Basketball Footwork and Application Supported by Deep Learning Unsupervised Transfer Method

Basketball Footwork and Application Supported by Deep Learning Unsupervised Transfer Method

Yu Feng, Hui Sun
DOI: 10.4018/IJITWE.334365
Article PDF Download
Open access articles are freely available for download

Abstract

The combination of traditional basketball footwork mobile teaching and AI will become a hot spot in basketball footwork research. This article used a deep learning (DL) unsupervised transfer method: Convolutional neural networks are used to extract source and target domain samples for transfer learning. Feature extraction is performed on the data, and the impending action of a basketball player is predicted. Meanwhile, the unsupervised human action transfer method is studied to provide new ideas for basketball footwork action series data modeling. Finally, the theoretical framework of DL unsupervised transfer learning is reviewed. Its principle is explored and applied in the teaching of basketball footwork. The results show that convolutional neural networks can predict players' movement trajectories, unsupervised training using network data dramatically increases the variety of actions during training. The classification accuracy of the transfer learning method is high, and it can be used for the different basketball footwork in the corresponding stage of the court.
Article Preview
Top

Literature Review

Zhao and his team studied the routine teaching of footwork among students in the school’s basketball elective course (Zhao et al., 2023). They found that in basketball teaching, whether it was the beginner or the proficiency period, the advance and defense movement exercises should be closely combined so the overall technique could be improved fast (Guo et al., 2023). Liu and Zhao pointed out a method to promote the transfer of skill motivation in their research on applying motor skills transfer theory in basketball teaching (Liu & Zhao, 2023). The teaching schedule should be rationally arranged. Physical fitness should be comprehensively developed. Student’s ability to analyze and generalize needed to be improved (X. Liang, 2023). Teachers should fully use the transfer law between sports and basketball combination techniques. Individual techniques should be scientifically combined, and students could master these movements through repeated practice (Bu, 2023). When Gong and Srivastava studied the footwork training in basketball teaching, it was concluded that the three paramount footwork patterns are the forward, backward, and lateral sliding steps (Gong & Srivastava, 2022). To sum up, the studies surmised there are relevant theoretical foundations for the research on basketball footwork teaching. Scholars have discussed the composition of footwork, practice awareness, conventional teaching of footwork, practice principles, and issues that should be paid attention to in the routine teaching of basketball footwork using the method of comparison, literature, and logical reasoning.

Complete Article List

Search this Journal:
Reset
Volume 19: 1 Issue (2024)
Volume 18: 1 Issue (2023)
Volume 17: 4 Issues (2022): 1 Released, 3 Forthcoming
Volume 16: 4 Issues (2021)
Volume 15: 4 Issues (2020)
Volume 14: 4 Issues (2019)
Volume 13: 4 Issues (2018)
Volume 12: 4 Issues (2017)
Volume 11: 4 Issues (2016)
Volume 10: 4 Issues (2015)
Volume 9: 4 Issues (2014)
Volume 8: 4 Issues (2013)
Volume 7: 4 Issues (2012)
Volume 6: 4 Issues (2011)
Volume 5: 4 Issues (2010)
Volume 4: 4 Issues (2009)
Volume 3: 4 Issues (2008)
Volume 2: 4 Issues (2007)
Volume 1: 4 Issues (2006)
View Complete Journal Contents Listing