Sports Teaching and Research Department, Heilongjiang University, Harbin 150040, Heilongjiang, China.
Comput Intell Neurosci. 2022 Apr 11;2022:5299497. doi: 10.1155/2022/5299497. eCollection 2022.
Racket sports such as tennis are amongst the most popular recreational sports activities. Optimizing tennis teaching methods and improving teaching modes can effectively improve the teaching quality of tennis. In this study, a video and image action recognition system based on image processing techniques and Internet of things is developed to overcome the shortcomings of the traditional tennis teaching methods. To validate its performance, the students of tennis courses are divided into experimental group and control group, respectively. The control group is taught by using the traditional tennis teaching method whereas the experimental group is taught by using the IoT video and image recognition teaching system. Three factors of students including service throwing height, arm elbow angle, and knee bending angles of both groups are measured and compared with those of world elite tennis players. The results show that the students' serving abilities in the experimental group are significantly improved using the video and image recognition system based on IoT, and they are better than those of the students in the control group. The proposed video and image processing technique can be applied in students' physical education and can be employed to provide the basis for the innovation of tennis teaching strategies in physical education.
球拍类运动，如网球，是最受欢迎的休闲运动之一。优化网球教学方法和改进教学模式，可以有效地提高网球的教学质量。在这项研究中，开发了一种基于图像处理技术和物联网的视频和图像动作识别系统，以克服传统网球教学方法的缺点。为了验证其性能，将网球课程的学生分为实验组和对照组，对照组采用传统的网球教学方法，实验组采用物联网视频和图像识别教学系统进行教学。分别测量和比较两组学生的服务投掷高度、手臂肘角和膝盖弯曲角度，并与世界优秀网球运动员进行比较。结果表明，实验组学生在使用基于物联网的视频和图像识别系统后，发球能力显著提高，优于对照组学生。所提出的视频图像处理技术可以应用于学生的体育教育，并为体育教育中网球教学策略的创新提供依据。
