Body posture recognition of aerobics based on improved OpenPose lightweight estimation algorithm

As aerobics becomes an increasingly popular form of exercise, the demand for precise movement standards has grown. Traditional human pose recognition models no longer meet the practical requirements of aerobics scenarios. To address this, the study is based on open pose estimation technology, optimized with attention mechanisms and graph neural networks, and proposes a hybrid human pose recognition model. Performance validation and ablation experiments show that the model have an accuracy of 95% and a loss value as low as 0.0089. The highest score for human key points is 7.5, with angle error and position error reduced to 4.9% and 5.4%, respectively, outperforming the base algorithm. This highlights the success of the proposed optimization and enhancement techniques. In practical application comparison experiments, the recognition model achieves a running time of 8 ms when recognizing 150 images, significantly outperforming the comparison models. In multi-person recognition experiments, the proposed model reaches an accuracy of 93%. Additionally, the model shows superior performance in visualizing human pose recognition in practical scenarios. These results indicate that the model has high recognition accuracy and robustness, and can adapt to various real-world applications, meeting the high demands for human pose recognition in aerobics.