RETRACTED ARTICLE: A video images-aware knowledge extraction method for intelligent healthcare management of basketball players

Xiaojun Liang; Xiaojun Liang

doi:10.3934/mbe.2023088

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 2: 1919-1937. doi: 10.3934/mbe.2023088

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Research article

RETRACTED ARTICLE: A video images-aware knowledge extraction method for intelligent healthcare management of basketball players

Xiaojun Liang ^,

College of Humanities, Zhaoqing Medical College, Zhaoqing 526020, Guangdong, China

Retraction published on 18 July 2024, see MBE 2024, 21(7), 6658.

Academic Editor: Man-Fai Leung

Received: 20 September 2022 Revised: 11 October 2022 Accepted: 17 October 2022 Published: 09 November 2022

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.
- smart health management,
- feature extraction,
- fuzzy clustering,
- adaptive median filter
Citation: Xiaojun Liang. RETRACTED ARTICLE: A video images-aware knowledge extraction method for intelligent healthcare management of basketball players[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 1919-1937. doi: 10.3934/mbe.2023088

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Abstract

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.

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