The computer vision field includes tasks like image classification, object recognition, and feature detection. The focus of this work is body pose estimation, where the human motion is analyzed using human activity recognition algorithms through pattern detection principles. The increasing popularity of cost-effective mobile sensors like Microsoft Kinect has led to the development of various algorithms for activity recognition and tools that enable sport performance analysis and motion rehabilitation at home. These algorithms have the potential to promote a healthy lifestyle, discourage unhealthy habits, and aid in condition tracking, particularly in sports science and healthcare applications. Therefore, in this work we will use data collected with RGB cameras to detect and classify sports movements and exercises involving both the upper and the lower body.