Aided Evaluation of Motion Action Based on Attitude Recognition

Abstract

For athletes who are eager for success, it is difficult to obtain their own movement data due to field equipment, artificial errors, and other factors, which means that they cannot get professional movement guidance and posture correction from sports coaches, which is a disastrous problem. To solve this big problem, combined with the latest research results of deep learning in the field of computer technology, based on the related technology of human posture recognition, this paper uses convolution neural network and video processing technology to create an auxiliary evaluation system of sports movements, which can obtain accurate data and help people interact with each other, so as to help athletes better understand their body posture and movement data. The research results show that: (1) using OpenPose open-source library for pose recognition, joint angle data can be obtained through joint coordinates, and the key points of video human posture can be identified and calculated for easy analysis. (2) The movements of the human body in the video are evaluated. In this way, it is judged whether the action amplitude of the detected target conforms to the standard action data. (3) According to the standard motion database created in this paper, a formal motion auxiliary evaluation system is established; compared with the standard action, the smaller the Euclidean distance is, the more standard it is. The action with an Euclidean distance of 4.79583 is the best action of the tested person. (4) The efficiency of traditional methods is very low, and the correct recognition rate of the method based on BP neural network can be as high as 96.4%; the correct recognition rate of the attitude recognition method based on this paper can be as high as 98.7%, which is 2.3% higher than the previous method. Therefore, the method in this paper has great advantages. The research results of the sports action assistant evaluation system in this paper are good, which effectively solves the difficult problems that plague athletes and can be considered to have achieved certain success; the follow-up system test and operation work need further optimization and research by researchers.