{"id":9850,"date":"2026-08-12T14:31:59","date_gmt":"2026-08-12T06:31:59","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=9850"},"modified":"2026-08-17T23:46:48","modified_gmt":"2026-08-17T15:46:48","slug":"jase-202611-34-038","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202611-34-038","title":{"rendered":"Developing an Intelligent System for Real-Time Sports Performance Feedback Using AI and Cloud Technologies"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 34<\/a><\/p>\n<\/div>\n\n\n\n

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Chenxi Qiao1<\/sup> and Qiang Zhang2<\/sup><\/i><\/a><\/p>\n\n\n\n

1<\/sup>College of Physical Education, Henan Institute of Economics and Trade, Zhengzhou 450046, Henan, China<\/p>\n\n\n\n

2<\/sup>Department of Criminal Science and Technology, Henan Police College, Zhengzhou 450046, Henan, Chin<\/p>\n<\/div>\n\n\n\n

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Received: April 07, 2026
Accepted: May 25, 2026
Publication Date: August 12, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Bidirectional Long Short-Term Memory <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202611_34.038<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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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.<\/p>\n\n\n\n

Keywords: Sports Action Recognition, BiLSTM, EfficientNet, Pose Estimation, Cloud Computing<\/em><\/p>\n\n\n\n

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  1. [1] X. Lei and P.-L. P. Rau, (2023) \u201cEmotional Responses to Performance Feedback in an Educational Game During Cooperation and Competition with a Robot: Evidence from fNIRS\u201d Computers in Human Behavior 138: 107496. DOI: 10.1016\/j.chb.2022.107496. URL: https:\/\/doi.org\/10.1016\/j.chb.2022.107496<\/a>.<\/li>\n
  2. [2] A. Alshardan, H. Mahgoub, S. Alahmari, M. Alonazi, R. Marzouk, and A. Mohamed, (2025) \u201cCloud-to-Thing Continuum-Based Sports Monitoring System Using Machine Learning and Deep Learning Model\u201d PeerJ Computer Science 11: e2539. DOI: 10.7717\/peerj-cs.2539. URL: https:\/\/doi.org\/10.7717\/peerj-cs.2539<\/a>.<\/li>\n
  3. [3] L. Zhao, (2023) \u201cA Hybrid Deep Learning-Based Intelligent System for Sports Action Recognition via Visual Knowledge Discovery\u201d IEEE Access 11: 46541\u201346549. DOI: 10.1109\/ACCESS.2023.3275012. URL: https:\/\/doi.org\/10.1109\/ACCESS.2023.3275012<\/a>.<\/li>\n
  4. [4] H. U. R. Siddiqui et al., (2023) \u201cEnhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning\u201d Sensors 23(15): 6839. DOI: 10.3390\/s23156839. URL: https:\/\/doi.org\/10.3390\/s23156839<\/a>.<\/li>\n
  5. [5] K. Wang, L. Wang, and J. Sun, (2025) \u201cThe Data Analysis of Sports Training by ID3 Decision Tree Algorithm and Deep Learning\u201d Scientific Reports 15(1): 15060. DOI: 10.1038\/s41598-025-99996-5. URL: https:\/\/doi.org\/10.1038\/s41598-025-99996-5<\/a>.<\/li>\n
  6. [6] Y. Hu and D. Liu, (2025) \u201cDesign of Sports Action Recognition and Evaluation Based on Improved DTW Algorithm\u201d ICIC International: DOI: 10.24507\/ijicic.21.01.37. URL: https:\/\/doi.org\/10.24507\/ijicic.21.01.37<\/a>.<\/li>\n
  7. [7] C. Mou, (2024) \u201cThe Attention Mechanism Performance Analysis for Football Players Using the Internet of Things and Deep Learning\u201d IEEE Access 12: 4948\u20134957. DOI: 10.1109\/ACCESS.2024.3350036. URL: https:\/\/doi.org\/10.1109\/ACCESS.2024.3350036<\/a>.<\/li>\n
  8. [8] Y. Zheng and L. Cai, (2025) \u201cArtificial Intelligence-Based Automatic Identification and Classification of Diverse Sports Using Advanced Deep Learning Models\u201d International Journal of Information and Communication Technology 26(23): 91\u2013113. DOI: 10.1504\/IJICT.2025.147123. URL: https:\/\/doi.org\/10.1504\/IJICT.2025.147123<\/a>.<\/li>\n
  9. [9] D. S. Jain, (2025) \u201cAI in Sports: Deep Learning Models for Player Performance Analysis and Injury Prediction\u201d International Journal of Research Technology 13(1): 11\u201317. URL: https:\/\/ijrt.org\/j\/article\/view\/130<\/a>.<\/li>\n
  10. [10] H. Jiang, (2024) \u201cAnalysis of Youth Sports Physical Health Data Based on Cloud Computing and Gait Awareness\u201d Journal of Intelligent Systems 33(1): DOI: 10.1515\/jisys-2023-0155. URL: https:\/\/doi.org\/10.1515\/jisys-2023-0155<\/a>.<\/li>\n
  11. [11] Y. B. S. Hussain, V. Gunasekhar, D. Reshma, K. S. Kumar, S. V. Reddy, and K. Bhargavi, (2024) \u201cRevolutionizing Sports Information Systems for Real-Time Analytics for Better Decision-Making\u201d Journal of Computer Education and Sports Health 1(1): 27\u201340. DOI: 10.69996\/jcesh.2024003. URL: https:\/\/doi.org\/10.69996\/jcesh.2024003<\/a>.<\/li>\n
  12. [12] K. Chang, P. Sun, and M. U. Ali, (2024) \u201cRetracted Article: A Cloud-Assisted Smart Monitoring System for Sports Activities Using SVM and CNN\u201d Soft Computing 28(1): 339\u2013362. DOI: 10.1007\/s00500-023-09404-1. URL: https:\/\/doi.org\/10.1007\/s00500-023-09404-1<\/a>.<\/li>\n
  13. [13] C. Yan, (2025) \u201cTinyML-Enhanced Cloud-Edge Collaborative Framework for Real-Time Sport Action Recognition\u201d Internet Technology Letters 8(5): e70100. DOI: 10.1002\/itl2.70100. URL: https:\/\/doi.org\/10.1002\/itl2.70100<\/a>.<\/li>\n
  14. [14] Y. Jia and J. Liang, (2025) \u201cReal-Time Performance Prediction in Sports Using Machine Learning Algorithms\u201d International Journal of Data Science 10(7): 285\u2013297. DOI: 10.1504\/IJDS.2025.151196. URL: https:\/\/doi.org\/10.1504\/IJDS.2025.151196<\/a>.<\/li>\n
  15. [15] S. H. Grandhi, B. R. Gudivaka, R. L. Gudivaka, R. K. Gudivaka, D. K. R. Basani, and M. M. Kamruzzaman, (2025) \u201cDetection and Diagnosis of ECH Signal Wearable System for Sportsperson Using Improved Monkey-Based Search Support Vector Machine\u201d International Journal of High Speed Electronics and Systems: 2540149. DOI: 10.1142\/S0129156425401494. URL: https:\/\/doi.org\/10.1142\/S0129156425401494<\/a>.<\/li>\n
  16. [16] S. Khater, M. Hadhoud, and M. B. Fayek, (2022) \u201cA Novel Human Activity Recognition Architecture: Using Residual Inception ConvLSTM Layer\u201d Journal of Engineering and Applied Science 69(1): 45. DOI: 10.1186\/s44147-022-00098-0. URL: https:\/\/doi.org\/10.1186\/s44147-022-00098-0<\/a>.<\/li>\n
  17. [17] V. Vec, S. Toma\u017ei\u010d, A. Kos, and A. Umek, (2024) \u201cTrends in Real-Time Artificial Intelligence Methods in Sports: A Systematic Review\u201d Journal of Big Data 11(1): 148. DOI: 10.1186\/s40537-024-01026-0. URL: https:\/\/doi.org\/10.1186\/s40537-024-01026-0<\/a>.<\/li>\n
  18. [18] Tennis Player Actions Dataset. Accessed March 11, 2026. 2026. URL: https:\/\/www.kaggle.com\/datasets\/orvile\/tennis-player-actions-dataset<\/a>.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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