Gymnastics Action Recognition and Training Posture Analysis Based on Artificial Intelligence Sensor

Abstract

In order to solve the problems that the gymnastics action recognition system cannot select gymnastics training items and difficulty modes, the calculation matching degree and its threshold angle are inaccurate, the effect and efficiency of action learning are low, and this research proposes a gymnastics action recognition and training pose analysis methods based on artificial intelligence sensors. This method completes the performance improvement of the traditional human action recognition algorithm and uses the skeletal features of the Kinect sensor to discriminate sports actions. Clustering based on static $K$ -means algorithm increases the accuracy of pose selection, and each pose is recognized by human action using artificial neural network (ANN) and hidden Markov model (HMM). The obtained results are as follows: comparison of nonstatic and proposed static $K$ -means algorithm on the training set and the overall accuracy of the proposed method is much better than the previous method. Among the four movements, the accuracy rate of “sitting” and “standing” movements is significantly higher, reaching 100%. In the gymnastics action recognition experiment, the average recognition rate of the system in this research is 93.6%, the false rejection rate is 5%, and the false acceptance rate is only 1.4%. It is proved that the system interface designed in this research can prompt the part that needs to be corrected, display the error on the output device, more efficiently assist the user to perform targeted training on the action to be learned, and improve the effect and efficiency of action learning.