## 3 Materials and Methods
### Dataset Creation
#### 3.1.8 Feature Extraction Tool  
As seen in Figure 4, to choose the proper human pose estimation tool, there are two performance evaluation for this recognition namely--mean average precision (mAP) and Percentage of Correct Key-points (PCK). To compute for Mean average precision, four key metrics were used (Confusion Matrix, Intersection-over-Union, precision and recall) [56]. Percentage of Correct Key-points (PCK) is the metric used for identifying if a detected joint is considered correct. This can be performed by checking if the distance between the predicted and the true joint is within a certain threshold. Specifically, this study used [EMAIL_ADDRESS] = Distance between predicted and true joint <0.2 * torso diameter. The performance of the model is shown in the Figure 4 below. The model has deployed three versions namely--heavy, full and lite. As seen in the figure below, the model outperformed the other existing solutions in mAP and PCK. This feature extraction tool was written in C# using a machine learning solution MediaPipe for high fidelity body pose tracking. This runs inference in small scale tools such as desktops/laptops and mobile phones compared to other machine learning solutions that require powerful desktop environments to generate accurate results. This task of computer vision requires large-scale computational devices to achieve desirable outcome and to run in a real-time basis. The Machine Learning Pipeline consisting of pose detection and tracking each key points must be very fast and requires low latency. Therefore, to achieve a best and fast performance of the detection and tracking, the most visible part of the frame will be the head. So, this could calculate the and detects the location of the person within the frame. From this, it explicitly predicts two additional virtual key points that could describe the center of the human body, rotation and scale. The Pose detector predicts the center of the human body, rotation and scale as a circle. This is essential to obtain the midpoints of a person's hips in the frame, the radius of a circle to map the whole person in the frame and the incline angle of line connecting the shoulder and hip midpoints. This works by running the pose detector in the first frame of the input video that will localize the Region-of-Interest (person) and draw a bounding box. The pose tracker will then predict all the 33 key points and run through all the subsequent frames using the previous frame's ROI. This only call the detection model when it fails to reach the target confidence score (means fails to track the person). Mediapipe was used to recognize and track key points in fast and acceptable manner, and the number of feature points are significantly greater than other existing models. The output of this feature extraction tool is a csv file which contains the x and y locations of the feature points mentioned above. 1. The feature extraction process is performed as follow:
2. The video will be fed into the tool (This could be completed one by one). 3. The video will be processed frame by frame (10 frames per second). 4. The given image resolution of 1280 \(\times\) 720 pixels:  
Figure 4: Comparison of the Performance of the Recognition [55]. * Then these points will then be converted into lines using the Equation 2 below: \[f((x_{1},y_{1}),\ (x_{2},\ y_{2}))=\sqrt{\left(\left(x_{1}-x_{2}\right)^{2}+ \left(y_{1}-y_{2}\right)^{2}\right)}\] (2) However, it does not cover some of the targeted points of this study such as chin, upper, middle, and lower back (see Figure 5). * To recognize these distinct key points, the study will use the common key points of Mediapipe as its base reference for computing the location of each distinct key point and getting its midpoints. * To compute for these distinct feature points, the study will use midpoint formula.