What and how well you exercised? An efficient analysis framework for fitness actions.

• An efficient and robust fitness action analysis framework to analyze fitness actions with computer vision. • A 2D/3D Fitness-28 dataset with 7530 action videos, which can be used to evaluate action analysis algorithms. • An efficient 2D/3D skeleton feature encoding method for action analysis, which is robust to spatio-temporal variation. • A local-global geometrical registration strategy for action assessment, which can evaluate the action performance effectively. Human action analysis has been an active research area in computer vision, and has many useful applications such as human computer interaction. Most of the state-of-the-art approaches of human action analysis are data-driven and focus on general action recognition. In this paper, we aim to analyze fitness actions with skeleton sequences and propose an efficient and robust fitness action analysis framework. Firstly, fitness actions from 15 subjects are captured and built to a fitness action dataset (Fitness-28). Secondly, skeleton information is extracted and made alignment with a simplified human skeleton model. Thirdly, the aligned skeleton information is transformed to an uniform human center coordinate system with the proposed spatial–temporal skeleton encoding method. Finally, the action classifier and local–global geometrical registration strategy are constructed to analyze the fitness actions. Experimental results demonstrate that our method can effectively assess fitness action, and have a good performance on artificial intelligence fitness system. [ABSTRACT FROM AUTHOR]