The research project focuses on the design and implementation of an electronic system for correcting athletic gestures in race walkers. Convolutional neural network technology is used to analyze athletes’ movements and detect key points in their gestures. The tracking system prototype consists of a presence sensor, stepper motor, servomotors, cameras, and limit switches to collect images and analyze movements. The objective is to improve race walking techniques, prevent injuries due to physical overexertion, and enable more efficient correction for practitioners. The project has demonstrated a 98% efficiency in key point detection and movement analysis, thanks to the use of technologies such as machine learning, computer vision, and deep learning.