# Multi platform Computer Vision System to Support Physical Fitness Assessments in Schoolchildren
## 1 Introduction  
The increase in the prevalence of sedentary lifestyles among schoolchildren due to the pandemic has acquired considerable importance due to its adverse effects on physical and mental health. Significant changes are observed in the habits of some population sectors, increasing obesity rates [1, 2]. This observed trend is worrying, especially in children and young people. However, this problem contains many factors that can contribute to suffering from the disease, making it difficult to control. In general, it is known that to avoid this evil, we must try to maintain good eating habits and physical activity. However, for any corrective action, data are needed as a starting point that allows us to understand the context and characteristics of the problem to reach a viable solution. More information about the physical fitness of children and youngpeople needs to be provided. Good physical fitness is essential for schoolchildren to ensure healthy development, improve academic performance, and promote active lifestyle habits. It can prevent chronic diseases in the future. The evaluation and monitoring of physical fitness in schoolchildren is classically carried out through field physical test measurements; however, these measurements tend to be subjective and depend primarily on the experience of the teacher or professional who performs the measurements. This prevents a physical condition measurement process that is sufficiently reliable in the data collection process. Therefore, in recent years, advances in sports video analysis and computer vision techniques in school physical education and sports have significantly improved a series of operations that allow rapid and accurate evaluation [3; 4]; these techniques provide enhanced information, such as detailed complex analyses of sporting activity [3], and offer non-intrusive methods to monitor, evaluate, and promote physical fitness among youth. In this sense, school systems must innovate in the methodologies and contents of the physical education subject due to the advancement of technology and the importance of motivating students to perform physical exercise [4]. For this reason, it is important to include computer vision since it has become an essential tool in various applications, especially in physical activity and sports evaluation. Recent advances in computer vision and artificial intelligence, combined with the ubiquity of high-powered smartphone and camera devices, offer opportunities for innovative digital health solutions to monitor and improve movement health [5]. Recently, a study highlighted the importance of monitoring children's physical activity in a non-intrusive way, using specialized cameras and relying on artificial intelligence algorithms, which allow the analysis of movement and behavior patterns in real-time, facilitating the early identification of possible health problems and promoting more effective interventions [6]. Furthermore, in recent years, several studies have emphasized the recognition of activities and gestures and tracking of human movement patterns in children, adults, and people who need assistance (for example, in environments such as parks and schoolyards), in which the computer vision technique was used [7; 8; 9]. These studies demonstrated a high degree of agreement with actual physical activity data, which reflects the reliability and precision of these technologies to provide detailed and objective information, allowing a more precise and efficient evaluation of the physical condition. In essence, physical fitness in schoolchildren is essential to ensure healthy development, improve academic performance, and promote active lifestyle habits that prevent chronic diseases in the future. However, traditional fitness assessment methods based on field testing are subjective and highly dependent on the assessor's experience, which prevents reliable and accurate data collection.