framework utilizes existing videos or recorded files from smartphone or computer cameras to estimate the skeleton point of the test players in the  
Figure 3: The framework of body skeleton tracking and scoring. video footage. Human pose estimation has a wide range of applications, such as quantifying sports exercises, sign language recognition, and full-body gesture control. Representative methods include MediaPipe [30] and YOLOv7 [31]. MediaPipe is an open-source project by Google that provides a cross-platform ML solution for high-fidelity human pose tracking. It leverages the research of BlazePose and obtains 33 2D landmarks (or 25 upper-body landmarks) from RGB video frames. Additionally, MediaPipe Pose's methods allow for real-time computation and estimation on most modern smartphones and even on the web. Based on these above advantages, we choose to use MediaPipe 0.10.21 as our method for human pose estimation. After obtaining the skeletal data of the test player, we select the three key joints that have the most significant impact on table tennis strokes and calculate the angles among them. These key joints are the elbow, shoulder, and hip of the grid hand of the table tennis player. These angles serve as the input data for the second part of the framework for evaluating the test player's hitting actions. The second part of our proposed framework is detailed as follows: First, we use a wavelet denoising method to eliminate noise from the data. Since the test player's movements are recorded continuously, a complete video will contain multiple repetitions of hitting actions (typically, the tested athlete is asked to perform ten consecutive hitting actions). By defining a time window function, as shown in Figure 3, we can identify the starting point of each repeated action by detecting a rapid decrease in the elbow joint angle (Figure 4). This allows us to separate the repeated hitting actions into individual actions. After sliding window segmentation, we apply wavelet denoising to the angle data [32]. Once these steps are completed, we can compare and score the actions against those of a standard athlete. The MediaPipe system estimates 2D body key points from RGB camera input and infers relative depth cues based on a learned regression model. While this approach is  
Figure 4: Skeleton model in computer setting with real player. cost-effective, real-time capable, and easy to deploy, it does not provide true 3D spatial measurements of joint positions. We acknowledge that relying solely on 2D key points may lead to potential inaccuracies, particularly in cases of out-of-plane motion, varying camera perspectives, or depth-related ambiguities.