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  1. 1

    A vision-based archery training aid system for real-time body form analysis

    Aiman, M. H., Arshad, N. W., Erizal, L. R., Hamid, R., Karim, R. A., Rasli, M. A. M., Majid, M. K. B. A.
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  2. 2

    Computer vision-based archery optics

    Raj, A.
    Published in Augmented Human Research (2025)
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  3. 3

    Quantitative analysis and evaluation of research on the application of computer vision in sports since the 21st century

    Cheng, C., Xue, J., Gou, W., Xie, M.
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  4. 4

    An introduction to performance analysis in sport

    Cullinane, A., Davies, G., O'Donoghue, P.
    Published 2024
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  5. 5

    The effectiveness of the training process of skiers using computer vision methods

    Novikova, N. B., Novikov, A. N., Ivanova, I. G., Belyova, A. N.
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  6. 6

    Table tennis coaching system based on a multimodal large language model with a table tennis knowledge base

    Ma, W., Liu, Y., Yi, Q., Liu, X., Xing, W., Zhao, R., Liu, H., Li, R.
    Published in PLOS ONE (2025)
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  7. 7

    Accessibility of motion capture as a tool for sports performance enhancement for beginner and intermediate cricket players

    Maduwantha, K., Jayaweerage, I., Kumarasinghe, C., Lakpriya, N., Madushan, T., Tharanga, D., Wijethunga, M., Induranga, A., Gunawardana, N., Weerakkody, P., Koswattage, K.
    Published in Sensors (2024)
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  8. 8

    Development of a low-cost markerless optical motion capture system for gait analysis and anthropometric parameter quantification

    Espitia-Mora, L. A., Vélez-Guerrero, M. A., Callejas-Cuervo, M.
    Published in Sensors (2024)
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  9. 9

    Masked autoencoder pretraining for event classification in elite soccer

    Rudolph, Y., Brefeld, U.
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  10. 10

    Emotion prediction through eye tracking in affective computing systems

    Edinger, J., Heck, M., Becker, C.
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  11. 11

    MOPED: an agent-based model for peloton dynamics in competitive cycling

    Martins Ratamero, E.
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  12. 12

    Training process modeling of hammer throwers taking into account peculiarities of stress adaptation

    Bolotin, A., Bakayev, V.
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  13. 13

    XR programmers give their perspective on how XR technology can be effectively utilized in high-performance sport.

    Le Noury, P. J., Polman, R. C., Maloney, M. A.
    Published in Sports Medicine - Open (2023)
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  14. 14

    Bayesian inference of the impulse-response model of athlete training and performance

    Peng, K., Brodie, R. T., Swartz, T. B., Clarke, D. C.
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  15. 15

    Automated service height fault detection using computer vision and machine learning for badminton matches

    Goh, G. L., Goh, G. D., Pan, J. W., Teng, P. S. P., Kong, P. W.
    Published in Sensors (2023)
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  16. 16

    Action quality assessment model using specialists' gaze location and kinematics data-focusing on evaluating figure skating jumps

    Hirosawa, S., Kato, T., Yamashita, T., Aoki, Y.
    Published in Sensors (2023)
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  17. 17

    Computer simulation of mechanisms to reduce the metabolic costs of running while taking into account the individual characteristics of the athlete

    Shestakov, M, Korchagin, A.
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  18. 18

    The influence of modeling parameters for computing joint kinetics in adolescent and adult pitchers

    Trunt, A., MacFadden, L. N.
    Published in Sports Biomechanics (2025)
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  19. 19

    Individual locating of soccer players from a single moving view

    Maglo, A., Orcesi, A., Denize, J., Pham, Q. C.
    Published in Sensors (2023)
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  20. 20

    Climbing technique evaluation by means of skeleton video stream analysis

    Beltrán Beltrán, R., Richter, J., Köstermeyer, G., Heinkel, U.
    Published in Sensors (2023)
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