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

9卷第6期

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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
2025年9卷第6期页码：150-153
纸质出版日期：2025-06-24
DOI：10.47297/wspiedWSP2516-250032.20250906
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(2025). 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. 国际教育与发展（英文版）, 9(6), 150-153.

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.
(2025). 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. 国际教育与发展（英文版）, 9(6), 150-153. DOI： 10.47297/wspiedWSP2516-250032.20250906.

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

A Oudah , R Abbood , S Shabib , et al . ( 2024 ) Developing Physical Education Curricula Within the Framework of Digital Transformation to Achieve Sustainable Development . Teacher Education Quarterly .

L Li , Y Fan , M Tse & KY Lin ( 2020 ) A review of applications in federated learning . Computers & Industrial Engineering .

T Sun , D Li & B Wang ( 2022 ) Decentralized federated averaging . IEEE Transactions on Pattern Analysis and Machine Intelligence .

K Hsieh , A Phanishayee , O Mutlu , et al . ( 2020 ) The non-iid data quagmire of decentralized machine learning . In International Conference on Machine Learning .

TB Moeslund , A Hilton & V Krüger ( 2006 ) A survey of advances in vision-based human motion capture and analysis . Computer Vision and Image Understanding .

Y Kong & Y Fu ( 2022 ) Human action recognition and prediction: A survey . International Journal of Computer Vision .

C Dwork ( 2008 ) Differential privacy: A survey of results . In International Conference on Theory and Applications of Models of Computation .

J Gou , B Yu , SJ Maybank & D Tao ( 2021 ) Knowledge distillation: A survey . International Journal of Computer Vision .

J Wu ( 2017 ) Introduction to convolutional neural networks . National Key Lab for Novel Software Technology, Nanjing University .

A Graves & A Graves ( 2012 ) Long short-term memory . Supervised Sequence Labelling with Recurrent Neural Networks .

Y Jiang , S Wang , V Valls , BJ Ko , et al . ( 2022 ) Model pruning enables efficient federated learning on edge devices . IEEE Transactions on Neural Networks and Learning Systems .

X Zhu , J Wang , K Sato & Z Zheng ( 2025 ) Adaptive Model Compression for Efficient Federated Learning in IoT Systems . IEEE Internet of Things Journal .

C Xu , Y Qu , Y Xiang & L Gao ( 2023 ) Asynchronous federated learning on heterogeneous devices: A survey . Computer Science Review .

Sietsma & Dow ( 1988 ) Neural net pruning-why and how . IEEE .

V Mothukuri , RM Parizi , S Pouriyeh , Y Huang , et al . ( 2021 ) A survey on security and privacy of federated learning . Future Generation Computer Systems .

T Ha , TK Dang , TT Dang , TA Truong , et al . ( 2019 ) Differential privacy in deep learning: an overview . In 2019 International Conference on Machine Learning and Cybernetics .

K Sandamal , S Arachchi , VO Erkudov , et al . ( 2024 ) Explainable artificial intelligence for fitness prediction of young athletes living in unfavorable environmental conditions . Results in Engineering .

J Xu , BS Glicksberg , C Su , P Walker , J Bian , et al . ( 2021 ) Federated learning for healthcare informatics . Journal of Healthcare Informatics Research .

P Soltani & AHP Morice ( 2020 ) Augmented reality tools for sports education and training . Computers & Education .

L Che , J Wang , Y Zhou & F Ma ( 2023 ) Multimodal federated learning: A survey . Sensors .