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Introduction Dynamic stretching is a particular form of training. Currently, there is little research in academia about dynamic stretching in sports dancing. Objective Explore the role of functional dynamic stretching training in dance sports. Methods 60 sports dancers with a history of ankle injuries were randomly divided into a control and experimental group. All performed a training protocol twice a week, lasting 45 minutes, for eight weeks. A functional dynamic stretching training session was added to the control group. The effects were evaluated by the Cumberland scale, bilateral stability comparison, and balance control by the Perkin system. Data were statistically treated for analysis. Results There was no significant difference between the scores of healthy ankle joints and injured ankle joints in the two groups (P>0.05). After eight weeks of functional dynamic stretching training, there was a significant difference between the experimental and control groups on injured ankle joints (P<0.05). Conclusion Dynamic stretching training can effectively improve ankle joint stability in sports dancers. Concomitantly, this method effectively prevents injuries to the athlete’s ankle joint. Evidence level II; Therapeutic Studies - Investigating the results.

Motion modularity is the main method of motion control for higher animals. That means the complex movements of the muscles are made up of basic motion primitives, and the brain or central nervous system does not care about the specific details of the movement. However, the industrial robot control system does not adopt the technical roadmap of motion modularity, it generates complex trajectories by providing a large number of sampling points. This approach is equivalent to using the brain to directly guide the specific movement of the muscle and has to rely on a faster Fieldbus system to obtain complex motion trajectories. This work constructs a modularized industrial robot trajectory generation component based on Dynamic Movement Primitives (DMP) theory. With this component, the robot controller can generate complex trajectories without increasing the sampling points and can obtain good trajectory accuracy. Finally, the rationality of this system is proved by simulations and experiments.