This study focuses on the application of artificial intelligence behavior constraints in the analysis of mechanical injuries in dance training, aiming to accurately analyze and effectively prevent mechanical injuries during dance training through the introduction of artificial intelligence technology. Dance, as a highly dependent art form on physical skills, often comes with a certain risk of mechanical injury during its training process. In this study, we first reviewed the relevant theories of mechanical injuries in dance training and analyzed the inherent relationship between dance movements and mechanical injuries. Subsequently, we utilized artificial intelligence technology to conduct behavior constraint analysis on the dance training process. By constructing a dance action recognition model, we achieved real-time monitoring and evaluation of dance training actions. On this basis, we further utilize the principles of mechanics to quantitatively analyze dance movements and extract key factors that affect mechanical damage. Through in-depth analysis and comparison, this study found that mechanical injuries in dance training are mainly influenced by various factors such as movement standardization, training intensity, and individual differences. We applied the theory of sports biomechanics to study sports dance injuries and analyzed the causes of athlete injuries. By exploring more scientific training methods and means, the correlation coefficients between main joint muscle strength, proprioception, tibialis anterior muscle imbalance response time, and rotational stability were measured to study proprioception training suitable for sports dance, adhering to the principle of gradual progression. In future sports dance rotation training, corresponding training should be carried out according to the characteristics of different rotation steps, providing reference for strengthening leg training and ankle training.
Wang et al. (Sat,) studied this question.