Journal of Applied Science and Engineering

Published by Tamkang University Press

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Developing an Intelligent System for Real-Time Sports Performance Feedback Using AI and Cloud Technologies

 2026

 Volume 34

Chenxi Qiao1 and Qiang Zhang2

1College of Physical Education, Henan Institute of Economics and Trade, Zhengzhou 450046, Henan, China

2Department of Criminal Science and Technology, Henan Police College, Zhengzhou 450046, Henan, Chin

Received: April 07, 2026
Accepted: May 25, 2026
Publication Date: August 12, 2026

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Bidirectional Long Short-Term Memory 

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Sports performance analysis is important for improving athlete training and decision-making, but many existing systems lack real-time feedback and efficient handling of complex movements. This study proposes an AI and cloud-based framework for real-time tennis action recognition using labeled images of forehand, backhand, serve, and volley. Preprocessing techniques such as resizing, normalization, and augmentation improve data quality. EfficientNet extracts spatial features, while pose estimation captures movement patterns. These features are fused and processed using a BiLSTM model for accurate classification. Results are stored in the cloud for real-time access and scalability. The model achieves high performance with 0.975 precision, 0.981 recall, 0.978 F1-score, and 0.992 accuracy, outperforming existing methods.

Keywords: Sports Action Recognition, BiLSTM, EfficientNet, Pose Estimation, Cloud Computing

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