Emerging sports science technologies in decoding and preventing joint injuries A new era for athletics in China and Asia
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Abstract
Sports injuries are a major issue for all athletes, but joint injuries particularly so for athletes in the rigorous sport of athletics, whose very sustainability depends on the sustainability of athletes. New sports science technologies are turning joint injuries into a transparent box problem with new understanding of their causes and new efficacy in preventing them. In this article, we review the state of the art in sports science technologies such as wearable sensors, artificial intelligence, computer vision and biomechanical modelling which work together to decode the micro-mechanics of joint damage. With an application and current research perspective centred around China and Asia, this article addresses the challenges remaining in these technologies including data integration, accessibility of technology, and development of intervention solutions tailored to individuals. Through the discussion of representative case studies, this review highlights how these new technologies enable personalised and precise joint management for improved injury prevention and rehabilitation. This review aims to drive the development of an intelligent prevention ecosystem which can not only improve the performance of Asian athletes but also protect their musculoskeletal health in the new era of sports medicine and athletics training.
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Aghaeeaval, M., Bendahan, N., Shivji, Z., McInnis, C., Jamzad, A., Lomax, L. B., Shukla, G., Mousavi, P., & Winston, G. P. (2021). Prediction of patient survival following postanoxic coma using EEG data and clinical features. Annu Int Conf IEEE Eng Med Biol Soc, 2021, 997-1000. https://doi.org/10.1109/EMBC46164.2021.9629946
Ajalik, R. E., Alenchery, R. G., Cognetti, J. S., Zhang, V. Z., McGrath, J. L., Miller, B. L., & Awad, H. A. (2022). Human Organ-on-a-Chip Microphysiological Systems to Model Musculoskeletal Pathologies and Accelerate Therapeutic Discovery. Front Bioeng Biotechnol, 10, 846230. https://doi.org/10.3389/fbioe.2022.846230
Arauz, P. G., Garcia, G., & Llerena, J. (2024). Biomechanical analysis of the snatch technique for elite and varsity weightlifters. J Biomech, 175, 112291. https://doi.org/10.1016/j.jbiomech.2024.112291
Arredondo-Soto, M., García-Murillo, M. A., Vidal-Lesso, A., Jesús Cervantes-Sánchez, J., & Moreno, H. A. (2021). A Novel Kinematic Model of the Tibiofemoral Joint Based on a Parallel Mechanism. J Biomech Eng, 143(6). https://doi.org/10.1115/1.4050034
Avci, O., & Röhrle, O. (2024). Determining a musculoskeletal system's pre-stretched state using continuum-mechanical forward modelling and joint range optimization. Biomech Model Mechanobiol, 23(3), 1031-1053. https://doi.org/10.1007/s10237-024-01821-x
Ayala, A. E., Khwaja, A., Goodison, B. C., Smith, S. L., Kim, S. Y., Irwin, J. T., & Latt, L. D. (2024). Effect of Fibular Malrotation on Tibiotalar Joint Contact Mechanics in a Weber B Ankle Fracture Model. Foot Ankle Spec, 17(6), 577-584. https://doi.org/10.1177/19386400221127835
Bedard, T. A., Johnson, C. K., Amendola, R. L., Scuderi, M. G., Ordway, N. R., Werner, F. W., & Cannizzaro, J. P. (2025). A biomechanical analysis of four medial patellofemoral ligament reconstruction techniques. Clin Biomech (Bristol), 122, 106401. https://doi.org/10.1016/j.clinbiomech.2024.106401
Bender, J. L., Flora, P. K., Milosevic, E., Soheilipour, S., Maharaj, N., Dirlea, M., Parvin, L., Matthew, A., & Kazanjian, A. (2021). Training prostate cancer survivors and caregivers to be peer navigators: a blended online/in-person competency-based training program. Support Care Cancer, 29(3), 1235-1244. https://doi.org/10.1007/s00520-020-05586-8
Bini, S. A., Gillian, N., Peterson, T. A., Souza, R. B., Schultz, B., Mormul, W., Cichoń, M. K., Szczotka, A. B., & Poupyrev, I. (2025). Unlocking Gait Analysis Beyond the Gait Lab: High-Fidelity Replication of Knee Kinematics Using Inertial Motion Units and a Convolutional Neural Network. Arthroplast Today, 33, 101656. https://doi.org/10.1016/j.artd.2025.101656
Cai, D., Xiao, T., Zou, A., Mao, L., Chi, B., Wang, Y., Wang, Q., Ji, Y., & Sun, L. (2022). Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases. Front Cardiovasc Med, 9, 964894. https://doi.org/10.3389/fcvm.2022.964894
Chang, J., Li, J., Ye, J., Zhang, B., Chen, J., Xia, Y., Lei, J., Carlson, T., Loureiro, R., Korsunsky, A. M., Tan, J. C., & Zhao, H. (2025). AI-Enabled Piezoelectric Wearable for Joint Torque Monitoring. Nanomicro Lett, 17(1), 247. https://doi.org/10.1007/s40820-025-01753-w
Chaumeil, A., Lahkar, B. K., Dumas, R., Muller, A., & Robert, T. (2024). Agreement between a markerless and a marker-based motion capture systems for balance related quantities. J Biomech, 165, 112018. https://doi.org/10.1016/j.jbiomech.2024.112018
Chen, T., Xu, D., Wang, M., Zhou, Z., Jie, T., Zhou, H., Yuan, Y., Baker, J. S., Gao, Z., & Gu, Y. (2025). Wearable monitoring for rehabilitation: Deep learning-driven vertical ground reaction force estimation for anterior cruciate ligament reconstruction. Clin Biomech (Bristol), 130, 106663. https://doi.org/10.1016/j.clinbiomech.2025.106663
Claudino, J. G., Capanema, D. O., de Souza, T. V., Serrão, J. C., Machado Pereira, A. C., & Nassis, G. P. (2019). Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review. Sports Med Open, 5(1), 28. https://doi.org/10.1186/s40798-019-0202-3
Cooper, D. J., Batt, M. E., O'Hanlon, M. S., & Palmer, D. (2021). A Cross-Sectional Study of Retired Great British Olympians (Berlin 1936-Sochi 2014): Olympic Career Injuries, Joint Health in Later Life, and Reasons for Retirement from Olympic Sport. Sports Med Open, 7(1), 54. https://doi.org/10.1186/s40798-021-00339-1
Crisafulli, D., Spataro, M., De Marchis, C., Risitano, G., & Milone, D. (2024). A New Sensorized Approach Based on a DeepLabCut Model and IR Thermography for Characterizing the Thermal Profile in Knees During Exercise. Sensors (Basel), 24(23). https://doi.org/10.3390/s24237862
Davidoviča, A., Davidovičs, S., Semjonova, G., Katashev, A., Oks, A., Lancere, L., Tomsone, S., & Zolovs, M. (2025). Correlation of Biomechanical Variables of Lower Extremity Movement During Functional Tests and Tasks in Youth League Football Players: Cross-Sectional Correlation Study. JMIR Form Res, 9, e69046. https://doi.org/10.2196/69046
Di Paolo, S., Lopomo, N. F., Della Villa, F., Paolini, G., Figari, G., Bragonzoni, L., Grassi, A., & Zaffagnini, S. (2021). Rehabilitation and Return to Sport Assessment after Anterior Cruciate Ligament Injury: Quantifying Joint Kinematics during Complex High-Speed Tasks through Wearable Sensors. Sensors (Basel), 21(7). https://doi.org/10.3390/s21072331
Gu, W., & Pandy, M. G. (2020). Direct Validation of Human Knee-Joint Contact Mechanics Derived From Subject-Specific Finite-Element Models of the Tibiofemoral and Patellofemoral Joints. J Biomech Eng, 142(7). https://doi.org/10.1115/1.4045594
Heneghan, N. R., Collacott, E., Martin, P., Spencer, S., & Rushton, A. (2021). Lumbosacral injuries in elite Paralympic athletes with limb deficiency: a retrospective analysis of patient records. BMJ Open Sport Exerc Med, 7(1), e001001. https://doi.org/10.1136/bmjsem-2020-001001
Heneghan, N. R., Heathcote, L., Martin, P., Spencer, S., & Rushton, A. (2020). Injury surveillance in elite Paralympic athletes with limb deficiency: a retrospective analysis of upper quadrant injuries. BMC Sports Sci Med Rehabil, 12, 36. https://doi.org/10.1186/s13102-020-00183-y
Hind, K., Konerth, N., Entwistle, I., Theadom, A., Lewis, G., King, D., Chazot, P., & Hume, P. (2020). Cumulative Sport-Related Injuries and Longer Term Impact in Retired Male Elite- and Amateur-Level Rugby Code Athletes and Non-contact Athletes: A Retrospective Study. Sports Med, 50(11), 2051-2061. https://doi.org/10.1007/s40279-020-01310-y
Jiang, Z., Hao, Y., Jin, N., & Li, Y. (2022). A Systematic Review of the Relationship between Workload and Injury Risk of Professional Male Soccer Players. Int J Environ Res Public Health, 19(20). https://doi.org/10.3390/ijerph192013237
Jiang, Z., & Shen, Y. (2025). Multimodal learning for enhanced SPECT/CT imaging in sports injury diagnosis. Front Physiol, 16, 1605426. https://doi.org/10.3389/fphys.2025.1605426
Jung, S., Zhou, M., Ma, J., Yang, R., Cramer, S. C., Dobkin, B. H., Yang, L. F., & Rosen, J. (2024). Wearable Body Sensors Integrated into a Virtual Reality Environment - A Modality for Automating the Rehabilitation of the Motor Control System. Annu Int Conf IEEE Eng Med Biol Soc, 2024, 1-4. https://doi.org/10.1109/EMBC53108.2024.10782207
Kanko, R. M., Laende, E. K., Strutzenberger, G., Brown, M., Selbie, W. S., DePaul, V., Scott, S. H., & Deluzio, K. J. (2021). Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system. J Biomech, 122, 110414. https://doi.org/10.1016/j.jbiomech.2021.110414
Kellis, E., Sahinis, C., & Baltzopoulos, V. (2023). Is hamstrings-to-quadriceps torque ratio useful for predicting anterior cruciate ligament and hamstring injuries? A systematic and critical review. J Sport Health Sci, 12(3), 343-358. https://doi.org/10.1016/j.jshs.2022.01.002
Khandan, A., Fathian, R., Carey, J. P., & Rouhani, H. (2022). Assessment of Three-Dimensional Kinematics of High- and Low-Calibre Hockey Skaters on Synthetic ice Using Wearable Sensors. Sensors (Basel), 23(1). https://doi.org/10.3390/s23010334
Lahkar, B. K., Muller, A., Dumas, R., Reveret, L., & Robert, T. (2022). Accuracy of a markerless motion capture system in estimating upper extremity kinematics during boxing. Front Sports Act Living, 4, 939980. https://doi.org/10.3389/fspor.2022.939980
Lambert, C., Ritzmann, R., Akoto, R., Lambert, M., Pfeiffer, T., Wolfarth, B., Lachmann, D., & Shafizadeh, S. (2022). Epidemiology of Injuries in Olympic Sports. Int J Sports Med, 43(5), 473-481. https://doi.org/10.1055/a-1641-0068
Lambert, C., Ritzmann, R., Akoto, R., Lambert, M., Pfeiffer, T., Wolfarth, B., Lachmann, D., & Shafizadeh, S. (2024). Epidemiology of Injuries in Olympic Sports. Sportverletz Sportschaden, 38(1), 18-26. https://doi.org/10.1055/a-2036-8166
Laupattarakasem, P., Cook, J. L., Stannard, J. P., Smith, P. A., Blecha, K. M., Guess, T. M., Sharp, R. L., & Leary, E. (2024). Using a Markerless Motion Capture System to Identify Preinjury Differences in Functional Assessments. J Knee Surg, 37(8), 570-576. https://doi.org/10.1055/s-0043-1772238
Lin, Y., Zhang, J., Ji, M., Li, X., Wang, D., Chen, L., & Ma, X. (2025). Automated rapid design method for personalized gradient pressure-relieving insoles. Proc Inst Mech Eng H, 239(9), 966-975. https://doi.org/10.1177/09544119251372338
Lloyd, D. G., Saxby, D. J., Pizzolato, C., Worsey, M., Diamond, L. E., Palipana, D., Bourne, M., de Sousa, A. C., Mannan, M. M. N., Nasseri, A., Perevoshchikova, N., Maharaj, J., Crossley, C., Quinn, A., Mulholland, K., Collings, T., Xia, Z., Cornish, B., Devaprakash, D., Barrett, R. S. (2023). Maintaining soldier musculoskeletal health using personalised digital humans, wearables and/or computer vision. J Sci Med Sport, 26 Suppl 1, S30-s39. https://doi.org/10.1016/j.jsams.2023.04.001
Lusi, S., Lacroix, M., Guerini, H., Campagna, R., Drapé, J. L., & Feydy, A. (2025). Shoulder Cuff Tears in Athletes: Magnetic Resonance Imaging Findings. Semin Musculoskelet Radiol, 29(3), 432-441. https://doi.org/10.1055/s-0045-1805076
Ma, M., Song, Q., & Liu, H. (2024). The effect of personalized orthopedic insoles on plantar pressure during running in subtle cavus foot. Front Bioeng Biotechnol, 12, 1343001. https://doi.org/10.3389/fbioe.2024.1343001
McCoy, J. A., Levine, L. D., Wan, G., Chivers, C., Teel, J., & La Cava, W. G. (2025). Intrapartum electronic fetal heart rate monitoring to predict acidemia at birth with the use of deep learning. Am J Obstet Gynecol, 232(1), 116.e111-116.e119. https://doi.org/10.1016/j.ajog.2024.04.022
Musat, C. L., Mereuta, C., Nechita, A., Tutunaru, D., Voipan, A. E., Voipan, D., Mereuta, E., Gurau, T. V., Gurău, G., & Nechita, L. C. (2024). Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods. Diagnostics (Basel), 14(22). https://doi.org/10.3390/diagnostics14222516
Nishikawa, N., Watanabe, S., & Yamamoto, K. (2025). Comparison of kinematics between markerless and marker-based motion capture systems for change of direction maneuvers. J Biomech, 192, 112965. https://doi.org/10.1016/j.jbiomech.2025.112965
Nouman, M., Dissaneewate, T., Leelasamran, W., & Chatpun, S. (2019). The insole materials influence the plantar pressure distributions in diabetic foot with neuropathy during different walking activities. Gait Posture, 74, 154-161. https://doi.org/10.1016/j.gaitpost.2019.08.023
Patra, Y., Liu, Q., Chan, R. H. M., Thomson, D., Chow, D. H. K., Fuller, B., & Cheung, R. T. H. (2023). Tracking Bilateral Lower Limb Kinematics of Distance Runners on Treadmill Using a Single Inertial Measurement Unit. Annu Int Conf IEEE Eng Med Biol Soc, 2023, 1-4. https://doi.org/10.1109/EMBC40787.2023.10339970
Ptaszyk, O., Boutefnouchet, T., Cummins, G., Kim, J. M., & Ding, Z. (2025). Wearable Devices for the Quantitative Assessment of Knee Joint Function After Anterior Cruciate Ligament Injury or Reconstruction: A Scoping Review. Sensors (Basel), 25(18). https://doi.org/10.3390/s25185837
Sarin, J. K., Te Moller, N. C. R., Mohammadi, A., Prakash, M., Torniainen, J., Brommer, H., Nippolainen, E., Shaikh, R., Mäkelä, J. T. A., Korhonen, R. K., van Weeren, P. R., Afara, I. O., & Töyräs, J. (2021). Machine learning augmented near-infrared spectroscopy: In vivo follow-up of cartilage defects. Osteoarthritis Cartilage, 29(3), 423-432. https://doi.org/10.1016/j.joca.2020.12.007
Scataglini, S., Abts, E., Van Bocxlaer, C., Van den Bussche, M., Meletani, S., & Truijen, S. (2024). Accuracy, Validity, and Reliability of Markerless Camera-Based 3D Motion Capture Systems versus Marker-Based 3D Motion Capture Systems in Gait Analysis: A Systematic Review and Meta-Analysis. Sensors (Basel), 24(11). https://doi.org/10.3390/s24113686
Shiao, Y., Chen, G. Y., & Hoang, T. (2024). Three-Dimensional Human Posture Recognition by Extremity Angle Estimation with Minimal IMU Sensor. Sensors (Basel), 24(13). https://doi.org/10.3390/s24134306
Silveira, S. L., Froehlich-Grobe, K., & Motl, R. W. (2023). Formative evaluation of an exercise training program for persons with multiple sclerosis who are wheelchair users. Eval Program Plann, 97, 102243. https://doi.org/10.1016/j.evalprogplan.2023.102243
Suo, X., Tang, W., & Li, Z. (2024). Motion Capture Technology in Sports Scenarios: A Survey. Sensors (Basel), 24(9). https://doi.org/10.3390/s24092947
Taketomi, S., Kawaguchi, K., Mizutani, Y., Takei, S., Yamagami, R., Kono, K., Murakami, R., Arakawa, T., Kage, T., Kobayashi, T., Furukawa, Y., Arino, Y., Fujiwara, S., Tanaka, S., & Ogata, T. (2024). Intrinsic Risk Factors for Noncontact Anterior Cruciate Ligament Injury in Young Female Soccer Players: A Prospective Cohort Study. Am J Sports Med, 52(12), 2972-2979. https://doi.org/10.1177/03635465241278745
Taketomi, S., Kawaguchi, K., Mizutani, Y., Takei, S., Yamagami, R., Kono, K., Murakami, R., Kage, T., Arakawa, T., Fujiwara, S., Tanaka, S., & Ogata, T. (2024). Lower hamstring to quadriceps muscle strength ratio and lower body weight as factors associated with noncontact anterior cruciate ligament injury in male American football players: A prospective cohort study. Asia Pac J Sports Med Arthrosc Rehabil Technol, 35, 43-47. https://doi.org/10.1016/j.asmart.2023.11.006
Torvinen, P., Ruotsalainen, K. S., Zhao, S., Cronin, N., Ohtonen, O., & Linnamo, V. (2024). Evaluation of 3D Markerless Motion Capture System Accuracy during Skate Skiing on a Treadmill. Bioengineering (Basel), 11(2). https://doi.org/10.3390/bioengineering11020136
Uzuner, S., Li, L., Kucuk, S., & Memisoglu, K. (2020). Changes in Knee Joint Mechanics After Medial Meniscectomy Determined With a Poromechanical Model. J Biomech Eng, 142(10). https://doi.org/10.1115/1.4047343
van Loggerenberg, C., Ramagole, D. A., van Rensburg, A. J., van Rensburg, D. J., & Boer, P. (2025). Sports-related injuries and illnesses amongst adolescent athletes in an urban sports medicine practice setting: a one-year prospective study. S Afr J Sports Med, 37(1), v37i31a20756. https://doi.org/10.17159/2078-516X/2025/v37i1a20756
Wang, C., Du, W., Li, Z., & Chen, W. (2025). Effects of custom-made insole on the mechanical response characteristics of the foot during static standing and walking. Proc Inst Mech Eng H, 239(4), 360-369. https://doi.org/10.1177/09544119251328060
Wang, Y. T., Chen, J. C., & Lin, Y. S. (2020). Effects of Artificial Texture Insoles and Foot Arches on Improving Arch Collapse in Flat Feet. Sensors (Basel), 20(13). https://doi.org/10.3390/s20133667
Xu, R., Wang, Z., Ren, Z., Ma, T., Jia, Z., Fang, S., & Jin, H. (2019). Comparative Study of the Effects of Customized 3D printed insole and Prefabricated Insole on Plantar Pressure and Comfort in Patients with Symptomatic Flatfoot. Med Sci Monit, 25, 3510-3519. https://doi.org/10.12659/MSM.916975
Yu, L., Jiang, H., Mei, Q., Mohamad, N. I., Fernandez, J., & Gu, Y. (2023). Intelligent prediction of lower extremity loadings during badminton lunge footwork in a lab-simulated court. Front Bioeng Biotechnol, 11, 1229574. https://doi.org/10.3389/fbioe.2023.1229574
Zhong, Z., & Di, W. (2025). Technological advancements in sports injury: diagnosis and treatment. J Sports Med Phys Fitness. https://doi.org/10.23736/S0022-4707.25.16817-5