Progress and application of machine learning in sports injury research_Journal of Biomedical Engineering

Authors：

LI Xinke ¹ ,  YANG Chen ¹ , FENG Ru ¹ , RONG Ke ¹ , ZHOU Zhipeng ²

1. Sport and Health Science, Nanjing Sport Insititute, Nanjing 210014, P. R. China;
2. College of Sports and Health, Shandong Sport University, Jinan 250102, P. R. China;

Corresponding?author：

YANG Chen, Email: chen_yang@nsi.edu.cn

Keywords：

Sports injury; Machine learning; Deep learning; Artificial intelligence; Sports and exercise

DOI：

10.7507/1001-5515.202508020

Video：

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Abstract Full text Figures/Tables Video References Cited by

The research on sports injury has long relied on traditional statistical methods, which often have obvious limitations when dealing with high-dimensional, nonlinear and multi-modal data. With the development of artificial intelligence technology, machine learning provides a new way to solve these complex problems. This paper systematically reviews the progress and application of machine learning in sports injury research. Firstly, the development of different machine learning methods in sports injury research was summarized. Secondly, we focus on five core applications of machine learning in the field of sports injury: data feature selection and dimensionality reduction, injury risk prediction, injury symptoms and signs classification, risk monitoring based on wearable devices, and imaging data analysis. Research shows that machine learning models show significant advantages in dealing with complex data patterns, improving prediction accuracy and achieving real-time dynamic intervention. However, research in related fields still faces challenges such as insufficient data quality and quantity, poor model interpretability, and barriers to multidisciplinary cooperation. Through the summary, it can be seen that future research in this field should focus on constructing standardized shared datasets, developing more interpretable models, and strengthening the cooperation and communication between multi-disciplines, and finally promoting the application of machine learning in sports injury prevention, diagnosis and rehabilitation.

Citation： LI Xinke, YANG Chen, FENG Ru, RONG Ke, ZHOU Zhipeng. Progress and application of machine learning in sports injury research. Journal of Biomedical Engineering, 2026, 43(1): 208-215. doi: 10.7507/1001-5515.202508020 Copy

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2.	Zhang D, Khalili A, Asgharian M. Post-model-selection inference in linear regression models: an integrated review. Statistics Surveys, 2022, 16: 86-136.
3.	Beato M, Jaward M H, Nassis G P, et al. An educational review on machine learning: a SWOT analysis for implementing machine learning techniques in football. International Journal of Sports Physiology and Performance, 2024, 20(2): 183-196.
4.	Bernardes R. Machine learning-basic principles. Acta Ophthalmologica, 2024, 102(S279): 26-28.
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8.	Sindayigaya L, Dey A. Machine learning algorithms: a review. International Journal of Science and Research, 2022, 11(8): 1127-1133.
9.	Carey D, Ong K L, Whiteley R, et al. Predictive modelling of training loads and injury in Australian football. International Journal of Computer Science in Sport, 2018, 17(1): 49-66.
10.	Leckey C, van Dyk N, Doherty C, et al. Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis. British Journal of Sports Medicine, 2025, 59(7): 491-500.
11.	Khatun Z, Jónsson H, Tsirilaki M, et al. Beyond pixel: superpixel-based MRI segmentation through traditional machine learning and graph convolutional network. Computer Methods and Programs in Biomedicine, 2024, 256: 108398.
12.	Yin R, Chen H, Wang C, et al. Transformer-based multilabel deep learning model is efficient for detecting ankle lateral and medial ligament injuries on magnetic resonance imaging and improving clinicians’ diagnostic accuracy for rotational chronic ankle instability. Arthroscopy: The Journal of Arthroscopic & Related Surgery, 2025, 41(3): 574-584.
13.	Ding N, Takeda K, Fujii K. Deep reinforcement learning in a racket sport for player evaluation with technical and tactical contexts. IEEE Access, 2022, 10: 54764-54772.
14.	Dingenen B, Staes F, Vanelderen R, et al. Subclassification of recreational runners with a running-related injury based on running kinematics evaluated with marker-based two-dimensional video analysis. Physical Therapy in Sport, 2020, 44: 99-106.
15.	Martin J A, Heiderscheit B C. A hierarchical clustering approach for examining the relationship between pelvis-proximal femur geometry and bone stress injury in runners. Journal of Biomechanics, 2023, 160: 111782.
16.	Shakya A, Pillai G, Chakrabarty S. Reinforcement learning algorithms: a brief survey. Expert Systems with Applications, 2023, 231(30): 120495.
17.	Song S, Kidziński ?, Peng X B, et al. Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation. Journal of Neuroengineering and Rehabilitation, 2021, 18(1): 126.
18.	Schumacher P, Geijtenbeek T, Caggiano V, et al. Emergence of natural and robust bipedal walking by learning from biologically plausible objectives. iScience, 2025, 28(4): 112203.
19.	Kimura R, Sato T, Kasukawa Y, et al. Automatic assist level adjustment function of a gait exercise rehabilitation robot with functional electrical stimulation for spinal cord injury: insights from clinical trials. Biomimetics, 2024, 9(10): 621.
20.	Lu Y, Pareek A, Lavoie-Gagne O Z, et al. Machine learning for predicting lower extremity muscle strain in National Basketball Association athletes. Orthopaedic Journal of Sports Medicine, 2022, 10(7): 23259671221111742.
21.	Moghadam S M, Yeung T, Choisne J. A comparison of machine learning models’ accuracy in predicting lower-limb joints’ kinematics, kinetics, and muscle forces from wearable sensors. Scientific Reports, 2023, 13(1): 5046.
22.	Sheridan D, Jaspers A, Viet Cuong D, et al. Estimating oxygen uptake in simulated team sports using machine learning models and wearable sensor data: a pilot study. PLoS One, 2025, 20(4): e0319760.
23.	Richter C, O’Reilly M, Delahunt E. Machine learning in sports science: challenges and opportunities. Sports Biomechanics, 2024, 23(8): 961-967.
24.	Rahimi Goloujeh M, Allen J L. Motor modules are largely unaffected by pathological walking biomechanics: a simulation study. Journal of Neuroengineering and Rehabilitation, 2025, 22(1): 16.
25.	Trudeau M B, von Tscharner V, Vienneau J, et al. Assessing footwear effects from principal features of plantar loading during running. Medicine and Science in Sports and Exercise, 2015, 47(9): 1988-1996.
26.	魏夢力, 鐘亞平, 桂輝賢, 等. 基于機器學習的運動損傷預警模型. 中國組織工程研究, 2025, 29(2): 409-418.
27.	Richardson E, Trevizani R, Greenbaum J A, et al. The receiver operating characteristic curve accurately assesses imbalanced datasets. Patterns, 2024, 5(6): 100994.
28.	Tsilimigkras T, Kakkos I, Matsopoulos G K, et al. Enhancing sports injury risk assessment in soccer through machine learning and training load analysis. Journal of Sports Science and Medicine, 2024, 23(1): 537-547.
29.	Ibitoye M O, Hamzaid N A, Abdul Wahab A K, et al. SVR modelling of mechanomyographic signals predicts neuromuscular stimulation-evoked knee torque in paralyzed quadriceps muscles undergoing knee extension exercise. Computers in Biology and Medicine, 2020, 117: 103614.
30.	Amendolara A, Pfister D, Settelmayer M, et al. An overview of machine learning applications in sports injury prediction. Cureus, 2023, 15(9): e46170.
31.	Martin R K, Wastvedt S, Pareek A, et al. Unsupervised machine learning of the combined danish and norwegian knee ligament registers: identification of 5 distinct patient groups with differing ACL revision rates. The American Journal of Sports Medicine, 2024, 52(4): 881-891.
32.	Suda E Y, Watari R, Matias A B, et al. Recognition of foot-ankle movement patterns in long-distance runners with different experience levels using support vector machines. Frontiers in Bioengineering and Biotechnology, 2020, 8: 576.
33.	Zhao Z, Yang J, Liu J, et al. Application of additive manufacturing and deep learning in exercise state discrimination. Sensors, 2025, 25(2): 389.
34.	Van Hooren B, Van Rengs L, Meijer K. Predicting musculoskeletal loading at common running injury locations using machine learning and instrumented insoles. Medicine and Science in Sports and Exercise, 2024, 56(10): 2059-2075.
35.	Ackland D C, Fang Z, Senanayake D. A machine learning approach to real-time calculation of joint angles during walking and running using self-placed inertial measurement units. Gait Posture, 2025, 118: 85-91.
36.	Xiang L, Gu Y, Gao Z, et al. Integrating an LSTM framework for predicting ankle joint biomechanics during gait using inertial sensors. Computers in Biology and Medicine, 2024, 170: 108016.
37.	Jiang D, Wu Z, Hsieh C Y, et al. Could graph neural networks learn better molecular representation for drug discovery? a comparison study of descriptor-based and graph-based models. Journal of Cheminformatics, 2021, 13(1): 12.
38.	Cui J, Du H, Wu X. Data analysis of physical recovery and injury prevention in sports teaching based on wearable devices. Preventive Medicine, 2023, 173: 107589.
39.	Zhou L, Mao J. Vision Transformer-based recognition tasks: a critical review. Journal of Image and Graphics, 2023, 28(10): 2969-3003.
40.	Ni M, Zhao Y, Zhang L, et al. MRI-based automated multitask deep learning system to evaluate supraspinatus tendon injuries. European Radiology, 2024, 34(6): 3538-3551.
41.	Ni M, Chen W, Zhao Q, et al. Deep learning approach for MRI in the classification of anterior talofibular ligament injuries. Journal of Magnetic Resonance Imaging, 2023, 58(5): 1544-1556.

1. Mandorino M, Figueiredo A J, Gjaka M, et al. Injury incidence and risk factors in youth soccer players: a systematic literature review. Part II: Intrinsic and extrinsic risk factors. Biol Sport, 2023, 40(1): 27-49.
2. Zhang D, Khalili A, Asgharian M. Post-model-selection inference in linear regression models: an integrated review. Statistics Surveys, 2022, 16: 86-136.
3. Beato M, Jaward M H, Nassis G P, et al. An educational review on machine learning: a SWOT analysis for implementing machine learning techniques in football. International Journal of Sports Physiology and Performance, 2024, 20(2): 183-196.
4. Bernardes R. Machine learning-basic principles. Acta Ophthalmologica, 2024, 102(S279): 26-28.
5. Quan W, Zhou H, Xu D, et al. Competitive and recreational running kinematics examined using principal components analysis. Healthcare (Basel), 2021, 9(10): 1321.
6. Majumdar A, Bakirov R, Hodges D, et al. Machine learning for understanding and predicting injuries in football. Sports Med Open, 2022, 8(1): 73.
7. Reis F J J, Alaiti R K, Vallio C S, et al. Artificial intelligence and machine learning approaches in sports: concepts, applications, challenges, and future perspectives. Brazilian Journal of Physical Therapy, 2024, 28(3): 101083.
8. Sindayigaya L, Dey A. Machine learning algorithms: a review. International Journal of Science and Research, 2022, 11(8): 1127-1133.
9. Carey D, Ong K L, Whiteley R, et al. Predictive modelling of training loads and injury in Australian football. International Journal of Computer Science in Sport, 2018, 17(1): 49-66.
10. Leckey C, van Dyk N, Doherty C, et al. Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis. British Journal of Sports Medicine, 2025, 59(7): 491-500.
11. Khatun Z, Jónsson H, Tsirilaki M, et al. Beyond pixel: superpixel-based MRI segmentation through traditional machine learning and graph convolutional network. Computer Methods and Programs in Biomedicine, 2024, 256: 108398.
12. Yin R, Chen H, Wang C, et al. Transformer-based multilabel deep learning model is efficient for detecting ankle lateral and medial ligament injuries on magnetic resonance imaging and improving clinicians’ diagnostic accuracy for rotational chronic ankle instability. Arthroscopy: The Journal of Arthroscopic & Related Surgery, 2025, 41(3): 574-584.
13. Ding N, Takeda K, Fujii K. Deep reinforcement learning in a racket sport for player evaluation with technical and tactical contexts. IEEE Access, 2022, 10: 54764-54772.
14. Dingenen B, Staes F, Vanelderen R, et al. Subclassification of recreational runners with a running-related injury based on running kinematics evaluated with marker-based two-dimensional video analysis. Physical Therapy in Sport, 2020, 44: 99-106.
15. Martin J A, Heiderscheit B C. A hierarchical clustering approach for examining the relationship between pelvis-proximal femur geometry and bone stress injury in runners. Journal of Biomechanics, 2023, 160: 111782.
16. Shakya A, Pillai G, Chakrabarty S. Reinforcement learning algorithms: a brief survey. Expert Systems with Applications, 2023, 231(30): 120495.
17. Song S, Kidziński ?, Peng X B, et al. Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation. Journal of Neuroengineering and Rehabilitation, 2021, 18(1): 126.
18. Schumacher P, Geijtenbeek T, Caggiano V, et al. Emergence of natural and robust bipedal walking by learning from biologically plausible objectives. iScience, 2025, 28(4): 112203.
19. Kimura R, Sato T, Kasukawa Y, et al. Automatic assist level adjustment function of a gait exercise rehabilitation robot with functional electrical stimulation for spinal cord injury: insights from clinical trials. Biomimetics, 2024, 9(10): 621.
20. Lu Y, Pareek A, Lavoie-Gagne O Z, et al. Machine learning for predicting lower extremity muscle strain in National Basketball Association athletes. Orthopaedic Journal of Sports Medicine, 2022, 10(7): 23259671221111742.
21. Moghadam S M, Yeung T, Choisne J. A comparison of machine learning models’ accuracy in predicting lower-limb joints’ kinematics, kinetics, and muscle forces from wearable sensors. Scientific Reports, 2023, 13(1): 5046.
22. Sheridan D, Jaspers A, Viet Cuong D, et al. Estimating oxygen uptake in simulated team sports using machine learning models and wearable sensor data: a pilot study. PLoS One, 2025, 20(4): e0319760.
23. Richter C, O’Reilly M, Delahunt E. Machine learning in sports science: challenges and opportunities. Sports Biomechanics, 2024, 23(8): 961-967.
24. Rahimi Goloujeh M, Allen J L. Motor modules are largely unaffected by pathological walking biomechanics: a simulation study. Journal of Neuroengineering and Rehabilitation, 2025, 22(1): 16.
25. Trudeau M B, von Tscharner V, Vienneau J, et al. Assessing footwear effects from principal features of plantar loading during running. Medicine and Science in Sports and Exercise, 2015, 47(9): 1988-1996.
26. 魏夢力, 鐘亞平, 桂輝賢, 等. 基于機器學習的運動損傷預警模型. 中國組織工程研究, 2025, 29(2): 409-418.
27. Richardson E, Trevizani R, Greenbaum J A, et al. The receiver operating characteristic curve accurately assesses imbalanced datasets. Patterns, 2024, 5(6): 100994.
28. Tsilimigkras T, Kakkos I, Matsopoulos G K, et al. Enhancing sports injury risk assessment in soccer through machine learning and training load analysis. Journal of Sports Science and Medicine, 2024, 23(1): 537-547.
29. Ibitoye M O, Hamzaid N A, Abdul Wahab A K, et al. SVR modelling of mechanomyographic signals predicts neuromuscular stimulation-evoked knee torque in paralyzed quadriceps muscles undergoing knee extension exercise. Computers in Biology and Medicine, 2020, 117: 103614.
30. Amendolara A, Pfister D, Settelmayer M, et al. An overview of machine learning applications in sports injury prediction. Cureus, 2023, 15(9): e46170.
31. Martin R K, Wastvedt S, Pareek A, et al. Unsupervised machine learning of the combined danish and norwegian knee ligament registers: identification of 5 distinct patient groups with differing ACL revision rates. The American Journal of Sports Medicine, 2024, 52(4): 881-891.
32. Suda E Y, Watari R, Matias A B, et al. Recognition of foot-ankle movement patterns in long-distance runners with different experience levels using support vector machines. Frontiers in Bioengineering and Biotechnology, 2020, 8: 576.
33. Zhao Z, Yang J, Liu J, et al. Application of additive manufacturing and deep learning in exercise state discrimination. Sensors, 2025, 25(2): 389.
34. Van Hooren B, Van Rengs L, Meijer K. Predicting musculoskeletal loading at common running injury locations using machine learning and instrumented insoles. Medicine and Science in Sports and Exercise, 2024, 56(10): 2059-2075.
35. Ackland D C, Fang Z, Senanayake D. A machine learning approach to real-time calculation of joint angles during walking and running using self-placed inertial measurement units. Gait Posture, 2025, 118: 85-91.
36. Xiang L, Gu Y, Gao Z, et al. Integrating an LSTM framework for predicting ankle joint biomechanics during gait using inertial sensors. Computers in Biology and Medicine, 2024, 170: 108016.
37. Jiang D, Wu Z, Hsieh C Y, et al. Could graph neural networks learn better molecular representation for drug discovery? a comparison study of descriptor-based and graph-based models. Journal of Cheminformatics, 2021, 13(1): 12.
38. Cui J, Du H, Wu X. Data analysis of physical recovery and injury prevention in sports teaching based on wearable devices. Preventive Medicine, 2023, 173: 107589.
39. Zhou L, Mao J. Vision Transformer-based recognition tasks: a critical review. Journal of Image and Graphics, 2023, 28(10): 2969-3003.
40. Ni M, Zhao Y, Zhang L, et al. MRI-based automated multitask deep learning system to evaluate supraspinatus tendon injuries. European Radiology, 2024, 34(6): 3538-3551.
41. Ni M, Chen W, Zhao Q, et al. Deep learning approach for MRI in the classification of anterior talofibular ligament injuries. Journal of Magnetic Resonance Imaging, 2023, 58(5): 1544-1556.

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Progress and application of machine learning in sports injury research

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