Institute of Mechanical and Dynamic Engineering, East China University of Science and Technology, Shanghai 200237, China.
School of Design and Art, Shanghai Dianji University, Shanghai 200240, China.
J Healthc Eng. 2022 Mar 9;2022:8388325. doi: 10.1155/2022/8388325. eCollection 2022.
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.
对于渴望成功的运动员来说，由于场地设备、人为误差等因素，很难获得自己的运动数据，这意味着他们无法从体育教练那里获得专业的运动指导和姿势矫正，这是一个灾难性的问题。为了解决这个大问题，结合计算机技术领域深度学习的最新研究成果，基于人体姿势识别的相关技术，本文利用卷积神经网络和视频处理技术，创建了一种运动辅助评估系统，可以获得准确的数据，帮助人们进行互动，从而帮助运动员更好地了解自己的身体姿势和运动数据。研究结果表明：（1）利用 OpenPose 开源库进行姿势识别，通过关节坐标获取关节角度数据，识别和计算视频人体姿势关键点，便于分析。（2）评估视频中人体的动作。这样，就可以判断检测到的目标的动作幅度是否符合标准动作数据。（3）根据本文创建的标准运动数据库，建立正式的运动辅助评估系统；与标准动作相比，检测到的目标的欧式距离越小，越标准。距离为 4.79583 的动作是被测人员的最佳动作。（4）传统方法的效率非常低，基于 BP 神经网络的方法的正确识别率可以高达 96.4%；基于本文的姿态识别方法的正确识别率可以高达 98.7%，比之前的方法高 2.3%。因此，本文提出的方法具有很大的优势。本文提出的运动辅助评估系统的研究结果良好，有效解决了困扰运动员的难题，可以认为已经取得了一定的成功；后续的系统测试和运行工作需要研究人员进一步优化和研究。
