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  1. 1

    Error assessment of a three-dimensional underwater motion capture methodology

    Thompson, I. M., Audot, D. A. G., Warner, M. B., Banks, J., Logan, O., Hudson, D.
    Published in Sports Biomechanics (2025)
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  2. 2

    Smartphone apps

    Düking, P., Sperlich, B.
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  3. 3

    Inter-trial variability is higher in 3D markerless compared to marker-based motion capture: Implications for data post-processing and analysis

    Horsak, B., Prock, K., Krondorfer, P., Siragy, T., Simonlehner, M., Dumphart, B.
    Published in Journal of Biomechanics (2024)
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  4. 4

    `I like to run to feel`: embodiment and wearable mobile tracking devices in distance running

    Toner, J., Allen-Collinson, J., Jackman, P. C., Jones, L., Addrison, J.
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  5. 5

    Development of a comprehensive method for musculoskeletal simulation in swimming using motion capture data

    Nakashima, M., Kanie, R., Shimana, T., Matsuda, Y., Kubo, Y.
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  6. 6

    Data analyses with Smart Paddles

    Vitazka, M.
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  7. 7

    Sprint acceleration mechanical outputs derived from position- or velocity-time data: a multi-system comparison study

    Fornasier-Santos, C., Arnould, A., Jusseaume, J., Millot, B., Guilhem, G., Couturier, A., Samozino, P., Slawinski, J., Morin, J.-B.
    Published in Sensors (2022)
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  8. 8

    Competing in hot conditions at the Tokyo Olympic Games: Preparation strategies used by Australian race walkers

    Carr, A. J., Vallance, B. S., Rothwell, J., Rea, A. E., Burke, L. M., Guy, J. H.
    Published in Frontiers in Physiology (2022)
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  9. 9

    Kinematic pose rectification for performance analysis and retrieval in sports

    Zecha, D., Einfalt, M., Eggert, C., Lienhart, R.
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  10. 10

    Refining joint locations for human pose tracking in sports videos

    Zecha, D., Einfalt, M., Lienhart, R.
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  11. 11

    Complementary use of wearable technology 3: temporal alignment and similarity measurement of accelerometer signals from two IMU devices

    Williams, J. H., Rizzuto, S. F., Dillard, B. A., Whitaker, E. J.
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  12. 12

    Complementary use of wearable technology 4: assessing gait asymmetry and shock attenuation using multiple IMU devices

    Williams, J. H., Rizzuto, S. F., Dillard, B. A., Whitaker, E. J.
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  13. 13

    ST-DeepGait: a spatiotemporal deep learning model for human gait recognition

    Konz, L., Hill, A., Banaei-Kashani, F.
    Published in Sensors (2022)
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  14. 14

    Inertial sensor estimation of initial and terminal contact during in-field running

    Yang, Y., Wang, L., Su, S., Watsford, M., Wood, L. M., Duffield, R.
    Published in Sensors (2022)
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  15. 15

    Accuracy and precision of wearable devices for real-time monitoring of swimming athletes

    Cosoli, G., Antognoli, L., Veroli, V., Scalise, L.
    Published in Sensors (2022)
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  16. 16

    SmartSwim, a novel IMU-based coaching assistance

    Hamidi Rad, M., Gremetaux, V., Massé, F., Dadashi, F., Aminian, K.
    Published in Sensors (2022)
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  17. 17

    The reliability and accuracy of Polar Team Pro GPS units

    Akyildiz, Z., Yildiz, M., Clemente, F. M.
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  18. 18

    Absolute reliability and validity of the OptoGaitTM system to measure spatiotemporal gait parameters during running

    Garcia-Pinillos, F., Latorre-Román, P. A., Chicano-Gutiérrez, J. M., Ruiz-Malagón, E. J., Párraga-Montilla, J. A., Roche-Seruendo, L. E.
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  19. 19

    Is additive manufacturing improving performance in Sports? A systematic review

    Novak, J. I., Novak, A. R.
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  20. 20

    The application mode of athletes in the calculation of cloud motion capture technology in football

    Hu, X.
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