In recent years, with the development of deep learning and attention mechanism, more research has been carried out to realize semantic image segmentation based on deep learning integrated attention mechanisms. However, the current semantic segmentation methods have low segmentation accuracy, high computation cost, and serious loss of detailed information. In this paper, a lightweight designed attention gate model was introduced to reduce the computation cost. And because it can suppress irrelevant regions in the input image, while highlighting the salient features of specific tasks, the combination of the two weighting factors input features ( 𝑥 𝑙 ) and gating signal (g) in this structure can improve segmentation accuracy and reduce loss of detail. Therefore, this study used the weighted attention U-Net network to perform semantic segmentation on the GID dataset and finally evaluated it on the four indicators of Precision, Recall, F1-Sorce, and mIoU. This result shows that different weight values have a more significant impact on the experimental results.

In order to solve the problem of low efficiency and accuracy of injury image recognition for sports athletes in high-intensity injury treatment, this paper proposes an injury recognition mode based on the deep neural network. In this paper, the image of sports injury is converted to gray level, and the contour of the injury part in the image is extracted according to the combination of adaptive thresholding and mathematical morphology. In this model, the seed points are selected, the active contour is used to approximate the initial contour, and the curve fitting method is used to fit the obtained discrete points to obtain the final damaged contour. The digital matrix is constructed by using the extracted number of pixels at the damaged position and relevant information. The images are arranged into feature vectors with a length of 64 according to the mode of column concatenation. The overall mean vector of the image is calculated. The calculation results, training samples, and image samples to be recognized are substituted into the Euclidean distance to obtain the preliminary recognition results of the damaged position of the image of sports injury. Then, the image segmentation is realized by clustering. The clustering segmentation results are used to color describe the pixel categories of the original image, calculate the relative damage proportion area in the sports injury image, and identify the damage parts of the high-intensity sports injury image. The experimental results show that the recognition rate of the neural network is 80%-100%, and the recognition time of this method is 0-0.6/s. The above method can improve the accuracy of the recognition of the damaged part of the sports injury image and shorten the recognition time and has certain feasibility in determining the sports injury part.

ObjectiveThis study explores the relationship between self-efficacy, sports participation, and health promotion behavior for middle-aged and elderly people. Therefore, it provides a theoretical reference for improving the quality of life for middle-aged and elderly adults and promoting a healthy lifestyle for the elderly.MethodsA total of 591 (men: 36.2%; women: 63.8%; age: above 50 years) middle-aged and elderly adults from five cities of Henan Province were selected as the research objects by convenient sampling. The self-efficacy, sports participation, and health promotion behavior scales were used for the questionnaire survey. Amos24.0 was used to test the structural equation model, intermediary function test, and bootstrap analysis. Results: The self-efficacy of middle-aged and elderly people positively impacted health promotion behavior. The path coefficient was 0.439. Sports participation played a partial intermediary role between self-efficacy and health promotion behavior (χ2/df = 1.785, root mean square error of approximation = 0.036, root mean square residual = 0.021, goodness-of-fit index = 0.967, comparative fit index = 0.976, Tucker–Lewis Index = 0.971) The proportion of intermediary effect was 26.34% (0.100, 0.225).Conclusion(1) Self-efficacy can significantly and positively affect health promotion behavior for middle-aged and elderly people; (2) sports participation plays a partial intermediary role between self-efficacy and health promotion behavior. From this point of view, we can enhance the self-efficacy of middle-aged and elderly people and improve their healthy life behavior by advancing sports participation. Thus, it provides theoretical support and practical guidance for promoting national health.

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