Evaluation of Student's Posture Using Motion Video Analysis, Extraction of Anatomical Point Parameters, and Critical Duration of Postures

This article discusses the development and implementation of an algorithm for the automatic analysis of schoolchildren's posture using video recordings. The study aims to create an effective tool for detecting posture deviations through biomechanical features, such as shoulder and head tilt angles, distances between anatomical points, and other parameters. The algorithm involves the use of the BlazePose neural network for extracting body key points, identifying irrelevant frames, and analyzing time-series data. The research methodology is based on the application of computer vision techniques and biomechanical feature analysis, followed by data visualization and automated report generation. The results demonstrate that the proposed algorithm effectively identifies posture deviations, providing visual feedback for the prevention and correction of potential disorders. The automation of the process enables large-scale monitoring of schoolchildren's posture and contributes to the prevention of chronic musculoskeletal disorders.