Artificial Intelligence is changing the way we approach significant decisions in all fields, among which healthcare, industry, and sport. In this context, Deep Learning (DL) is gaining more and more importance thanks to technological development, large dataset availability and cutting-edge results, making it suitable as a support for decision-making and performance analysis. This thesis aims to compare different DL-based methodologies for single person Human Pose Estimation (HPE) from monocular camera videos in gymnastics; in particular, some of the considered exercises include Balance Beam, Uneven Bars, Floor and Vault. This way, an athlete can be monitored in order to assess the performed exercises both for evaluation and training purposes.