Wang Yuxuan, Yang Xiaoming, Wang Lili, Hong Zheng, Zou Wenjun
Sports Institute, Nanchang JiaoTong Institute, Nanchang, China.
Graduate School, University of Perpetual Help System Dalta, Las Piñas, Philippines.
Front Neurorobot. 2022 Apr 28;16:857595. doi: 10.3389/fnbot.2022.857595. eCollection 2022.
At present, there are many kinds of intelligent training equipment in tennis sports, but they all need human control. If a single tennis player uses the robot to return the ball, it will save some human resources. This study aims to improve the recognition rate of tennis sports robots in the return action and the return strategy. The human-oriented motion recognition of the tennis sports robot is taken as the starting point to recognize and analyze the return action of the tennis sports robot. The OpenPose traversal dataset is used to recognize and extract human motion features of tennis sports robots under different classifications. According to the return characteristics of the tennis sports robot, the method of tennis return strategy based on the support vector machine (SVM) is established, and the SVM algorithm in machine learning is optimized. Finally, the return strategy of tennis sports robots under eight return actions is analyzed and studied. The results reveal that the tennis sports robot based on the SVM-Optimization (SVM-O) algorithm has the highest return recognition rate, and the average return recognition rate is 88.61%. The error rates of the backswing, forward swing, and volatilization are high in the return strategy of tennis sports robots. The preparation action, backswing, and volatilization can achieve more objective results in the analysis of the return strategy, which is more than 90%. With the increase of iteration times, the effect of the model simulation experiment based on SVM-O is the best. It suggests that the algorithm proposed has a reliable accuracy of the return strategy of tennis sports robots, which meets the research requirements. Human motion recognition is integrated with the return motion of tennis sports robots. The application of the SVM-O algorithm to the return action recognition of tennis sports robots has good practicability in the return action recognition of tennis sports robot and solves the problem that the optimization algorithm cannot be applied to the real-time requirements. It has important research significance for the application of an optimized SVM algorithm in sports action recognition.
目前，网球运动中有多种智能训练设备，但都需要人工控制。如果单个网球运动员使用机器人来回球，将节省一些人力资源。本研究旨在提高网球运动机器人在回球动作和回球策略方面的识别率。以网球运动机器人的以人为本的动作识别为出发点，对网球运动机器人的回球动作进行识别和分析。利用OpenPose遍历数据集对不同分类下网球运动机器人的人体运动特征进行识别和提取。根据网球运动机器人的回球特点，建立了基于支持向量机（SVM）的网球回球策略方法，并对机器学习中的SVM算法进行了优化。最后，对网球运动机器人在八种回球动作下的回球策略进行了分析研究。结果表明，基于支持向量机优化（SVM-O）算法的网球运动机器人回球识别率最高，平均回球识别率为88.61%。网球运动机器人的回球策略中，后摆、前摆和挥拍的错误率较高。在回球策略分析中，准备动作、后摆和挥拍能取得更客观的结果，均超过90%。随着迭代次数的增加，基于SVM-O的模型仿真实验效果最佳。这表明所提出的算法在网球运动机器人回球策略方面具有可靠的准确性，满足研究要求。人体运动识别与网球运动机器人的回球动作相结合。将SVM-O算法应用于网球运动机器人的回球动作识别，在网球运动机器人的回球动作识别中具有良好的实用性，解决了优化算法不能应用于实时需求的问题。这对于优化后的SVM算法在体育动作识别中的应用具有重要的研究意义。
