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  1. 1

    Sensor input type and location influence outdoor running terrain classification via deep learning approaches

    Thibault, G., Dixon, P. C., Pearsall, D. J.
    Published in Sensors (2025)
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  2. 2

    Tribological devices in cycling: A review

    Aubert, R., Roizard, X., Grappe, F., Lallemand, F. “…ranking…”
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  3. 3

    Investigation of supervised machine learning for IMU-based swimming activity recognition

    Aram, S., Thompson, I. M., Hudson, D. A., Banks, J. “…ranking…”
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  4. 4

    Is the classification of athletes based on force-velocity profiling from countermovement jumps influenced by the choice of method?

    Paulsen, G., Lindberg, K., Eriksrud, O., Solberg, P., Bjørnsen, T., Seynnes, O., Gløersen-Haga, Ø., Bobbert, M., Rice, H. “…ranking…”
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  5. 5

    Exercise classification in resistance training: a systematic review of technological approaches

    Brennan, T. R., Weakley, J., Johnston, R. D., Creaby, M. W.
    Published in Sports Medicine (2025)
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  6. 6

    Will artificial intelligence solve the riddle of athlete development? A critical review of how AI is being used for athlete identification, selection, and development

    Baker, J., Cattle, A., McAuley, A., Kelly, A., Johnston, K.
    Published in Psychology of Sport and Exercise (2025)
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  7. 7

    Boxing punch detection and classification using motion tape and machine learning

    Huang, S.-C., Pierce, T., Lin, Y.-A., Loh, K. J.
    Published in Sensors (2025)
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  8. 8

    Automated rowing event assignment: a machine learning approach

    Li, Y., Koldenhoven, R. M., Jiwan, N. C., Zhan, J., Liu, T.
    Published in Sports Biomechanics (2025)
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  9. 9

    Efficient markerless motion classification using radar

    Eom, C., Han, S., Chun, S., Joo, S., Yoon, J., Kim, M., Park, J., Park, S.
    Published in Sensors (2025)
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  10. 10

    Rugby scene classification enhanced by vision language model

    Nonaka, N., Fujihira, R., Koshiba, T., Maeda, A., Seita, J. “…ranking…”
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  11. 11

    The classification of technical devices in the training process of athletes playing games

    Guba V. P., Rodin A. V., Skripko A. D., Pleshakov A. A. “…ranking…”
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  12. 12

    SkateboardAI: The coolest video action recognition for skateboarding (student abstract)

    Chen, H. “…ranking…”
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  13. 13

    A comparative study of plantar pressure and inertial sensors for cross-country ski classification using deep learning

    Polo-Rodríguez, A., Escobedo, P., Martínez-Martí, F., Marcen-Cinca, N., Carvajal, M. A., Medina-Quero, J., Martínez-García, M. S.
    Published in Sensors (2025)
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  14. 14

    Deep learning with 3D ResNets for comprehensive dual-lane speed climbing video analysis

    Xie, Y., Mariano, V. “…ranking…”
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  15. 15

    Simulation study for the motion classification using the radar and the 3D motion analysis system

    Eom, C. H., Chun, S. B., Kim, B. S., Joo, S. Y., You, J. H., Yoon, J. S., Han, S. J., Park, J. C., Park, S. H. “…ranking…”
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  16. 16

    Smart Truck - Wie Tricks im Skateboarden durch Machine Learning klassifizieren kann (Smart Truck - How machine learning can classify tricks in skateboarding)

    Westenberger, E., Kreiner, J., Freitas, G., Fresen, L., Hollaus, B. “…ranking…”
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  17. 17

    Cross-country ski skating style sub-technique detection and skiing characteristic analysis on snow using high-precision GNSS

    Uda, S., Miyamoto, N., Hirose, K., Nakano, H., Stöggl, T., Linnamo, V., Lindinger, S., Takeda, M.
    Published in Sensors (2024)
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  18. 18

    Gear classification in skating cross-country skiing using inertial sensors and deep learning

    Pousibet-Garrido, A., Polo-Rodríguez, A., Moreno-Pérez, J. A., Ruiz-García, I., Escobedo, P., López-Ruiz, N., Marcen-Cinca, N., Medina-Quero, J., Carvajal, M. Á.
    Published in Sensors (2024)
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  19. 19

    Classification of force-time metrics into lower-body strength domains

    Geneau, M. C., Carey, D. L., Gastin, P. B., Robertson, S., James, L. P. “…ranking…”
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  20. 20

    Using multiple machine learning algorithms to classify distinguishing characteristics between elite defenders and their sub-elite counterparts in professional men`s football

    Phatak, A., Schlenger, J., Jamil, M., Mehta, S., Biermann, H., Memmert, D. “…ranking…”
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