Search Results - Äyrämö, S.

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  1. 1

    Global positioning system-derived metrics and machine learning models for injury prediction in professional rugby union players

    Ren, X., Boisbluche, S., Philippe, K., Demy, M., Äyrämö, S., Rautiainen, I., Ding, S., Prioux, J.
    Published in European Journal of Sport Science (2025)
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  2. 2

    Machine learning in rugby union: predicting and identifying key performance indicators for professional rugby union players in match play based workload

    Ren, X., Boisbluche, S., Philippe, K., Demy, M., Äyrämö, S., Rautiainen, I., Ding, S., Prioux, J.
    Published in European Journal of Sport Science (2025)
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  3. 3

    Quantification of workload and characterisation of key performance factors in elite adolescent female volleyball players using machine learning

    Bouzigues, T., Candau, R., Äyrämö, S., Maurelli, O., Prioux, J.
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  4. 4

    Balance perturbations as a measurement tool for trunk impairment in cross-country sit skiing

    Rosso, V., Gastaldi, L., Rapp, W., Lindinger, S., Vanlandewijck, Y., Äyrämö, S., Linnamo, V.
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  5. 5

    Cross-cultural adaptation and validation of the Kerlan-Jobe Orthopaedic Clinic shoulder and elbow score in Finnish-speaking overhead athletes

    Sukanen, M., Pajari, J., Äyrämö, S., Paloneva, J., Waller, B., Häkkinen, A., Multanen, J.
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  6. 6

    Predicting ACL injury using machine learning on data from an extensive screening test battery of 880 female elite athletes

    Jauhiainen, S., Kauppi, J.-P., Krosshaug, T., Bahr, R., Bartsch, J., Äyrämö, S.
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  7. 7

    Change of direction biomechanics in a 180-degree pivot turn and the risk for noncontact knee injuries in youth basketball and floorball players

    Leppänen, M., Parkkari, J., Vasankari, T., Äyrämö, S., Kulmala, J.-P., Krosshaug, T., Kannus, P., Pasanen, K.
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  8. 8

    Altered hip control during a standing knee-lift test is associated with increased risk of knee injuries

    Leppänen, M., Rossi, M. T., Parkkari, J., Heinonen, A., Äyrämö, S., Krosshaug, T., Vasankari, T., Kannus, P., Pasanen, K.
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  9. 9

    New machine learning approach for detection of injury risk factors in young team sport athletes

    Jauhiainen, S., Kauppi, J.-P., Leppänen, M., Pasanen, K., Parkkari, J., Vasankari, T., Kannus, P., Äyrämö, S.
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  10. 10

    Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity

    Girka, A., Kulmala, J.-P., Äyrämö, S.
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  11. 11

    Talent identification in soccer using a one-class support vector machine

    Jauhiainen, S., Äyrämö, S., Forsman, H., Kauppi, J.-P.
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  12. 12

    Prediction of active peak force using a multilayer perceptron

    Niemelä, M., Kulmala, J. P., Kauppi, J. P., Kosonen, J., Äyrämö, S.
    Published in Sports Engineering (2017)
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  13. 13

    Stiff landings are associated with increased ACL injury risk in young female basketball and floorball players

    Leppänen, M., Pasanen, K., Kujala, U. M., Vasankari, T., Kannus, P., Äyrämö, S., Krosshaug, T., Bahr, R., Avela, J., Perttunen, J., Parkkari, J.
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  14. 14

    Oxygen uptake, acid-base balance and anaerobic energy system contribution in maximal 300 - 400 m running in child, adolescent and adult athletes

    Vilmi, N., Äyrämö, S., Nummela, A., Pullinen, T., Linnamo, V., Häkkinen, K., Mero, A. A.
    Published in Journal of Athletic Enhancement (2016)
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  15. 15

    Monitoring training adaptation with a submaximal running test under field conditions

    Vesterinen, V., Nummela, A., Äyrämö, S., Laine, T., Hynynen, E., Mikkola, J., Häkkinen, K.
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