Arntz, A., Weber, F., Handgraaf, M., Lällä, K., Korniloff, K., Murtonen, K.-P., Chichaeva, J., Kidritsch, A., Heller, M., & Sakellari, E. (2023). Technologies in Home-Based Digital Rehabilitation: Scoping Review. JMIR rehabilitation and assistive technologies, 10(1), e43615.
Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., & Baskurt, A. (2010). Action classification in soccer videos with long short-term memory recurrent neural networks. International Conference on Artificial Neural Networks.
Baclig, M. M., Ergezinger, N., Mei, Q., Gül, M., Adeeb, S., & Westover, L. (2020). A deep learning and computer vision based multi-player tracker for squash. Applied Sciences, 10(24), 8793.
Badiola-Bengoa, A., & Mendez-Zorrilla, A. (2021). A systematic review of the application of camera-based human pose estimation in the field of sport and physical exercise. Sensors, 21(18), 5996.

Baranyi, G., Dos Santos Melício, B. C., Gaál, Z., Hajder, L., Simonyi, A., Sindely, D., Skaf, J., Dušek, O., Nekvinda, T., & Lőrincz, A. (2022). AI Technologies for Machine Supervision and Help in a Rehabilitation Scenario. Multimodal Technologies and Interaction, 6(7), 48. https://www.mdpi.com/2414-4088/6/7/48.

Barbon Junior, S., Pinto, A., Barroso, J. V., Caetano, F. G., Moura, F. A., Cunha, S. A., & Torres, R. d. S. (2022). Sport action mining: Dribbling recognition in soccer. Multimedia Tools and Applications, 81(3), 4341-4364.

Battelini, S. (2020). Computer vision for detecting and tracking players in basketball videos Politecnico di Torino].

Baysal, S., & Duygulu, P. (2015). Sentioscope: a soccer player tracking system using model field particles. IEEE Transactions on Circuits and Systems for Video Technology, 26(7), 1350-1362.

Beng, T. H., San, N. S. K., Meng, T. S., Peng, T. W., & Komar, T. J. (2023). The Use of Human Pose Estimation to Enhance Teaching & Learning in Physical Education. Proceedings of the 8th ACPES (ASEAN Council of Physical Education and Sport) International Conference, ACPES 2022, October 28th–30th, 2022, Medan, North Sumatera, Indonesia.

Chen, Y., Yang, L., & Chang, M. (2023). ShootPro: An Interactive and Immersive Basketball Shooting Practice Assistance System using Artificial Intelligence and Computer Vision. CS & IT Conference Proceedings.

Crewther, B. T., Kilduff, L. P., Cook, C. J., Middleton, M. K., Bunce, P. J., & Yang, G.-Z. (2011). The acute potentiating effects of back squats on athlete performance. The Journal of Strength & Conditioning Research, 25(12), 3319-3325.

Difini, G. M., Martins, M. G., & Barbosa, J. L. V. (2021). Human pose estimation for training assistance: a systematic literature review. Proceedings of the Brazilian Symposium on Multimedia and the Web.

Dill, S., Rohr, M., Güney, G., Hoog Antink, C., Rösch, A., De Witte, L., & Schwartz, E. (2023). Accuracy Evaluation of 3D Pose Estimation with MediaPipe Pose for Physical Exercises. Current Directions in Biomedical Engineering.

Diop, C.-A., Pelloux, B., Yu, X., Yi, W.-J., & Saniie, J. (2022). Soccer Player Recognition using Artificial Intelligence and Computer Vision. 2022 IEEE International Conference on Electro Information Technology (eIT).

Foysal, M. F. A., Islam, M. S., Karim, A., & Neehal, N. (2019). Shot-Net: A convolutional neural network for classifying different cricket shots. Recent Trends in Image Processing and Pattern Recognition: Second International Conference, RTIP2R 2018, Solapur, India, December 21–22, 2018, Revised Selected Papers, Part I 2.

Fu, X., Zhang, K., Wang, C., & Fan, C. (2020). Multiple player tracking in basketball court videos. Journal of Real-Time Image Processing, 17, 1811-1828.

Gajbhiye, R., Jarag, S., Gaikwad, P., & Koparde, S. (2022). AI Human Pose Estimation: Yoga Pose Detection and Correction. International Journal of Innovative Science and Research Technology.

Ganesh, Y., Sri Teja, A., Munnangi, S. K., & Rama Murthy, G. (2019). A novel framework for fine grained action recognition in soccer. Advances in Computational Intelligence: 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Gran Canaria, Spain, June 12-14, 2019, Proceedings, Part II 15.

Garg, S., Saxena, A., & Gupta, R. (2022). Yoga pose classification: a CNN and MediaPipe inspired deep learning approach for real-world application. Journal of Ambient Intelligence and Humanized Computing, 1-12.

Giles, B., Kovalchik, S., & Reid, M. (2020). A machine learning approach for automatic detection and classification of changes of direction from player tracking data in professional tennis. Journal of sports sciences, 38(1), 106-113.

Hande, T., Kakirwar, B., Bharadwaja, A., Kshirsagar, P., Gupta, A., & Vijayakumar, P. (2023). Correction and Estimation of Workout Postures with Pose Estimation using AI. 2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE).

Hassaballah, M., & Awad, A. I. (2020). Deep learning in computer vision: principles and applications. CRC Press.

Hayashi, M., Yamamoto, T., Aoki, Y., Ohshima, K., & Tanabiki, M. (2013). Head and upper body pose estimation in team sport videos. 2013 2nd IAPR Asian Conference on Pattern Recognition.

Janardhana, D., Shashwatha, P., Manasa, V., Kavya, H., & Pruthvi, R. (2023). Yoga Pose Estimation using Artificial Intelligence. 2023 International Conference on Data Science and Network Security (ICDSNS).

Janbi, N. F., & Almuaythir, N. (2023). BowlingDL: A Deep Learning-Based Bowling Players Pose Estimation and Classification. 2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC).

Kamble, P. R., Keskar, A. G., & Bhurchandi, K. M. (2019). A deep learning ball tracking system in soccer videos. Opto-Electronics Review, 27(1), 58-69.

Kercher, V. M. M., Kercher, K., Levy, P., Bennion, T., Alexander, C., Amaral, P. C., Batrakoulis, A., Chávez, L. F. J. G., Cortés-Almanzar, P., & Haro, J. L. (2023). 2023 Fitness Trends from around the Globe. ACSM's Health & Fitness Journal, 27(1), 19-30.

Khan, A., Nicholson, J., & Plötz, T. (2017). Activity recognition for quality assessment of batting shots in cricket using a hierarchical representation. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(3), 1-31.

Kien, N. P., Long, N. V., Ngoc, D. T. A., Phuong, D. T. M., Hanh, N. H., Trang, N. M., Hien, P. T., Binh, D. T., Cuong, N. M., & Vu, T. A. (2023). Evaluation System for Straight Punch Training: A Preliminary Study. The International Conference on Intelligent Systems & Networks.

Kim, J.-W., Choi, J.-Y., Ha, E.-J., & Choi, J.-H. (2023). Human Pose Estimation Using MediaPipe Pose and Optimization Method Based on a Humanoid Model. Applied Sciences, 13(4), 2700. https://www.mdpi.com/2076-3417/13/4/2700.

Kim, Y., Jung, C., & Kim, C. (2020). Motion Recognition of Assistant Referees in Soccer Games via Selective Color Contrast Revelation. EasyChair Preprint(2604).

Kishore, D. M., Bindu, S., & Manjunath, N. K. (2022). Estimation of yoga postures using machine learning techniques. International Journal of Yoga, 15(2), 137.

Koo, T. K., & Li, M. Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of chiropractic medicine, 15(2), 155-163.

Kulkarni, K. M., & Shenoy, S. (2021). Table tennis stroke recognition using two-dimensional human pose estimation. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

Latreche, A., Kelaiaia, R., Chemori, A., & Kerboua, A. (2023). Reliability and validity analysis of MediaPipe-based measurement system for some human rehabilitation motions. Measurement, 214, 112826.

Liu, L. (2021). Objects detection toward complicated high remote basketball sports by leveraging deep CNN architecture. Future Generation Computer Systems, 119, 31-36.

Liu, W., Yan, C. C., Liu, J., & Ma, H. (2017). Deep learning based basketball video analysis for intelligent arena application. Multimedia Tools and Applications, 76, 24983-25001.

Llanos, M. J., Obrero, J. R., Alvarez, L. M., Yang, C.-H., & Aliac, C. J. (2022). Computer-assisted Table Tennis Posture Analysis using Machine Learning. 2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET).

Lopes, M., Melo, A. S., Cunha, B., & Sousa, A. S. (2023). Smartphone-Based Video Analysis for Guiding Shoulder Therapeutic Exercises: Concurrent Validity for Movement Quality Control. Applied Sciences, 13(22), 12282.

Luo, X., Berendsen, B., Tan, R. T., & Veltkamp, R. C. (2010). Human pose estimation for multiple persons based on volume reconstruction. 2010 20th international conference on pattern recognition.

Ma, J., Ma, L., Ruan, W., Chen, H., & Feng, J. (2022). A Wushu Posture Recognition System Based on MediaPipe. 2022 2nd International Conference on Information Technology and Contemporary Sports (TCS).

Ma, K. (2021). A real time artificial intelligent system for tennis swing classification. 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI).

Mili, R., Das, N. R., Tandon, A., Mokhtar, S., Mukherjee, I., & Paul, G. (2022). Pose Recognition in Cricket using Keypoints. 2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON).

Naik, B. T., & Hashmi, M. F. (2023). YOLOv3-SORT: detection and tracking player/ball in soccer sport. Journal of Electronic Imaging, 32(1), 011003-011003.

Nelikanti, A., Venkata Rami Reddy, G., & Karuna, G. (2021). An optimization based deep lstm predictive analysis for decision making in cricket. Innovative Data Communication Technologies and Application: Proceedings of ICIDCA 2020.

Ning, B., & Na, L. (2021). Deep Spatial/temporal-level feature engineering for Tennis-based action recognition. Future Generation Computer Systems, 125, 188-193.

Owens, N., Harris, C., & Stennett, C. (2003). Hawk-eye tennis system. 2003 international conference on visual information engineering VIE 2003.

Pardos, A., Tziomaka, M., Menychtas, A., & Maglogiannis, I. (2022). Automated Posture Analysis for the Assessment of Sports Exercises. Proceedings of the 12th Hellenic Conference on Artificial Intelligence.

Rahman, R., Rahman, M. A., Islam, M. S., & Hasan, M. (2021). DeepGrip: Cricket bowling delivery detection with superior CNN architectures. 2021 6th international conference on inventive computation technologies (ICICT).

Ratner, B. (2009). The correlation coefficient: Its values range between+ 1/− 1, or do they? Journal of targeting, measurement and analysis for marketing, 17(2), 139-142.

Rezaei, A., & Wu, L. C. (2022). Automated soccer head impact exposure tracking using video and deep learning. Scientific reports, 12(1), 9282.

Sen, A., Deb, K., Dhar, P. K., & Koshiba, T. (2021). Cricshotclassify: an approach to classifying batting shots from cricket videos using a convolutional neural network and gated recurrent unit. Sensors, 21(8), 2846.

Siddiqui, H. U. R., Younas, F., Rustam, F., Flores, E. S., Ballester, J. B., Diez, I. d. l. T., Dudley, S., & Ashraf, I. (2023). Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning. Sensors, 23(15), 6839. https://www.mdpi.com/1424-8220/23/15/6839 .

Sport Administration, M. o. E. (2022). Sports Admnistration Newsletter #121 December 2022
Srinivasan, H. K., Mathunny, J. J., Devaraj, A., & Karthik, V. (2023). Validation of an Automated Step Length Measurement Method in Sprinting Athletes Using Computer Vision and Pose Estimation. 2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI).

Supanich, W., Kulkarineetham, S., Sukphokha, P., & Wisarnsart, P. (2023). Machine Learning-Based Exercise Posture Recognition System Using MediaPipe Pose Estimation Framework. 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS).

Tang, J. (2021). An action recognition method for volleyball players using deep learning. Scientific Programming, 2021, 1-9.

Thompson, W. R. (2023). Worldwide Survey of Fitness Trends for 2023. ACSM's Health & Fitness Journal, 27(1), 9-18. https://doi.org/10.1249/fit.0000000000000834.

Thulasya Naik, B., Farukh Hashmi, M., & Dhanraj Bokde, N. (2022). A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions. arXiv e-prints, arXiv: 2203.02281.

Tian, C., De Silva, V., Caine, M., & Swanson, S. (2019). Use of machine learning to automate the identification of basketball strategies using whole team player tracking data. Applied Sciences, 10(1), 24.

Vinyes Mora, S., & Knottenbelt, W. J. (2017). Deep learning for domain-specific action recognition in tennis. Proceedings of the IEEE conference on computer vision and pattern recognition workshops.

Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018). Deep learning for computer vision: A brief review. Computational intelligence and neuroscience, 2018.

Yichen, W., & Yamashita, H. (2021). Lineup optimization model of basketball players based on the prediction of recursive neural networks. International Journal of Economics and Management Engineering, 15(3), 287-293.

Yoon, Y., Hwang, H., Choi, Y., Joo, M., Oh, H., Park, I., Lee, K.-H., & Hwang, J.-H. (2019). Analyzing basketball movements and pass relationships using realtime object tracking techniques based on deep learning. IEEE Access, 7, 56564-56576.

Zhou, X., Xie, L., Huang, Q., Cox, S. J., & Zhang, Y. (2014). Tennis ball tracking using a two-layered data association approach. IEEE Transactions on Multimedia, 17(2), 145-156.

Zou, C., Harvard, A., Qian, J., & Fox, B. I. (2023). A systematic review of digital health technologies for the care of older adults during COVID-19 pandemic. DIGITAL HEALTH, 9, 20552076231191050. https://doi.org/10.1177/20552076231191050