AI Predictive Models in Sports Using Biomechanics
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I2P122Keywords:
Sports Biomechanics, Artificial Intelligence, Injury Prediction, Machine Learning, Inertial Measurement Units, Motion Capture, Deep Learning, CNN-LSTM, Performance Optimization, Wearable Sensors, Multimodal FusionAbstract
The convergence of artificial intelligence and sports biomechanics has inaugurated a paradigm shift in athletic science, enabling transition from reactive injury treatment and subjective performance coaching toward proactive, data-driven predictive systems. By integrating multimodal biomechanical data sources — including inertial measurement units (IMUs), surface electromyography (sEMG), force plates, and markerless motion capture — with advanced AI architectures such as CNN-xLSTM hybrids, attention-enhanced BiLSTM networks, and multimodal fusion frameworks, contemporary predictive models can forecast injury risk with up to 95% accuracy, predict ground reaction forces with R²=0.909, and classify athletic movements with 93.1% accuracy. This paper provides a comprehensive examination of AI predictive model architectures in sports biomechanics, presenting a unified methodology and experimental framework, synthesized performance benchmarks across 36 key studies, and strategic recommendations for advancing the field. The study addresses four primary application domains: injury prediction and prevention, performance optimization, rehabilitation guidance, and real-time game analytics. Ethical considerations including data privacy, model interpretability, and demographic bias are critically examined, alongside persistent technical limitations spanning the laboratory-to-field translation gap and data standardization challenges. Future research directions encompassing foundation models for biomechanics, federated learning for privacy-preserving multi-team collaboration, and digital twins for individualized athlete simulation are identified as transformative priorities for the next decade of sports AI development.
References
1. M. Souaifi, A. El Yaagoubi, M. Bouzelmat, and A. Ennasry, "Artificial Intelligence in Sports Biomechanics: A Scoping Review on Wearable Technology, Motion Analysis, and Injury Prevention," PubMed Central (PMC), 2025.
2. A. Farsi, "Artificial Intelligence Approach in Biomechanics of Gait and Sport: A Systematic Literature Review," Journal of Biomedical Physics and Engineering, vol. 13, no. 5, pp. 383–402, 2023.
3. InjuryPrediction.com, "Injury Prediction — AI-Powered Injury Risk Intelligence Platform," injuryprediction.com, April 2026.
4. PhysioQinesis, "AI Coaching: Predictive Analytics for Injury Prevention and Athletic Performance," PhysioQinesis Blog, March 2026.
5. S. Zhao, Y. Liu, and K. Chen, "AI-Driven Medical Image Analysis for Sports Injury Diagnosis and Prevention," Nature Scientific Reports, November 2025.
6. T. Chen, J. Li, and W. Zhang, "Prediction of Vertical Ground Reaction Forces Under Different Running Speeds: Integration of Wearable IMU with CNN-xLSTM Architecture," Sensors, vol. 25, no. 4, Art. no. 1247, 2025.
7. R. Kumar, A. Sharma, and P. Singh, "Multi-Modal Fusion of Medical Imaging and Biomechanical Data Using Attention-Based Swin-UNet and LSTM for Sports Injury Prediction," Frontiers in Physiology, vol. 17, Art. no. 1421893, April 2026.
8. H. Zheng, F. Wang, and Y. Sun, "Real-Time Sports Injury Monitoring System Based on the Deep Learning Algorithm," BMC Medical Imaging, vol. 24, Art. no. 132, May 2024.
9. X. Wang, M. Liu, and J. Chen, "Multimodal Sequence Dynamics and Convergence Optimization in Dual-Stream LSTM Networks for Complex Physiological State Estimation," Frontiers in Neurorobotics, vol. 20, Art. no. 1537292, April 2026.
10. H. Nunome, "Artificial Intelligence in Sports Biomechanics: New Dawn or False Hope?" International Journal of Sports Biomechanics, PubMed Central, 2006.
11. A. Jameel, S. Khan, and B. Malik, "Real-Time Wearable Biomechanics Framework for Sports Injury Prevention and Rehabilitation Optimization," Nature Scientific Reports, vol. 16, Art. no. 2874, January 2026.
12. J. Frontera, C. Mendes, and D. Costa, "Commercial Vision Sensors and AI-Based Pose Estimation Frameworks for Markerless Motion Analysis in Sports and Exercises: A Mini Review," Frontiers in Physiology, vol. 17, Art. no. 1387211, April 2026.
13. P. Mahoney, T. Haber, and J. Heckman, "Differentiable Biomechanics Unlocks Opportunities for Markerless Motion Capture," arXiv preprint arXiv:2404.15710, 2024.
14. Y. Zhang and X. Liu, "Attention-Enhanced Convolutional BiLSTM Model for Predicting Recovery Outcomes in Sports Injuries," Lecture Notes in Computer Science, Springer, August 2025.
15. MedicalXpress News Team, "Generative AI Can Help Athletes Avoid Injuries: BIGE Model for Biomechanically-Informed Exercise Science," MedicalXpress, October 2025.
16. L. Yang, H. Guo, and Z. Chen, "Forensic-Oriented Injury and Abnormality Assessment in Sports Medicine via a Biomechanically-Informed Predictive Optimization Network (BIPON)," Frontiers in Medicine, vol. 13, Art. no. 1531274, March 2026.
17. F. Martins, C. Oliveira, R. Dias, and J. Brito, "Machine Learning-Based Prediction of Muscle Injury Risk in Professional Football: A Four-Year Longitudinal Study," Journal of Clinical Medicine, vol. 14, no. 22, Art. no. 7089, 2025.
18. J. Hughes, G. Saw, P. Blanch, N. Collins, and K. Drew, "Machine Learning Approaches to Injury Risk Prediction in Sport: A Scoping Review with Evidence Synthesis," British Journal of Sports Medicine, vol. 59, no. 7, pp. 451–461, 2024.
19. E. M. Cust, A. F. Sweeting, K. Ball, and S. Robertson, "Explainable Machine Learning Techniques to Predict Muscle Injuries in Professional Soccer Players through Biomechanical Analysis Using SHAP Values," Sensors, vol. 24, no. 1, Art. no. 245, 2024.
20. J. Luan, L. Li, and M. Wang, "CNN and LSTM-Based Multimodal Data Fusion for Performance Optimization in Aerobics Using Wearable Sensors," Informatica, vol. 49, no. 4, November 2025.
21. R. Feng, X. Qin, Y. Zhao, and H. Wu, "Deep Learning with an Attention Mechanism for Continuous Biomechanical Motion Estimation Across Varied Activities," Frontiers in Bioengineering and Biotechnology, vol. 10, Art. no. 894640, 2022.
22. Y. Nakano, S. Fukashiro, and S. Yoshioka, "Evaluation of 3D Markerless Motion Capture Accuracy Using OpenPose with Multiple Video Cameras," ResearchGate (Transactions of the Japanese Society for Artificial Intelligence), 2023.
23. Sigmoidal AI, "Real-Time Human Pose Estimation Using MediaPipe: BlazePose Architecture and Sports Applications," Sigmoidal.ai Technical Blog, April 2026.
24. J. Doherr, K. Patel, and M. Huang, "Multi-Person Physics-Based Pose Estimation for Combat Sports Using Transformer Networks with Epipolar Geometry," arXiv preprint arXiv:2501.09874, 2025.
25. L. Ceyssens, B. Vanrenterghem, K. Robinson, and R. Meeusen, "Multidisciplinary Prediction of Running-Related Injuries Using Machine Learning: Integrating Biomechanics, Training Load, and Physiology," npj Digital Medicine, Nature, vol. 9, Art. no. 34, February 2026.
26. J.-B. Morin, M. Samozino, A. Nagahara, and P.-M. Girard, "Comparison of Visual Trackers for Automated Biomechanical Analysis of Sprint Running Kinematics," arXiv preprint arXiv:2502.08621, February 2025.
27. A. Baca, P. Dabnichki, and M. Lames, "A Narrative Review of Deep Learning Applications in Sports Performance Analysis: Current Practices, Challenges, and Future Directions," BMC Sports Science, Medicine and Rehabilitation, vol. 17, Art. no. 156, August 2025.
28. L. Dobers, J. Vette, and R. Müller, "Extended Application of Inertial Measurement Units in Biomechanics: From Activity Recognition to Force Estimation," Sensors, vol. 23, no. 9, Art. no. 4547, 2023.
29. P. Xu, Y. Zhou, F. Li, and T. Zhang, "Construction and Application of a Model for Predicting Athletes' Injury Risk Based on Machine Learning: A Multi-sport Cohort Study," BMC Medical Informatics and Decision Making, vol. 25, Art. no. 412, December 2025.
30. D. Claudino, T. Capanema, G. Helal, and J. Serrão, "Machine Learning Applications in Sports Injury Prediction: A Narrative Review of Methods, Challenges, and Evidence Quality," Scientific Progress, vol. 108, no. 4, Art. no. SP1247, 2025.
31. S. Falbriard, F. Meyer, A. Mariani, and K. Aminian, "Sensor Data Required for Automatic Recognition of Athletic Tasks Using Deep Neural Networks: A Systematic Sensitivity Analysis," Frontiers in Bioengineering and Biotechnology, vol. 7, Art. no. 306, 2019.
32. Healify AI, "AI Models for Injury Risk Assessment in Fitness and Sport: Prospective Validation Study," Healify AI Platform Technical Report, June 2025.
33. J. DiFiori, A. Gallo, S. Brenner, and H. Brooks, "Diagnostic Applications of Artificial Intelligence in Sports Medicine: A Comprehensive Review of Injury Risk Prediction Methods," Diagnostics, vol. 14, no. 22, Art. no. 2584, 2024.
34. X. Hou, L. Feng, Y. Cao, and Z. Li, "Action Recognition in Rehabilitation: Combining 3D Convolution and LSTM with Spatiotemporal Attention Mechanisms," Frontiers in Physiology, vol. 15, Art. no. 1341206, 2024.
35. T. Gronwald, F. Klein-Soetebier, and R. Jakobsmeyer, "Can Machine Learning Predict Running Kinematics Based on Upper Trunk GPS-IMU Acceleration? A Practical Application for Field-Based Biomechanical Assessment," Applied Sciences, vol. 14, no. 5, Art. no. 2001, 2024.
36. M. Falbriard, F. Atrsaei, F. Paraschiv-Ionescu, and K. Aminian, "Validation and Analysis of Recreational Runners' Kinematics Obtained from a Sacral Inertial Measurement Unit Across Speed and Incline Conditions," Sensors, vol. 25, no. 2, Art. no. 573, 2025.
