At present, the industry research of volleyball technology is relatively in-depth, and the analysis of the muscle strength characteristics and coordination of the jumping ball is less, which is not conducive to the control of technical movements. This study used a wireless portable surface EMG tester (16 lines) to analyze the EMG of the main muscle groups in athletes’ volleyball and conducted a video synchronization test method to find the position of the human body. Therefore, a background-based frame difference method is proposed to detect the position and obtain the precise position of the human body. Experiments show that the background-based three-frame difference method effectively eliminates the “hole” effect of the original three-frame difference method and provides an accurate and complete framework for identifying the human body. Adjust the recognition frame according to the proportion of the human body in the image, and use the predefined parameters of the severe frame to perform forward/volleyball background segmentation. The novelty of this document lies in the completion of the complete human body placement of the above three tasks, precapture/background segmentation, and an improved human body position estimation algorithm to extract the human body pose from the video. First, locate the human body in each frame of the video, and then, perform the process of estimating the position of the graphic model based on the color and texture of the unit. After recognizing the gesture of each image in the video, the recognition result will be displayed. Experiments show that after detecting the position of the human body, the predefined frame setting process of the tomb is carried out in two steps, which improves the automation of the human body image detection algorithm, effectively extracts the human motion video, and increases the motion capture rate by more than 30%, to provide a useful reference for the improvement of college volleyball players’ movement skills and training competitions.

中文翻译：

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基于步态触觉传感器的排球动作提取与动态识别

目前排球技术的行业研究比较深入，对跳球的肌力特点和协调性分析较少，不利于技术动作的控制。本研究使用无线便携式表面肌电图测试仪（16线）分析运动员排球主要肌肉群的肌电图，并进行视频同步测试法寻找人体位置。因此，提出了一种基于背景的帧差分方法来检测位置并获得人体的精确位置。实验表明，基于背景的三帧差分法有效地消除了原有三帧差分法的“洞”效应，为人体识别提供了准确完整的框架。根据图像中人体的比例调整识别框，并使用严重框的预定义参数进行前/排球背景分割。该文档的新颖之处在于完成了上述三个任务的完整人体放置，预捕获/背景分割，以及一种改进的人体位置估计算法从视频中提取人体姿态。首先，在视频的每一帧中定位人体，然后根据单元的颜色和纹理进行估计图形模型位置的过程。识别出视频中每张图片的手势后，会显示识别结果。实验表明，检测人体位置后，