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Digital human and embodied intelligence for sports science: advancements, opportunities and prospects

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Abstract

This paper presents a comprehensive review of state-of-the-art motion capture techniques for digital human modeling in sports, including traditional optical motion capture systems, wearable sensor capture systems, computer vision capture systems, and fusion motion capture systems. The review explores the strengths, limitations, and applications of each technique in the context of sports science, such as performance analysis, technique optimization, injury prevention, and interactive training. The paper highlights the significance of accurate and comprehensive motion data acquisition for creating high-fidelity digital human models that can replicate an athlete’s movements and biomechanics. However, several challenges and limitations are identified, such as limited capture volume, marker occlusion, accuracy limitations, lack of diverse datasets, and computational complexity. To address these challenges, the paper emphasizes the need for collaborative efforts from researchers and practitioners across various disciplines. By bridging theory and practice and identifying application-specific challenges and solutions, this review aims to facilitate cross-disciplinary collaboration and guide future research and development efforts in harnessing the power of digital human technology for sports science advancement, ultimately unlocking new possibilities for athlete performance optimization and health.

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References

  1. Peters, M., et al.: Biomechanical digital human models: chances and challenges to expand ergonomic evaluation. In: Ahram, T., Karwowski, W., Taiar, R. (eds.) Human Systems Engineering and Design, pp. 885–890. Springer International Publishing, Cham (2019). https://doi.org/10.1007/978-3-030-02053-8_134

    Chapter  MATH  Google Scholar 

  2. Aughey, R.J., et al.: Comparison of a computer vision system against three-dimensional motion capture for tracking football movements in a stadium environment. Sports Eng. 25(1), 2 (2022). https://doi.org/10.1007/s12283-021-00365-y

    Article  Google Scholar 

  3. Lorenz, E.A., Su, X., Skjæret-Maroni, N.: A review of combined functional neuroimaging and motion capture for motor rehabilitation. J. Neuroeng. Rehabil. 21(1), 3 (2024). https://doi.org/10.1186/s12984-023-01294-6

    Article  MATH  Google Scholar 

  4. Das, K., de Paula Oliveira, T., Newell, J.: Comparison of markerless and marker-based motion capture systems using 95% functional limits of agreement in a linear mixed-effects modelling framework. Sci. Rep. 13(1), 22880 (2023). https://doi.org/10.1038/s41598-023-49360-2

    Article  Google Scholar 

  5. Haratian, R.: Motion capture sensing technologies and techniques: a sensor agnostic approach to address wearability challenges. Sens Imaging 23(1), 25 (2022). https://doi.org/10.1007/s11220-022-00394-2

    Article  Google Scholar 

  6. Lam, W.W.T., Tang, Y.M., Fong, K.N.K.: A systematic review of the applications of markerless motion capture (MMC) technology for clinical measurement in rehabilitation. J. Neuroeng. Rehabil. 20(1), 57 (2023). https://doi.org/10.1186/s12984-023-01186-9

    Article  MATH  Google Scholar 

  7. Lin, X., Sun, S., Huang, W., Sheng, B., Li, P., Feng, D.D.: EAPT: efficient attention pyramid transformer for image processing. IEEE Trans. Multimed. 25, 50–61 (2023). https://doi.org/10.1109/TMM.2021.3120873

    Article  MATH  Google Scholar 

  8. Armitano-Lago, C., Willoughby, D., Kiefer, A.W.: A SWOT analysis of portable and low-cost markerless motion capture systems to assess lower-limb musculoskeletal kinematics in sport. Front. Sports Act. Living 3, 809898 (2022). https://doi.org/10.3389/fspor.2021.809898

    Article  Google Scholar 

  9. Torvinen, P., Ruotsalainen, K.S., Zhao, S., Cronin, N., Ohtonen, O., Linnamo, V.: Evaluation of 3D markerless motion capture system accuracy during skate skiing on a treadmill. Bioengineering 11(2), 136 (2024). https://doi.org/10.3390/bioengineering11020136

    Article  Google Scholar 

  10. Sawan, N., Eltweri, A., De Lucia, C., Pio Leonardo Cavaliere, L., Faccia, A., Roxana Moşteanu, N.: Mixed and augmented reality applications in the sport industry. In: Proceedings of the 2020 2nd International Conference on E-Business and E-commerce Engineering, in EBEE ’20, pp. 55–59. Association for Computing Machinery, New York, NY, USA. (Mar. 2021). https://doi.org/10.1145/3446922.3446932

  11. Colyer, S.L., Evans, M., Cosker, D.P., Salo, A.I.T.: A review of the evolution of vision-based motion analysis and the integration of advanced computer vision methods towards developing a markerless system. Sports Med.-Open 4(1), 24 (2018). https://doi.org/10.1186/s40798-018-0139-y

    Article  Google Scholar 

  12. Aurand, A.M., Dufour, J.S., Marras, W.S.: Accuracy map of an optical motion capture system with 42 or 21 cameras in a large measurement volume. J. Biomech. 58, 237–240 (2017). https://doi.org/10.1016/j.jbiomech.2017.05.006

    Article  MATH  Google Scholar 

  13. Merriaux, P., Dupuis, Y., Boutteau, R., Vasseur, P., Savatier, X.: A study of vicon system positioning performance. Sensors 17(7), 1591 (2017). https://doi.org/10.3390/s17071591

    Article  Google Scholar 

  14. Corazza, S., Mündermann, L., Gambaretto, E., Ferrigno, G., Andriacchi, T.P.: Markerless motion capture through visual hull, articulated icp and subject specific model generation. Int. J. Comput. Vision 87(1), 156 (2009). https://doi.org/10.1007/s11263-009-0284-3

    Article  Google Scholar 

  15. Topley, M., Richards, J.G.: A comparison of currently available optoelectronic motion capture systems. J. Biomech. 106, 109820 (2020). https://doi.org/10.1016/j.jbiomech.2020.109820

    Article  MATH  Google Scholar 

  16. Trivedi, U., Menychtas, D., Alqasemi, R., Dubey, R.: Biomimetic approaches for human arm motion generation: literature review and future directions. Sensors 23(8), 3912 (2023). https://doi.org/10.3390/s23083912

    Article  MATH  Google Scholar 

  17. van der Kruk, E., Reijne, M.M.: Accuracy of human motion capture systems for sport applications; state-of-the-art review. Eur. J. Sport Sci. 18(6), 806–819 (2018). https://doi.org/10.1080/17461391.2018.1463397

    Article  MATH  Google Scholar 

  18. Nakano, N., et al.: Evaluation of 3d markerless motion capture accuracy using openpose with multiple video cameras. Front. Sports Act. Living 2, 50 (2020). https://doi.org/10.3389/fspor.2020.00050

    Article  MATH  Google Scholar 

  19. Pagnon, D., Domalain, M., Reveret, L.: Pose2Sim: an end-to-end workflow for 3D markerless sports kinematics—part 1: robustness. Sensors 21(19), 6530 (2021). https://doi.org/10.3390/s21196530

    Article  MATH  Google Scholar 

  20. Malus, J., et al.: Marker placement reliability and objectivity for biomechanical cohort study: healthy aging in industrial environment (HAIE—Program 4). Sensors 21(5), 1830 (2021). https://doi.org/10.3390/s21051830

    Article  MATH  Google Scholar 

  21. Kanko, R.M., Laende, E.K., Davis, E.M., Selbie, W.S., Deluzio, K.J.: Concurrent assessment of gait kinematics using marker-based and markerless motion capture. J. Biomech. 127, 110665 (2021). https://doi.org/10.1016/j.jbiomech.2021.110665

    Article  Google Scholar 

  22. Liu, X., et al.: Wearable devices for gait analysis in intelligent healthcare. Front. Comput. Sci. 3, 661676 (2021). https://doi.org/10.3389/fcomp.2021.661676

    Article  Google Scholar 

  23. Benjaminse, A., Bolt, R., Gokeler, A., Otten, B.: A validity study comparing xsens with vicon. ISBS Proc. Arch. 38(1), 752 (2020)

    Google Scholar 

  24. Umek, A., Kos, A.: Validation of UWB positioning systems for player tracking in tennis. Pers. Ubiquit. Comput. 26(4), 1023–1033 (2022). https://doi.org/10.1007/s00779-020-01486-0

    Article  MATH  Google Scholar 

  25. Wittmann, F., Lambercy, O., Gassert, R.: Magnetometer-based drift correction during rest in IMU arm motion tracking. Sensors 19(6), 1312 (2019). https://doi.org/10.3390/s19061312

    Article  Google Scholar 

  26. Retscher, G., Gikas, V., Hofer, H., Perakis, H., Kealy, A.: Range validation of UWB and Wi-Fi for integrated indoor positioning. Appl. Geomat. 11(2), 187–195 (2019). https://doi.org/10.1007/s12518-018-00252-5

    Article  Google Scholar 

  27. Stelzer, A., Pourvoyeur, K., Fischer, A.: Concept and application of LPM - a novel 3-D local position measurement system. IEEE Trans. Microw. Theory Tech. 52(12), 2664–2669 (2004). https://doi.org/10.1109/TMTT.2004.838281

    Article  MATH  Google Scholar 

  28. Li, X., et al.: Accuracy and reliability of multi-GNSS real-time precise positioning: GPS, GLONASS, BeiDou, and Galileo. J. Geod. 89(6), 607–635 (2015). https://doi.org/10.1007/s00190-015-0802-8

    Article  MATH  Google Scholar 

  29. Nguyen, K.D., Chen, I.-M., Luo, Z., Yeo, S.H., Duh, H.B.-L.: A wearable sensing system for tracking and monitoring of functional arm movement. IEEE/ASME Trans. Mechatron. 16(2), 213–220 (2011). https://doi.org/10.1109/TMECH.2009.2039222

    Article  Google Scholar 

  30. Ates, H.C., et al.: End-to-end design of wearable sensors. Nat. Rev. Mater. 7(11), 887–907 (2022). https://doi.org/10.1038/s41578-022-00460-x

    Article  MathSciNet  MATH  Google Scholar 

  31. Naik, B.T., Hashmi, M.F., Bokde, N.D.: A comprehensive review of computer vision in sports: open issues, future trends and research directions. Appl. Sci. 12(9), 4429 (2022). https://doi.org/10.3390/app12094429

    Article  MATH  Google Scholar 

  32. Sinha, A.K., Thalmann, N.M., Cai, Y.: Measuring anthropomorphism of a new humanoid hand-arm system. Int. J. Soc. Robot. 15(8), 1341–1363 (2023). https://doi.org/10.1007/s12369-023-00999-x

    Article  MATH  Google Scholar 

  33. Manakitsa, N., Maraslidis, G.S., Moysis, L., Fragulis, G.F.: A review of machine learning and deep learning for object detection, semantic segmentation, and human action recognition in machine and robotic vision. Technologies 12(2), 15 (2024). https://doi.org/10.3390/technologies12020015

    Article  MATH  Google Scholar 

  34. Al-Jebrni, A.H., et al.: SThy-Net: a feature fusion-enhanced dense-branched modules network for small thyroid nodule classification from ultrasound images. Vis. Comput. 39(8), 3675–3689 (2023). https://doi.org/10.1007/s00371-023-02984-x

    Article  MATH  Google Scholar 

  35. Avogaro, A., Cunico, F., Rosenhahn, B., Setti, F.: Markerless human pose estimation for biomedical applications: a survey. Front. Comput. Sci. 5, 1153160 (2023). https://doi.org/10.3389/fcomp.2023.1153160

    Article  MATH  Google Scholar 

  36. Zhang, Z., Zheng, J., Thalmann, N.M.: Context-aware personality estimation and emotion recognition in social interaction. Vis. Comput. (2023). https://doi.org/10.1007/s00371-023-02862-6

    Article  MATH  Google Scholar 

  37. Toshev A., Szegedy, C.: DeepPose: Human Pose Estimation via Deep Neural Networks, p. 8. (2014)

  38. Samkari, E., Arif, M., Alghamdi, M., Al Ghamdi, M.A.: Human pose estimation using deep learning: a systematic literature review. Mach. Learn. Knowl. Extr. 5(4), 1612–1659 (2023). https://doi.org/10.3390/make5040081

    Article  Google Scholar 

  39. Li, Y.-C., Chang, C.-T., Cheng, C.-C., Huang, Y.-L.: Baseball swing pose estimation using openpose. In: 2021 IEEE International Conference on Robotics, Automation and Artificial Intelligence (RAAI), pp. 6–9. (Apr. 2021) https://doi.org/10.1109/RAAI52226.2021.9507807

  40. Zhang, Z., Zheng, J., Magnenat Thalmann, N.: Engagement estimation of the elderly from wild multiparty human–robot interaction. Comput. Anim. Virtual Worlds 33(6), e2120 (2022). https://doi.org/10.1002/cav.2120

    Article  MATH  Google Scholar 

  41. Liu, W., Bao, Q., Sun, Y., Mei, T.: Recent advances of monocular 2D and 3D human pose estimation: a deep learning perspective. ACM Comput. Surv. 55(4), 1–41 (2022). https://doi.org/10.1145/3524497

    Article  MATH  Google Scholar 

  42. Khan, F., Salahuddin, S., Javidnia, H.: Deep learning-based monocular depth estimation methods—a state-of-the-art review. Sensors 20(8), 2272 (2020). https://doi.org/10.3390/s20082272

    Article  MATH  Google Scholar 

  43. Toshpulatov, M., Lee, W., Lee, S., Haghighian Roudsari, A.: Human pose, hand and mesh estimation using deep learning: a survey. J. Supercomput. 78(6), 7616–7654 (2022). https://doi.org/10.1007/s11227-021-04184-7

    Article  MATH  Google Scholar 

  44. Baumgartner, T., Paassen, B., Klatt, S.: Extracting spatial knowledge from track and field broadcasts for monocular 3D human pose estimation. Sci. Rep. 13(1), 14031 (2023). https://doi.org/10.1038/s41598-023-41142-0

    Article  MATH  Google Scholar 

  45. Yin, L., Han, R., Feng, W., Wang, S.: Self-supervised human pose based multi-camera video synchronization. In: Proceedings of the 30th ACM International Conference on Multimedia, in MM ’22, pp. 1739–1748. Association for Computing Machinery, New York, NY, USA. (2022). https://doi.org/10.1145/3503161.3547766

  46. Shan, W., Lu, H., Wang, S., Zhang, X., Gao, W.: Improving robustness and accuracy via relative information encoding in 3D human pose estimation. In: Proceedings of the 29th ACM International Conference on Multimedia, in MM ’21, pp. 3446–3454. Association for Computing Machinery. New York, NY, USA, (2021). https://doi.org/10.1145/3474085.3475504

  47. Tian, L., Cheng, X., Honda, M., Ikenaga, T.: Multi-view 3D human pose reconstruction based on spatial confidence point group for jump analysis in figure skating. Complex Intell. Syst. 9(1), 865–879 (2023). https://doi.org/10.1007/s40747-022-00837-z

    Article  Google Scholar 

  48. Pinheiro, G.D.S., Jin, X., Costa, V.T.D., Lames, M.: Body pose estimation integrated with notational analysis: a new approach to analyze penalty kicks strategy in elite football. Front. Sports Act. Living 4, 818556 (2022). https://doi.org/10.3389/fspor.2022.818556

    Article  MATH  Google Scholar 

  49. Duan, C., Hu, B., Liu, W., Song, J.: Motion capture for sporting events based on graph convolutional neural networks and single target pose estimation algorithms. Appl. Sci. 13(13), 7611 (2023). https://doi.org/10.3390/app13137611

    Article  Google Scholar 

  50. Xiao, L., Cao, Y., Gai, Y., Khezri, E., Liu, J., Yang, M.: Recognizing sports activities from video frames using deformable convolution and adaptive multiscale features. J. Cloud Comput. 12(1), 167 (2023). https://doi.org/10.1186/s13677-023-00552-1

    Article  Google Scholar 

  51. Lei, Q., Du, J.-X., Zhang, H.-B., Ye, S., Chen, D.-S.: A survey of vision-based human action evaluation methods. Sensors 19(19), 4129 (2019). https://doi.org/10.3390/s19194129

    Article  MATH  Google Scholar 

  52. Tang, W., Ren, Z., Wang, J.: Guest editorial: special issue on human pose estimation and its applications. Mach. Vis. Appl. 34(6), 120 (2023). https://doi.org/10.1007/s00138-023-01474-3

    Article  MATH  Google Scholar 

  53. Xu, L., Luo, H., Hui, B., Chang, Z.: Real-time robust tracking for motion blur and fast motion via correlation filters. Sensors 16(9), 1443 (2016). https://doi.org/10.3390/s16091443

    Article  MATH  Google Scholar 

  54. Hiemann, A., Kautz, T., Zottmann, T., Hlawitschka, M.: Enhancement of speed and accuracy trade-off for sports ball detection in videos—finding fast moving, small objects in real time. Sensors 21(9), 3214 (2021). https://doi.org/10.3390/s21093214

    Article  Google Scholar 

  55. Zhuang, H., Xia, Y., Wang, N., Dong, L.: High inclusiveness and accuracy motion blur real-time gesture recognition based on YOLOv4 model combined attention mechanism and DeblurGanv2. Appl. Sci. 11(21), 9982 (2021). https://doi.org/10.3390/app11219982

    Article  Google Scholar 

  56. Cobos, M., Ahrens, J., Kowalczyk, K., Politis, A.: An overview of machine learning and other data-based methods for spatial audio capture, processing, and reproduction. J. Audio Speech Music Proc. 2022(1), 10 (2022). https://doi.org/10.1186/s13636-022-00242-x

    Article  MATH  Google Scholar 

  57. Gurbuz, S.Z., Rahman, M.M., Kurtoglu, E., Martelli, D.: Continuous human activity recognition and step-time variability analysis with FMCW radar. In: 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 01–04. (Sep. 2022). https://doi.org/10.1109/BHI56158.2022.9926892

  58. Li, X., He, Y., Jing, X.: A survey of deep learning-based human activity recognition in radar. Remote Sens. 11(9), 1068 (2019). https://doi.org/10.3390/rs11091068

    Article  MATH  Google Scholar 

  59. Sheng, B., Xiao, F., Sha, L., Sun, L.: Deep spatial-temporal model based cross-scene action recognition using commodity WiFi. IEEE Internet Things J. 7(4), 3592–3601 (2020). https://doi.org/10.1109/JIOT.2020.2973272

    Article  Google Scholar 

  60. Liang, D., Thomaz, E.: Audio-based activities of daily living (ADL) recognition with large-scale acoustic embeddings from online videos. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3(1), 1–18 (2019)

    Article  Google Scholar 

  61. Gu, Y., Zhan, J., Ji, Y., Li, J., Ren, F., Gao, S.: MoSense: An RF-based motion detection system via off-the-shelf WiFi devices. IEEE Internet Things J. 4(6), 2326–2341 (2017). https://doi.org/10.1109/JIOT.2017.2754578

    Article  MATH  Google Scholar 

  62. Gilbert, A., Trumble, M., Malleson, C., Hilton, A., Collomosse, J.: Fusing visual and inertial sensors with semantics for 3D human pose estimation. Int. J. Comput. Vis. 127(4), 381–397 (2019). https://doi.org/10.1007/s11263-018-1118-y

    Article  Google Scholar 

  63. Wang, L., Li, Y., Xiong, F., Zhang, W.: Gait recognition using optical motion capture: a decision fusion based method. Sensors 21(10), 3496 (2021). https://doi.org/10.3390/s21103496

    Article  MATH  Google Scholar 

  64. Redkar, S.: A review on wearable inertial tracking based human gait analysis and control strategies of lower-limb exoskeletons. Int. Robot. & Autom. J. (2017). https://doi.org/10.15406/iratj.2017.03.00080

    Article  MATH  Google Scholar 

  65. Shaikh, M.B., Chai, D., Islam, S.M.S., Akhtar, N.: Multimodal fusion for audio-image and video action recognition. Neural Comput. & Applic. 36(10), 5499–5513 (2024). https://doi.org/10.1007/s00521-023-09186-5

    Article  MATH  Google Scholar 

  66. Phutane, U., et al.: Evaluation of optical and radar based motion capturing technologies for characterizing hand movement in rheumatoid arthritis—a pilot study. Sensors 21(4), 1208 (2021). https://doi.org/10.3390/s21041208

    Article  MATH  Google Scholar 

  67. Mears, A., Roberts, J., Wallace, E., Kong, P., Forrester, S.: Comparison of two- and three-dimensional methods for analysis of trunk kinematic variables in the golf swing. J. Appl. Biomech. 32(1), 23–31 (2015). https://doi.org/10.1123/jab.2015-0032

    Article  Google Scholar 

  68. Gurchiek, R.D., et al.: Sprint assessment using machine learning and a wearable accelerometer. J. Appl. Biomech. 35(2), 164–169 (2019). https://doi.org/10.1123/jab.2018-0107

    Article  Google Scholar 

  69. Imsdahl, S.I., et al.: Anteroposterior translational malalignment of ankle arthrodesis alters foot biomechanics in cadaveric gait simulation. J. Orthop. Res. 38(2), 450–458 (2020). https://doi.org/10.1002/jor.24464

    Article  Google Scholar 

  70. Willwacher, S., et al.: The habitual motion path theory: evidence from cartilage volume reductions in the knee joint after 75 minutes of running. Sci. Rep. 10(1), 1363 (2020). https://doi.org/10.1038/s41598-020-58352-5

    Article  MATH  Google Scholar 

  71. Nasr, A., Hashemi, A., McPhee, J.: Scalable musculoskeletal model for dynamic simulations of upper body movement. Comput. Methods Biomech. Biomed. Engin. 27(3), 306–337 (2024). https://doi.org/10.1080/10255842.2023.2184747

    Article  MATH  Google Scholar 

  72. Carabasa García, L., Lorca-Gutiérrez, R., Vicente-Mampel, J., Part-Ferrer, R., Fernández-Ehrling, N., Ferrer-Torregrosa, J.: Relationship between anterior cruciate ligament injury and subtalar pronation in female basketball players: case-control study. J. Clin. Med. 12(24), 7539 (2023). https://doi.org/10.3390/jcm12247539

    Article  Google Scholar 

  73. Valaei Sharif, S., Habibi Moshfegh, P., Kashani, H.: Simulation modeling of operation and coordination of agencies involved in post-disaster response and recovery. Reliab. Eng. & Syst. Saf. 235, 109219 (2023). https://doi.org/10.1016/j.ress.2023.109219

    Article  MATH  Google Scholar 

  74. Di Raimondo, G., et al.: Peak tibiofemoral contact forces estimated using IMU-based approaches are not significantly different from motion capture-based estimations in patients with knee osteoarthritis. Sensors (Basel) 23(9), 4484 (2023). https://doi.org/10.3390/s23094484

    Article  MATH  Google Scholar 

  75. Lavikainen, J., Stenroth, L., Alkjær, T., Karjalainen, P.A., Korhonen, R.K., Mononen, M.E.: Prediction of knee joint compartmental loading maxima utilizing simple subject characteristics and neural networks. Ann. Biomed. Eng. 51(11), 2479–2489 (2023). https://doi.org/10.1007/s10439-023-03278-y

    Article  Google Scholar 

  76. Hribernik, M., Umek, A., Tomažič, S., Kos, A.: Review of real-time biomechanical feedback systems in sport and rehabilitation. Sensors (Basel) 22(8), 3006 (2022). https://doi.org/10.3390/s22083006

    Article  MATH  Google Scholar 

  77. Telfer, S.: Musculoskeletal Modeling of the Foot and Ankle, p. 387. (2022). https://doi.org/10.1016/B978-0-12-815449-6.00021-4

  78. Su, B., Gutierrez-Farewik, E.M.: Simulating human walking: a model-based reinforcement learning approach with musculoskeletal modelling. Front. Neurorobot. 17, (2023). https://doi.org/10.3389/fnbot.2023.1244417

  79. Mathieu, E., Crémoux, S., Duvivier, D., Amarantini, D., Pudlo, P.: Biomechanical modeling for the estimation of muscle forces: toward a common language in biomechanics, medical engineering, and neurosciences. J. NeuroEng. Rehabil. 20(1), 130 (2023). https://doi.org/10.1186/s12984-023-01253-1

    Article  Google Scholar 

  80. Seth, A., et al.: OpenSim: simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. PLoS Comput. Biol. 14(7), e1006223 (2018). https://doi.org/10.1371/journal.pcbi.1006223

    Article  Google Scholar 

  81. McClintock, F.A., Callaway, A.J., Clark, C.J., Williams, J.M.: Validity and reliability of inertial measurement units used to measure motion of the lumbar spine: a systematic review of individuals with and without low back pain. Med. Eng. Phys. 126, 104146 (2024). https://doi.org/10.1016/j.medengphy.2024.104146

    Article  Google Scholar 

  82. Morais, J.E., Oliveira, J.P., Sampaio, T., Barbosa, T.M.: Wearables in swimming for real-time feedback: a systematic review. Sensors 22(10), 3677 (2022). https://doi.org/10.3390/s22103677

    Article  MATH  Google Scholar 

  83. Hohmuth, R., Schwensow, D., Malberg, H., Schmidt, M.: A wireless rowing measurement system for improving the rowing performance of athletes. Sensors 23(3), 1060 (2023). https://doi.org/10.3390/s23031060

    Article  Google Scholar 

  84. Stančin, S., Tomažič, S.: Early improper motion detection in golf swings using wearable motion sensors: the first approach. Sensors 13(6), 7505–7521 (2013). https://doi.org/10.3390/s130607505

    Article  MATH  Google Scholar 

  85. Goebert, C.: Augmented reality in sport marketing: uses and directions. Sports Innov. J. 1, 134–151 (2020). https://doi.org/10.18060/24227

    Article  MATH  Google Scholar 

  86. Li, H.: Research on basketball sports training based on virtual reality technology. J. Phys. Conf. Ser. 1992, 032047 (2021). https://doi.org/10.1088/1742-6596/1992/3/032047

    Article  Google Scholar 

  87. Kim, A., Kim, S.-S.: Engaging in sports via the metaverse? An examination through analysis of metaverse research trends in sports. Data Sci. Manag. (2024). https://doi.org/10.1016/j.dsm.2024.01.002

    Article  MATH  Google Scholar 

  88. Marshall, B., Uiga, L., Parr, J.V.V., Wood, G.: A preliminary investigation into the efficacy of training soccer heading in immersive virtual reality. Virtual Real. 27(3), 2397–2404 (2023). https://doi.org/10.1007/s10055-023-00807-x

    Article  Google Scholar 

  89. He, Q., et al.: From digital human modeling to human digital twin: framework and perspectives in human factors. Chin. J. Mech. Eng. 37(1), 9 (2024). https://doi.org/10.1186/s10033-024-00998-7

    Article  MATH  Google Scholar 

  90. Roupa, I., da Silva, M.R., Marques, F., Gonçalves, S.B., Flores, P., da Silva, M.T.: On the modeling of biomechanical systems for human movement analysis: a narrative review. Arch. Computat. Methods Eng. 29(7), 4915–4958 (2022). https://doi.org/10.1007/s11831-022-09757-0

    Article  MATH  Google Scholar 

  91. Reinschmidt, C., van den Bogert, A.J., Nigg, B.M., Lundberg, A., Murphy, N.: Effect of skin movement on the analysis of skeletal knee joint motion during running. J. Biomech. 30(7), 729–732 (1997). https://doi.org/10.1016/s0021-9290(97)00001-8

    Article  Google Scholar 

  92. Ren, L., Howard, D., Ren, L., Nester, C., Tian, L.: A generic analytical foot rollover model for predicting translational ankle kinematics in gait simulation studies. J. Biomech. 43(2), 194–202 (2010). https://doi.org/10.1016/j.jbiomech.2009.09.027

    Article  MATH  Google Scholar 

  93. Veloso, A., Esteves, G., Silva, S., Ferreira, C., Brandão, F.: Biomechanics modeling of human musculoskeleial system using adams multibody dynamics package. 2006, 401–407 (2006)

  94. Chao, E.Y., Armiger, R.S., Yoshida, H., Lim, J., Haraguchi, N.: Virtual interactive musculoskeletal system (VIMS) in orthopaedic research, education and clinical patient care. J. Orthop. Surg. Res. 2(1), 2 (2007). https://doi.org/10.1186/1749-799X-2-2

    Article  Google Scholar 

  95. He, K., Zuo, C., Shao, J., Sui, Y.: Self model for embodied intelligence: modeling full-body human musculoskeletal system and locomotion control with hierarchical low-dimensional representation. arXiv. (Dec. 09, 2023). https://doi.org/10.48550/arXiv.2312.05473

  96. Koga, H., et al.: Mechanisms for noncontact anterior cruciate ligament injuries knee joint kinematics in 10 injury situations from female team handball and basketball. Am. J. Sports Med. 38, 2218–2225 (2010). https://doi.org/10.1177/0363546510373570

    Article  MATH  Google Scholar 

  97. Yang, J., Meng, C., Ling, L.: Prediction and simulation of wearable sensor devices for sports injury prevention based on BP neural network. Meas.: Sens. 33, 101104 (2024). https://doi.org/10.1016/j.measen.2024.101104

    Article  MATH  Google Scholar 

  98. Strojny, P., Dużmańska-Misiarczyk, N.: Measuring the effectiveness of virtual training: a systematic review. Comput. & Educ.: X Real. 2, 100006 (2023). https://doi.org/10.1016/j.cexr.2022.100006

    Article  MATH  Google Scholar 

  99. Nozawa, T., Wu, E., Koike, H.: VR ski coach: indoor ski training system visualizing difference from leading skier. In: 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pp. 1341–1342. IEEE, Osaka, Japan. (Mar. 2019). https://doi.org/10.1109/VR.2019.8797717

  100. Okada, Y., et al.: Virtual ski training system that allows beginners to acquire ski skills based on physical and visual feedbacks, pp. 1268–1275. (Oct. 2023). https://doi.org/10.1109/IROS55552.2023.10342020

  101. Chatzopoulos, D., Bermejo, C., Huang, Z., Hui, P.: Mobile augmented reality survey: from where we are to where we go. IEEE Access 5, 6917–6950 (2017). https://doi.org/10.1109/ACCESS.2017.2698164

    Article  Google Scholar 

  102. Musse, S.R., Thalmann, D.: Hierarchical model for real time simulation of virtual human crowds. IEEE Trans. Visual Comput. Gr. 7(2), 152–164 (2001). https://doi.org/10.1109/2945.928167

    Article  MATH  Google Scholar 

  103. Capasa, L., Zulauf, K., Wagner, R.: Virtual reality experience of mega sports events: a technology acceptance study. J. Theor. Appl. Electron. Commer. Res. 17(2), 686–703 (2022). https://doi.org/10.3390/jtaer17020036

    Article  MATH  Google Scholar 

  104. Bulearca, M., Tamarjan, D.: Augmented reality: a sustainable marketing tool? Glob. Bus. Manag. Res. 2, 237–252 (2010)

    Google Scholar 

  105. Tanier, M.: Future of the NFL: the virtual, augmented, 3D, 360-degree football experience. Bleach. Rep. Accessed: Mar. 14, 2024. [Online]. Available: https://bleacherreport.com/articles/2659861-future-of-the-nfl-the-virtual-augmented-3d-360-degree-football-experience

  106. Shimizu, K., Sugawara, K.: Validation of potential reference measure for indoor walking distance to evaluate wearable sensing devices. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 7178–7181. (Nov. 2021). https://doi.org/10.1109/EMBC46164.2021.9629854

  107. Tretschk, E., et al.: State of the art in dense monocular non-rigid 3D reconstruction. Comput. Gr. Forum 42(2), 485–520 (2023). https://doi.org/10.1111/cgf.14774

    Article  MATH  Google Scholar 

  108. Qian, B., et al.: DRAC 2022: a public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images. Patterns, p. 100929. (Feb. 2024). https://doi.org/10.1016/j.patter.2024.100929

  109. Lugrís, U., Pérez-Soto, M., Michaud, F., Cuadrado, J.: Human motion capture, reconstruction, and musculoskeletal analysis in real time. Multibody Syst. Dyn. 60(1), 3–25 (2024). https://doi.org/10.1007/s11044-023-09938-0

    Article  MathSciNet  MATH  Google Scholar 

  110. Desmarais, Y., Mottet, D., Slangen, P., Montesinos, P.: A review of 3D human pose estimation algorithms for markerless motion capture. arXiv. (Jul. 12, 2021). Accessed: May 17, 2022. [Online]. Available: http://arxiv.org/abs/2010.06449

  111. Karambakhsh, A., et al.: SparseVoxNet: 3-D object recognition with sparsely aggregation of 3-D dense blocks. IEEE Trans. Neural Netw. Learn. Syst. 35(1), 532–546 (2024). https://doi.org/10.1109/TNNLS.2022.3175775

    Article  Google Scholar 

  112. Hu, P., Ho, E.S., Munteanu, A.: 3DBodyNet: fast reconstruction of 3D animatable human body shape from a single commodity depth camera. IEEE Trans. Multimedia 24, 2139–2149 (2022). https://doi.org/10.1109/TMM.2021.3076340

    Article  MATH  Google Scholar 

  113. Aouaidjia, K., Sheng, B., Li, P., Kim, J., Feng, D.D.: Efficient body motion quantification and similarity evaluation using 3-D joints skeleton coordinates. IEEE Trans. Syst. Man Cybern.: Syst. 51(5), 2774–2788 (2021). https://doi.org/10.1109/TSMC.2019.2916896

    Article  MATH  Google Scholar 

  114. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multi-person linear model. In: Seminal Graphics Papers: Pushing the Boundaries, 1st ed., Volume 2, pp. 851–866. Association for Computing Machinery, New York, NY, USA. (2023). Accessed: Mar. 14, 2024. [Online]. Available: https://doi.org/10.1145/3596711.3596800

  115. Urtasun, R., Glardon, P., Boulic, R., Thalmann, D., Fua, P.: Style-based motion synthesis†. Comput. Gr. Forum 23(4), 799–812 (2004). https://doi.org/10.1111/j.1467-8659.2004.00809.x

    Article  MATH  Google Scholar 

  116. Joo, H., Simon, T., Sheikh, Y.: Total capture: a 3D deformation model for tracking faces, hands, and bodies. arXiv, (Jan. 04, 2018). https://doi.org/10.48550/arXiv.1801.01615

  117. Debevec, P., Hawkins, T., Tchou, C., Duiker, H.-P., Sarokin, W., Sagar, M.: Acquiring the reflectance field of a human face. In: Proceedings of the 27th annual conference on Computer graphics and interactive techniques, in SIGGRAPH ’00, pp. 145–156. ACM Press/Addison-Wesley Publishing Co., USA. (Jul. 2000). https://doi.org/10.1145/344779.344855

  118. Liang, H., Yuan, J., Thalmann, D.: Parsing the hand in depth images. IEEE Trans. Multimed. 16(5), 1241–1253 (2014). https://doi.org/10.1109/TMM.2014.2306177

    Article  MATH  Google Scholar 

  119. Nazir, A., Cheema, M.N., Sheng, B., Li, P., Kim, J., Lee, T.-Y.: Living donor-recipient pair matching for liver transplant via ternary tree representation with cascade incremental learning. IEEE Trans. Biomed. Eng. 68(8), 2540–2551 (2021). https://doi.org/10.1109/TBME.2021.3050310

    Article  Google Scholar 

  120. Alldieck, T., Magnor, M., Xu, W., Theobalt, C., Pons-Moll, G.: Detailed human avatars from monocular video. arXiv, (Aug. 03, 2018). https://doi.org/10.48550/arXiv.1808.01338

  121. Zhu, H., Zuo, X., Wang, S., Cao, X., Yang, R.: Detailed human shape estimation from a single image by hierarchical mesh deformation. arXiv, (May 08, 2019). https://doi.org/10.48550/arXiv.1904.10506

  122. Pons-Moll, G., Romero, J., Mahmood, N., Black, M.J.: Dyna: a model of dynamic human shape in motion. ACM Trans. Gr. 34(4), 1–14 (2015). https://doi.org/10.1145/2766993

    Article  MATH  Google Scholar 

  123. Huang, S., et al.: TransMRSR: transformer-based self-distilled generative prior for brain MRI super-resolution. arXiv. (Jun. 11, 2023). https://doi.org/10.48550/arXiv.2306.06669

  124. Vlasic, D., et al.: Dynamic shape capture using multi-view photometric stereo. In: ACM SIGGRAPH Asia 2009 papers, in SIGGRAPH Asia ’09, pp. 1–11. Association for Computing Machinery. New York, NY, USA. (2009). https://doi.org/10.1145/1661412.1618520

  125. Xie, K., Wang, T., Iqbal, U., Guo, Y., Fidler, S., Shkurti, F.: Physics-based human motion estimation and synthesis from videos. arXiv, (Aug. 11, 2022). https://doi.org/10.48550/arXiv.2109.09913

  126. Habermann, M., Xu, W., Rhodin, H., Zollhoefer, M., Pons-Moll, G., Theobalt, C.: NRST: non-rigid surface tracking from monocular video. arXiv, (Jul. 12, 2021). https://doi.org/10.48550/arXiv.2107.02407

  127. Pueo, B., Jimenez-Olmedo, J.M.: Application of motion capture technology for sport performance analysis (El uso de la tecnología de captura de movimiento para el análisis del rendimiento deportivo). Retos 32, 241–247 (2017). https://doi.org/10.47197/retos.v0i32.56072

    Article  Google Scholar 

  128. Tonkin, E.L., et al.: A multi-sensor dataset with annotated activities of daily living recorded in a residential setting. Sci. Data 10(1), 162 (2023). https://doi.org/10.1038/s41597-023-02017-1

    Article  MATH  Google Scholar 

  129. Abdel-Malek, K., et al.: Digital human method and simulation for predicting musculoskeletal injuries. (Jun. 2016)

  130. Naumann, A., Roetting, M.: Digital human modeling for design and evaluation of human-machine systems. MMI Interaktiv. (Jan. 2007)

  131. Thewlis, D., Bishop, C., Daniell, N., Paul, G.: Next-generation low-cost motion capture systems can provide comparable spatial accuracy to high-end systems. J. Appl. Biomech. 29, 112–117 (2012). https://doi.org/10.1123/jab.29.1.112

    Article  Google Scholar 

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Funding

National Natural Science Foundation of China (grant number: 62077037) and Research and Innovation Grant for Graduate Students, Shanghai University of Sport (Project Number: YJSCX-2023-034).

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  1. Shanghai University of Sport, Shanghai, 200438, China

    Xiang Suo, Weidi Tang, Lijuan Mao & Zhen Li

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  1. Xiang Suo
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X.S. and W.T. wrote the main manuscript text and prepared the figures and tables. Z.L. and L.M. contributed to supervision and project administration. All authors reviewed the manuscript.

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Suo, X., Tang, W., Mao, L. et al. Digital human and embodied intelligence for sports science: advancements, opportunities and prospects. Vis Comput 41, 2477–2493 (2025). https://doi.org/10.1007/s00371-024-03547-4

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