College of Humanities, Zhaoqing Medical College, Zhaoqing 526020, Guangdong, China.
Math Biosci Eng. 2023 Jan;20(2):1919-1937. doi: 10.3934/mbe.2023088. Epub 2022 Nov 9.
Currently, the health management for athletes has been a significant research issue in academia. Some data-driven methods have emerged in recent years for this purpose. However, numerical data cannot reflect comprehensive process status in many scenes, especially in some highly dynamic sports like basketball. To deal with such a challenge, this paper proposes a video images-aware knowledge extraction model for intelligent healthcare management of basketball players. Raw video image samples from basketball videos are first acquired for this study. They are processed using adaptive median filter to reduce noise and discrete wavelet transform to boost contrast. The preprocessed video images are separated into multiple subgroups by using a U-Net-based convolutional neural network, and basketball players' motion trajectories may be derived from segmented images. On this basis, the fuzzy KC-means clustering technique is adopted to cluster all segmented action images into several different classes, in which images inside a classes are similar and images belonging to different classes are different. The simulation results show that shooting routes of basketball players can be properly captured and characterized close to 100% accuracy using the proposed method.
目前，运动员的健康管理已成为学术界的一个重要研究课题。近年来，为此出现了一些数据驱动的方法。然而，在许多场景中，数值数据无法反映全面的过程状态，特别是在篮球等一些高度动态的运动中。针对这一挑战，本文提出了一种基于视频图像的知识提取模型，用于篮球运动员的智能医疗保健管理。本研究首先获取来自篮球视频的原始视频图像样本。它们经过自适应中值滤波处理以减少噪声，经过离散小波变换以提高对比度。使用基于 U-Net 的卷积神经网络将预处理后的视频图像分成多个子组，并可以从分割后的图像中得出篮球运动员的运动轨迹。在此基础上，采用模糊 KC-均值聚类技术将所有分割的动作图像聚类成几个不同的类别，其中同一类别的图像相似，而属于不同类别的图像不同。模拟结果表明，使用所提出的方法可以接近 100%的准确率正确捕获和描述篮球运动员的投篮路线。
