With the rapid development of the information society, human body gesture recognition has become an important technology for human-computer interaction. This paper combines Kinect’s human bone monitoring technology with auxiliary gymnastics training. The gymnastics and dance training can correct students’ wrong movements in time through feedback and improve the training efficiency, so as to meet the needs of nature and harmony of human-machine interaction. In this paper, based on the wireless network Kinect, the human body posture recognition method and tracking technology are studied, and the joint point angle representation method based on the fixed axis is proposed, and the posture recognition method based on the joint point angle is improved, which can accurately recognize the human body posture. Aiming at the situation that the human joint points are occluded, the human joint point repair algorithm is improved. The algorithm is based on the proportion of human bone nodes and the characteristics of human motion, and based on geometric principles, it repairs the occluded points. The feasibility of the original joint point data, angle feature, and distance feature in expressing human behavior is analyzed through experiments, a standard gymnastics movement database is established, and new gymnastics movements can be entered at any time. A gymnastics auxiliary training system is designed, which can analyze and evaluate the exercises of the trainer from the joint point coordinates and the angle formed by the joints and provide the trainer with intuitive error correction prompts. The human body posture recognition method studied in this paper can accurately give the difference between the trainer’s movement and the standard movement, and the trainer can adjust the movement posture according to the prompts, improve the level of gymnastics, and achieve the purpose of auxiliary training. Experiments show that the algorithm model has an accuracy rate of 95.7% for human body posture recognition, and it plays a huge role in line dance aerobics and gymnastics training.

中文翻译：

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基于无线网络Kinect的人体姿势识别方法在排舞健美操训练中的作用

随着信息社会的飞速发展，人体手势识别已成为人机交互的一项重要技术。本文将Kinect的人体骨骼监测技术与辅助体操训练相结合。体操和舞蹈训练可以通过反馈及时纠正学生的错误动作，提高训练效率，满足人机交互自然和谐的需要。本文基于无线网络Kinect，研究人体姿态识别方法和跟踪技术，提出了基于固定轴的关节点角度表示方法，基于关节点角度的姿态识别方法为改进，可以准确识别人体姿势。针对人体关节点被遮挡的情况，对人体关节点修复算法进行了改进。该算法根据人体骨骼节点的比例和人体运动的特点，根据几何原理，对遮挡点进行修复。通过实验分析了原始关节点数据、角度特征、距离特征在表达人体行为方面的可行性，建立了标准的体操动作数据库，可以随时输入新的体操动作。设计了一种体操辅助训练系统，可以从关节点坐标和关节形成的角度对训练者的练习进行分析和评价，为训练者提供直观的纠错提示。本文研究的人体姿势识别方法可以准确给出训练者动作与标准动作的差异，训练者可以根据提示调整动作姿势，提高体操水平，达到辅助训练的目的. 实验表明，该算法模型对人体姿势识别的准确率达到95.7%，在排舞健美操和体操训练中发挥了巨大的作用。