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  1. 1

    The influence of sample and filter frequencies on baseball pitching data

    Dowling, B., Slowik, J. S., Fleisig, G. S.
    Published in Sports Biomechanics (2026)
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  2. 2

    Velocity-based strength training: the validity and personal monitoring of barbell velocity with the Apple Watch

    Achermann, B., Oberhofer, K., Ferguson, S. J., Lorenzetti, S. R.
    Published in Sports (2023)
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  3. 3

    Establishing reliable acceleration-speed profiles: Minimum data requirements in rugby union matches

    Maviel, C., Couderc, A., Duché, P., Morin, J. B., Vercruyssen, F.
    Published in Journal of Sports Sciences (2024)
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  4. 4

    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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  5. 5

    Kubios threshold-based artefact correction affects heart rate variability parameters in elite athletes

    Itaborahy, A., Freire, R., Hausen, M.
    article
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  6. 6

    ArcheryVis: A tool for analyzing and visualizing archery performance data

    Cheng, Z., Li, Z., Luo, Z., Liu, M., D`Alonzo, J., Wang, C.
    Published in Advances in Visual Computing (2023)
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  7. 7

    Deep learning-based 2D keypoint detection in alpine ski racing - A performance analysis of state-of-the-art algorithms applied to regular skiing and injury situations

    Zwölfer, M., Heinrich, D., Schindelwig, K., Wandt, B., Rhodin, H., Spörri, J., Nachbauer, W.
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  8. 8

    Profiling and benchmarking

    McGuigan, M.
    book
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  9. 9

    Data hygiene

    Varley, M. C., Lovell, R., Carey, D.
    book
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  10. 10

    Aerodynamic benefits of drafting in speed skating: Estimates from in-field skater's wakes and wind tunnel measurements

    Terra, W., Spoelstra, A., Sciacchitano, A.
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  11. 11

    Large scale, long-term, high granularity measurement of active travel using smartphones apps

    Heller, B. W., Mazumdar, S., Ciravegna, F.
    Published in Proceedings (2018)
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  12. 12

    Measuring and classifying land-based and water-based daily living activities using inertial sensors

    Kaneda, K., Ohgi, Y., McKean, M. R., Burkett, B. J.
    Published in Proceedings (2018)
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  13. 13

    External load of Flamenco Zap-3 footwork test: Use of PlayerLoad Concept with triaxial accelerometry

    Zhang, N., Gómez-Lozano, S., Armstrong, R., Liu, H., Vargas-Macías, A.
    Published in Sensors (2022)
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  14. 14

    A comparison of a novel stretchable smart patch for measuring runner`s step rates with existing measuring technologies

    Verdel, N., Drobnic, M., Maslik, J., Rahimi, K. B., Tantillo, G., Gumiero, A., Hjort, K., Holmberg, H. C., Supej, M.
    Published in Sensors (2022)
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  15. 15

    An end-to-end deep learning pipeline for football activity recognition based on wearable acceleration sensors

    Cuperman, R., Jansen, K. M. B., Ciszewski, M. G.
    Published in Sensors (2022)
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  16. 16

    Design and analysis of electronic head protector for taekwondo sports

    Qureshi, F., Krishnan, S.
    Published in Sensors (2022)
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  17. 17

    Temporal orders and causal vector for physiological data analysis

    Mlynczak, M.
    congress proceedings
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  18. 18

    Research on balance control of freestyle skiing aerial skills based on ant colony algorithm

    Jun, W., Song, W., Li, Z.
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  19. 19

    Assessing group-based changes in high-performance sport. Part 1: null hypothesis significance testing and the utility of p values

    Turner, A. N., Parmar, N., Jovanovski, A., Hearne, G.
    Published in Strength and Conditioning Journal (2021)
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  20. 20

    Open Data im sportwissenschaftlichen Anwendungsfeld motorischer Tests. Eine Analyse zur Relevanz und Akzeptanz der eResearch-Infrastruktur MO|RE data für Motorikforschungsdaten (Open data in the motor test research field. Analysing the relevance and acceptance of the eResearch-infrastructure MO|RE data for motor research data)

    Kloe, M., Niessner, C., Woll, A., Bös, K.
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