Research on Training Model of Volleyball Based on Flexible Strain Sensing Network for Training

This paper provides an in-depth investigation and analysis of volleyball training patterns using a sensing network composed of flexible strain sensors. This paper models the motion of the hand and leg joints based on an improved human posture estimation technique. The pattern recognition of human motion used the fusion of acceleration sensor and spiral meter data, the output of human motion information from the spiral meter, combined with GA-BP neural network algorithm to repair and fuse the output information, effectively improving the accuracy of posture angle measurement. Based on wireless positioning technology, an improved RFID positioning algorithm combining the LANDMARC algorithm and weighted center-of-mass algorithm in wireless sensor network is proposed, and the positioning accuracy reaches 89.2%; and the error feedback mechanism is introduced to improve the improved algorithm, and the positioning accuracy is improved again by 3.2%. Finally, the two-way dynamic time regularization algorithm is used to align the input action with the standard action, and the action pose evaluation index is compared and analyzed for the action sequence after alignment to obtain the final action quality comparison analysis results. In this paper, the proposed method is applied in practical training, which can effectively locate the stance of athletes and evaluate their movement quality without disturbing them and can provide valuable analysis information to support coaches’ guidance.