Research on the construction of basketball training behavior analysis system based on machine learning——A cross-national empirical study on the digital transformation of physical education in colleges and universities

Jiajun Nan

doi:10.47297/wspiedWSP2516-250032.20250906

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Research on the construction of basketball training behavior analysis system based on machine learning——A cross-national empirical study on the digital transformation of physical education in colleges and universities

Volume 9, Issue 6

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Research on the construction of basketball training behavior analysis system based on machine learning——A cross-national empirical study on the digital transformation of physical education in colleges and universities
Research on the construction of basketball training behavior analysis system based on machine learning——A cross-national empirical study on the digital transformation of physical education in colleges and universities
Liaoning University of International Business and Economics, Dalian, Liaoning, 116052
Vol. 9, Issue 6, Pages: 150-153(2025)
Published：24 June 2025
DOI：10.47297/wspiedWSP2516-250032.20250906
Accepted：

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Nan Jiajun.Research on the construction of basketball training behavior analysis system based on machine learning——A cross-national empirical study on the digital transformation of physical education in colleges and universities[J].Journal of International Education and Development,2025,09(06):150-153.
DOI：

Nan Jiajun.Research on the construction of basketball training behavior analysis system based on machine learning——A cross-national empirical study on the digital transformation of physical education in colleges and universities[J].Journal of International Education and Development,2025,09(06):150-153. DOI： 10.47297/wspiedWSP2516-250032.20250906.

摘要

Abstract

We introduce FL-BTBAS

a federated learning framework designed for analyzing basketball training behaviors

tackling non-IID data distribution and privacy concerns in decentralized sports data environments. By combining adaptive dynamic pruning with a multimodal CNN-LSTM framework

it facilitates cross-facility collaborative training without sharing raw data: local clients handle RGB-D and inertial sensor data using layer-wise magnitude pruning

which adjusts sparsity to address data heterogeneity

while the global model integrates pruned structures through weighted averaging and structural alignment to manage variations among clients. Combining pose estimation with biomechanical data at the edge cuts latency by 40% compared to cloud-based approaches. FL-BTBAS

deployed on NVIDIA Jetson AGX Orin

incorporates heterogeneity-aware sparsity management and federated structural alignment to maintain the significance of underrepresented patterns

showcasing scalability

privacy adherence

and precise analytics for multi-institutional digital physical education.

关键词

Keywords

references

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