## 5 Discussion
### Comparison with Existing Research
A machine-learning AI model analyzes posture data. Over the past 50 years, sports motion analysis systems have developed, making understanding sports mechanics more challenging. Although data acquisition equipment has improved, understanding data processing remains essential for sports [10]. Thus, the authors decided to use these technologies in our paper. The HeadUp vest is a training tool designed to block a child's gaze towards the ball during training. It requires a developed ball sense and optimal intersegmental coordination ability. The vest is made of sponge, safe, comfortable, and lightweight. It forces players to lift their gaze from the ground and adapt to new conditions. The vest also promotes reflexes, clairvoyance, and peripheral vision development. The action system includes various types of exercises, such as standing coordination exercises, running coordination exercises, and themed games. Assessment instruments include the Adams test, T-Blaze test training, and time spent playing with the head up and down. The Adams test measures coordination skills by dividing a line into four spaces and aiming to perform as many jumps as possible within 15 s.  
In the current research, it was also calculated the average time spent playing with the head down in experimental group T1 = 342.19 s (\(p\) = 0.89) \(\rightarrow\) T2 = 305.05 s (\(p\) = 0.105) \(\rightarrow\) T3 = 191.05 s (\(p\)-value = 0.005) and control group T1 = 339.43 s (\(p\) = 0.89) \(\rightarrow\) T2 = 341.14 s (\(p\) = 0.105), T3 = 259.81 s (\(p\)-value = 0.005). One may observe the difference between experimental and control T1 = 2.76 s \(\rightarrow\) T2 = \(-\)36.09 s \(\rightarrow\)T3 = \(-\)68.81 s. The consistently large decrease in this difference highlights the effectiveness of the Blaze technology application of the Adam test and the effectiveness of the training program, which is based on the cognitive, behavioral, attention, perception, and tracking of multiple objects. These factors have a positive impact on running, ball control, diribbling, direction changes, coordinationskills, and task-solving abilities. They also have a positive impact on lowering negative emotions like anxiety and depression. Game analysis is performed using the OpenPifPaf project, a machine-learning artificial intelligence model, and Python programming language. OpenPifPaf is more efficient in processing speed and accuracy than other methods, such as CrowdPose or PoseTrack2018. It is suitable for analyzing pictures and videos with larger numbers of people, making it suitable for football game analysis. In the current research, it was also calculated the number of passes, based on OpenPifPaf technology, on experimental group T1 = 16.43 passes (\(p\) = 0.45) \(\rightarrow\) T2 = 18.76 passes (\(p\) = 0.14) \(\rightarrow\) T3 = 26.67 passes (\(p\)-value = 0.005) and control group T1 = 15.43 passes (\(p\) = 0.45) \(\rightarrow\) T2 = 17 passes (\(p\) = 0.14) \(\rightarrow\) T3 = 18.86 passes (\(p\)-value = 0.005). One may observe that the difference between the experimental and control regarding the number of passes increased in favor of the experimental group: T1 = 1 passes \(\rightarrow\) T2 = 1.76 passes \(\rightarrow\) T3 = 5.81 passes. Once again, our research proved that OpenPifPaf is very accurate in data processing and helps trainers and athletes understand sports mechanics.