Study on badminton movement evaluation method based on human pose estimation

Aiming at the issues of insufficient real-time performance and low accuracy in traditional motion capture analysis methods，human pose estimation technology was applied to badminton sports instruction，the identification and standard evaluation of badminton swing movements were studied．First，an improved human pose estimation model based on OpenPose was proposed．T he VGG19 feature extraction network was replaced with a lightweight MobileNet network，and the 7×7 convolution kernel structure inside the model was restructured into a tandem structure composed of a 1×1 convolution，a 3×3 depthwise separable convolution，and a dilated convolution with a dilation rate of 2，achieving model lightweighting． Then，according to the characteristics of badminton swing actions，a sparse representation model of 14-point human pose was proposed to evaluate the standard degree of swing actions． Experimental results show that the improved OpenPose model increases processing speed by 3 times while simultaneously improving the accuracy of skeletal joint recognition for human arms by 3.57%，and the 14-point human pose sparse representation model achieves a 2.98-fold improvement in response time while maintaining precision． © 2024 Huazhong University of Science and Technology. All rights reserved.