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2024
Armand, S., Sawacha, Z., Goudriaan, M., Horsak, B., van der Krogt, M., Huenaerts, C., Daly, C., Kranzl, A., Boehm, H., Petrarca, M., Guiotto, A., Merlo, A., Spolaor, F., Campanini, I., Cosma, M., Hallemans, A., Horemans, H., Gasq, D., Moissenet, F., … Sangeux, M. (2024). Current practices in clinical gait analysis in Europe: A comprehensive survey-based study from the European society for movement analysis in adults and children (ESMAC) standard initiative. Gait & Posture, 111, 65–74. https://doi.org/10.1016/j.gaitpost.2024.04.014
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., 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
Totorean, A., Lancere, L., Horsak, B., Simonlehner, M., Stoia, D. I., Crisan-Vida, M., Moco, D., Fernandes, R., Gere, A., Sterckx, Y., Zulkarnain, A., Gal-Nadasan, N., & Stoia, A. (2024). Heart Rate and Surface Electromyography Analysis to Assess Physical Activity Using a Virtual-Reality Exergame. In N. Herisanu & V. Marinca (Eds.), Acoustics and Vibration of Mechanical Structures—AVMS-2023 (pp. 139–146). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-48087-4_15
2023
de Jesus Oliveira, V. A., Slijepčević, D., Dumphart, B., Ferstl, S., Reis, J., Raberger, A.-M., Heller, M., Horsak, B., & Iber, M. (2023). Auditory feedback in tele-rehabilitation based on automated gait classification. Personal and Ubiquitous Computing. https://doi.org/10.1007/s00779-023-01723-2
Dumphart, B., Slijepcevic, D., Zeppelzauer, M., Kranzl, A., Unglaube, F., Baca, A., & Horsak, B. (2023). Robust deep learning-based gait event detection across various pathologies. PLOS ONE, 18(8), e0288555. https://doi.org/10.1371/journal.pone.0288555
Dumphart, B., Slijepcevic, D., Kranz, A., Zeppelzauer, M., & Horsak, B. (2023). Is it time to re-think the appropriateness of autocorrelation for gait event detection? Preliminary results of an ongoing study. Gait & Posture, 106, S50–S51. https://doi.org/10.1016/j.gaitpost.2023.07.064
Durstberger, S., Kranzl, A., & Horsak, B. (2023). Effects of three different regression-based hip joint center localization methods in adolescents with obesity on kinematics and kinetics - preliminary results of the HIPstar study. Gait & Posture, 100, 42–43. https://doi.org/10.1016/j.gaitpost.2022.11.056
Guggenberger, B., Horsak, B., Habersack, A., Smith, C., Svehlik, M., & Kainz, H. (2023). Internal lower limb rotation increases patella cartilage pressure in individuals with patellofemoral instability. Gait & Posture, 106, S71–S72. https://doi.org/10.1016/j.gaitpost.2023.07.088
Guggenberger, B., Horsak, B., Habersack, A., Smith, C. R., Kainz, H., & Svehlik, M. (2023). Different walking strategies impact patella cartilage pressure in individuals with patellofemoral instability. Gait & Posture, 100, 9–10. https://doi.org/10.1016/j.gaitpost.2022.11.025
Holder, J., Stief, F., van Drongelen, S., & Horsak, B. (2023). A comparative analysis of kinematic simulation results obtained by manually and automated scaled OpenSim models during walking – preliminary findings. Gait & Posture, 106, S80–S82. https://doi.org/10.1016/j.gaitpost.2023.07.099
Horsak, B., Simonlehner, M., Dumphart, B., & Siragy, T. (2023). Overground walking while using a virtual reality head mounted display increases variability in trunk kinematics and reduces dynamic balance in young adults. Virtual Reality. https://doi.org/10.1007/s10055-023-00851-7
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
Horsak, B., Eichmann, A., Lauer-Maier, K., Prock, K., & Dumphart, B. (2023). Concurrent assessment of a smartphone-based markerless and marker-based motion capture system in pathological gait. Gait & Posture, 106, S79–S80. https://doi.org/10.1016/j.gaitpost.2023.07.098
Horst, F., Slijepcevic, D., Simak, M., Horsak, B., Schöllhorn, W. I., & Zeppelzauer, M. (2023). Modeling biological individuality using machine learning: A study on human gait. Computational and Structural Biotechnology Journal, 21, 3414–3423. https://doi.org/10.1016/j.csbj.2023.06.009
Neubauer, M., Moser, L., Neugebauer, J., Raudner, M., Wondrasch, B., Führer, M., Emprechtinger, R., Dammerer, D., Ljuhar, R., Salzlechner, C., & Nehrer, S. (2023). Artificial-Intelligence-Aided Radiographic Diagnostic of Knee Osteoarthritis Leads to a Higher Association of Clinical Findings with Diagnostic Ratings. Journal of Clinical Medicine, 12(3), 744. https://doi.org/10.3390/jcm12030744
Siragy, T., Russo, Y., Young, W., & Lamb, S. E. (2023). Comparison of over-ground and treadmill perturbations for simulation of real-world slips and trips: A systematic review. Gait & Posture, 100, 201–209. https://doi.org/10.1016/j.gaitpost.2022.12.015
Slijepcevic, D., Zeppelzauer, M., Unglaube, F., Kranzl, A., Breiteneder, C., & Horsak, B. (2023). Explainable Machine Learning in Human Gait Analysis: A Study on Children With Cerebral Palsy. IEEE Access, 11, 65906–65923. https://doi.org/10.1109/ACCESS.2023.3289986
Slijepcevic, D., Zeppelzauer, M., Unglaube, F., Kranzl, A., Breiteneder, C., & Horsak, B. (2023). Towards more transparency: The utility of Grad-CAM in tracing back deep learning based classification decisions in children with cerebral palsy. Gait & Posture, 100, 32–33. https://doi.org/10.1016/j.gaitpost.2022.11.045
Slijepcevic, D., Horst, F., Simak, M., Schöllhorn, W. I., Zeppelzauer, M., & Horsak, B. (2023). Towards personalized gait rehabilitation: How robustly can we identify personal gait signatures with machine learning? Gait & Posture, 106, S192–S193. https://doi.org/10.1016/j.gaitpost.2023.07.232