# Multi platform Computer Vision System to Support Physical Fitness Assessments in Schoolchildren
## 2 Materials and Methods
### Multiplatform Implementation
#### 2.3.1 Business Understanding  
A descriptive cross-sectional study was carried out in primary and secondary schoolchildren. The current subjectivity in physical fitness evaluations in schools and the lack of a protocol for collecting and digitizing this data type is a problem that has not been completely solved. This study was carried out in three stages: the first involved the traditional data collection process of anthropometric measurements and physical tests, generally using instruments such as a stopwatch and tape measure. The second involved filming eachphysical test according to protocols. The third stage consisted of evaluating all the recorded videos using computer vision to capture the number of repetitions and the distance reached in the tests. Two environments were used. The first was a laboratory environment using Azure Kinect cameras, where we performed the initial tests. The second was a field environment, the schools where the students' data were taken using Canon video cameras. Finally, the validation environment was performed through a mobile device and by physical education teachers. The characteristics of the laboratory environment are that it is a controlled environment, where testing can be performed with the Kinect Azure cameras and the software running on the workstation. Here, you can maintain the lighting level, the number of exercise shots, and other variables to consider when performing the tests. In this environment, it is only necessary to collect high-precision data using the cameras and then analyze the collected data. Therefore, it is only required that each test evaluation module works independently, can take data from the exercises carried out, and can be executed at will. The second environment considered is the schools in the province of Arequipa (Peru) that agreed to participate in the study. Canon cameras were used to record the physical fitness tests effectively and quickly. These videos are then processed in the desktop version of the developed software to allow analysis using computer vision techniques, and the results can be compared with control data taken in the field. In this case, it is only necessary that the system be able to receive and analyze previously recorded videos to extract valuable data for each exercise. Subsequently, after having collected and analyzed the data and obtained the percentiles of the student's physical fitness level, these can be used to determine the results of the student's performance in the tests and indicate whether they are above average, average, or below average in physical fitness based on your age and gender. In this case, it is only necessary that the system can be used in a mobile environment, where the teacher records a student while taking the test; the system analyzes the exercise performed and indicates the student's level of physical fitness. In this context, the teacher must enter the student's anthropometric data and then take the video of the test performed. Each environment has necessary hardware requirements. In the laboratory environment, to obtain the best possible precision due to the use of the Azure Kinect DK cameras [13], a laptop with the Windows 11 operating system, Intel core 9 processor, and RTX 3070 graphics card with 16 GB of memory was used. RAM and 1 TB M.2 SSD + storage was also used. One of the fundamental points for the development of the proposal is the use of sensors that capture people's movements in the different defined tests, which is why the comparison of three cameras was carried out in their most appropriate use according to the advantages. Each one depends on the environment; in Table 1, we can see this comparison, where we find that the Microsoft Kinect DK cameras are ideal in laboratory tests due to the specialization of their sensors for the objective of our study. The Canon EOS m50 MarkII cameras were used in the field tests due to their high ease of transport and logical configuration approach to cameras on mobile devices.