Liu Jie, Wang Le, Zhou Hang
Sports Training and Health Care, Zhoukou Normal University, Zhoukou, China.
Sports Training, Zhongyuan University of Technology, Zhengzhou, China.
Front Psychol. 2021 Jul 22;12:677590. doi: 10.3389/fpsyg.2021.677590. eCollection 2021.
The purposes are to digitalize and intellectualize current professional sports training and enrich the application scenarios of motion capture technology of moving targets based on artificial intelligence (AI) and human-computer interaction (HCI) in sports training. From an educational psychology perspective, sport techniques are a cognitive ability of sports, and a tacit knowledge. However, sports technology, language, image, and other methods play an auxiliary role in sports training. Here, a General Framework of Knowledge-Based Coaching System (KBCS) is proposed using the HCI technology and sports knowledge to accomplish autonomous and intelligent sports training. Then, the KBCS is applied to table tennis training. The athletic performance is evaluated quantitatively through the calculation of the sports features, motion recognition, and the hitting stage division in table tennis. Results demonstrate that the speed calculated by the position after mosaicking has better continuity after the initial frame of the unmarked segment is compared with the end frame of the market segment. The typical serve and return trajectories in three serving modes of slight-spin, top-spin, and back-spin, as well as the trajectories of common services and return errors, are obtained through the judgment of the serving and receiving of table tennis. Comparison results prove that the serving accuracy of slight-spin and back-spin is better than that of top-spin, and a lower serve speed has higher accuracy. Experimental results show that the level distribution of the three participants calculated by the system is consistent with the actual situation in terms of the quality of the ball returned and the standard of the motion, proving that the proposed KBCS and algorithm are useful in a small sample, thereby further improving the accuracy of pose restoration of athletes in sports training.
目的是使当前的专业体育训练数字化和智能化,并丰富基于人工智能(AI)和人机交互(HCI)的运动目标运动捕捉技术在体育训练中的应用场景。从教育心理学的角度来看,运动技术是一种体育认知能力,是一种隐性知识。然而,运动技术、语言、图像等方法在体育训练中起辅助作用。在此,提出了一种基于知识的教练系统(KBCS)通用框架,利用人机交互技术和体育知识来实现自主和智能的体育训练。然后,将KBCS应用于乒乓球训练。通过计算乒乓球的运动特征、动作识别和击球阶段划分,对运动表现进行定量评估。结果表明,在将未标记段的初始帧与标记段结束帧进行比较后,拼接后位置计算出的速度具有更好的连续性。通过对乒乓球发球和接球的判断,得到了轻旋、上旋和下旋三种发球方式下的典型发球和回球轨迹,以及常见发球和回球失误的轨迹。比较结果证明,轻旋和下旋的发球准确性优于上旋,较低的发球速度具有更高的准确性。实验结果表明,该系统计算出的三名参与者的水平分布在回球质量和动作规范方面与实际情况一致,证明所提出的KBCS和算法在小样本中是有用的,从而进一步提高了体育训练中运动员姿态恢复的准确性。