## 2 Literature Review
To perform game analysis, the authors used the OpenPifPaf v0.13.11 project to collect data and the PythonTM programming language to process these data and calculate the gaze angle. A machine-learning artificial intelligence model specialized in analyzing and collecting data on a person's posture in different poses was also used. Following a review of the last 50 years, there has been considerable development of sports motion analysis systems in terms of computer-motion modeling. Understanding the mechanics of sports techniques has become a profound challenge in many sports, with the musculoskeletal models being increasingly detailed. If it was quite difficult in the past to provide data acquisition equipment, presently, this factor is less limiting, but the fundamental understanding of how data are processed is still imperative [10]. Using the OpenPifPaf pose estimation technique, the authors performed key-point-based movement analysis by measuring reference and capacitive ECG synced with a video recording [42; 43]. OpenPifPaf is based on deep neural network architecture and uses supervised learning techniques to learn to locate and track human joints. The neural network uses multi-class learning techniques to classify and regress joint positions in an image in parallel. An interesting aspect of OpenPifPaf is that it can detect and analyze human joints, even in the case of partially visible objects or in complex joint overlapping situations. This makes it suitable for applications such as real-time posture tracking, gesture analysis, and human activity recognition. OpenPifPaf is available as open source and provides detailed documentation, usage examples, and source code to help researchers and developers use it in their projects. It is an active project that continues to be developed and improved by its community [12]. Having in mind this context the research's main goal is to show how well a modified training program, which includes an exercise that combines reaction speed work with a decreased visibility job to enhance passing, can increase passing. During the training program, the authors decided to exercise with Heads Vests to train the athletes to play more time with the gaze upwards. To evaluate the impact of the training based on the [PERSON]'s method the authors used T-BlazePod (BlazePod Israel Ltd., Tel Aviv, Israel) to avoid the inaccuracy of the timer and to add technology to the classic test (\(t\)-test). The authors also choose to implement the Adam test for coordinative performance. For data collection and game performance analysis, the authors implemented OpenPifPaf facilities. Given the research objective, the main hypothesis of the study, H1, arises: Training the children to play football with their heads up for a longer time will improve the number of passes that are successful and decrease the number of passes that are incorrect during a soccer match. **H1. **: _There are significant statistical differences between the Experimental group/Control group regarding results in the Adams test._
**H2. **: _There are significant statistical differences between the Experimental group/Control group regarding Time spent playing with the head up._
**H3. **: _There are significant statistical differences between the Experimental group/Control group regarding Time spent playing with the head down._
**H4. **: _There are significant statistical differences between the Experimental group/Control group regarding the Number of successful passes._