Institute of
Health Sciences

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Philipp Krondorfer MSc

  • Junior Researcher
    Center for Digital Health and Social Innovation
Location: B - Campus-Platz 1

Publications

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Krondorfer, P., Slijepčević, D., Unglaube, F., Kranzl, A., Zeppelzauer, M., Kainz, H., & Horsak, B. (2024). Predicting knee contact forces in walking: A comparative study of machine learning models including a physics-informed approach. Gait & Posture, 113, 125–126. https://doi.org/10.1016/j.gaitpost.2024.07.140
Slijepcevic, D., Krondorfer, P., Unglaube, F., Kranzl, A., Zeppelzauer, M., & Horsak, B. (2024). Predicting ground reaction forces in overground walking from gait kinematics using machine learning. Gait & Posture, 113, 214–215. https://doi.org/10.1016/j.gaitpost.2024.07.231
Horsak, B., Durstberger, S., Krondorfer, P., Thajer, A., Greber-Platzer, S., & Kranzl, A. (2024). Which method should we use to determine the hip joint center location in individuals with a high amount of soft tissue? Clinical Biomechanics, 0(0). https://doi.org/10.1016/j.clinbiomech.2024.106254
Horsak, B., Prock, K., Krondorfer, P., Siragy, T., Simonlehner, M., & Dumphart, B. (2024). Inter-trial variability is higher in 3D markerless compared to marker-based motion capture: Implications for data post-processing and analysis. Journal of Biomechanics, 112049. https://doi.org/10.1016/j.jbiomech.2024.112049
Horsak, B., Eichmann, A., Lauer, K., Prock, K., Krondorfer, P., Siragy, T., & Dumphart, B. (2023). Concurrent validity of smartphone-based markerless motion capturing to quantify lower-limb joint kinematics in healthy and pathological gait. Journal of Biomechanics, 159, 111801. https://doi.org/10.1016/j.jbiomech.2023.111801