AI-driven personalized training model for dance movement and health management research

In this paper, a personalized scheme recommendation method for dance movements based on ontological similarity is proposed.An ontology model of trainers is established, and in order to explore the interactions between trainers' attribute features and their influence on core parameters, SWRL rules are established using Jena inference engine for the inference of core parameters of training programs.The similarity degree is calculated according to the different types of user variables respectively, and the artificial neural network model is used to determine the degree of similarity between different trainers, in order to complete the recommendation of personalized training programs for dance movements.And then the requirements of the system are summarized to achieve the framework construction of the personalized dance movement training program recommendation system to achieve the health management in the training process.The recommendation effects presented by the similarity calculation method of this paper have reached the design goal of this paper, and the personalized recommendation system of this paper has also significantly improved the physical fitness level and the performance effect of dance skills of the experimental group of dance trainees, and the success rate of the kicking back leg movement has reached 91.67%.However, the system's function of improving health knowledge and health awareness needs to be further upgraded.