#Motor Rehabilitation

The research area of Motor Rehabilitation at the St. Pölten UAS develops technology-assisted approaches to physical rehabilitation and promotes their widespread use in clinical practice by cooperating with industrial partners.

Projects

HIPstar

Evaluation of the accuracy of non-invasive hip joint centre estimation methods for clinical gait analysis in children and adolescents

ELSA- Evaluation of simple gait analysis devices

Evaluation of the effectiveness of rehabilitation measures after reconstruction of the anterior cruciate ligament using simplified gait analysis

Publications

Horsak, B., Simonlehner, M., Schöffer, L., Dumphart, B., Jalaeefar, A., & Husinsky, M. (2021). Overground walking in a fully immersive Virtual Reality: Preliminary results of a comprehensive study on the effects on walking biomechanics. Gait & Posture, 90, 100–101. https://doi.org/10.1016/j.gaitpost.2021.09.051
Schwab, C., Durstberger, S., Kainz, H., Baca, A., Thajer, A., Greber-Platzer, S., Ilse, J., Horsak, B., & Kranzl, A. (2021). Accuracy of 3-dimensional freehand ultrasound to estimate anatomical landmarks in children and adolescents with obesity. Gait & Posture, 90, 232–233. https://doi.org/10.1016/j.gaitpost.2021.09.120
Krondorfer, P., Slijepčević, D., Unglaube, F., Kranzl, A., Breiteneder, C., Zeppelzauer, M., & Horsak, B. (2021). Deep learning-based similarity retrieval in clinical 3D gait analysis. Gait & Posture, 90, 127–128. https://doi.org/10.1016/j.gaitpost.2021.09.066
Dumphart, B., Slijepčević, D., Unglaube, F., Kranzl, A., Baca, A., Zeppelzauer, M., & Horsak, B. (2021). An automated deep learning-based gait event detection algorithm for various pathologies. Gait & Posture, 90, 50–51. https://doi.org/10.1016/j.gaitpost.2021.09.026
Horsak, B., Schwab, C., Durstberger, S., Thajer, A., Greber-Platzer, S., Kainz, H., Jonkers, I., & Kranzl, A. (2021). 3D free-hand ultrasound to register anatomical landmarks at the pelvis and localize the hip joint center in lean and obese individuals. Scientific Reports, 11(1), 10650. https://doi.org/10/gkgk2t
Thajer, A., Skacel, G., Truschner, K., Jorda, A., Vasek, M., Horsak, B., Strempfl, J., Kautzky-Willer, A., Kainberger, F., & Greber-Platzer, S. (2021). Comparison of Bioelectrical Impedance-Based Methods on Body Composition in Young Patients with Obesity. Children, 8(4), 295. https://doi.org/10/gjq3dn
Horsak, B., Simonlehner, M., Schöffer, L., Maureder, J., Schwab, C., Raberger, A. M., Zeller, M., & Husinsky, M. (2020). Applicability and usability of an immersive virtual reality-based balance control exergame for prosthetic users: A pilot study with healthy individuals. Gait & Posture. ESMAC 29th Annual Meeting, Virtual Meeting. https://doi.org/10.1016/j.gaitpost.2020.07.113
Horsak, B., Dumphart, B., Slijepcevic, D., & Zeppelzauer, M. (2020). Explainable Artificial Intelligence (XAI) und ihre Anwendung auf Klassifikationsprobleme in der Ganganalyse. Abstractband Des 3. GAMMA Kongress. 3. GAMMA Kongress, München, Deutschland.
Horst, F., Slijepcevic, D., Zeppelzauer, M., Raberger, A. M., Lapuschkin, S., Samek, W., Schöllhorn, W. I., Breiteneder, C., & Horsak, B. (2020). Explaining automated gender classification of human gait. Gait & Posture, 81, 159–160. https://doi.org/10.1016/j.gaitpost.2020.07.114
Horsak, B., Slijepcevic, D., Raberger, A.-M., Schwab, C., Worisch, M., & Zeppelzauer, M. (2020). GaitRec, a large-scale ground reaction force dataset of healthy and impaired gait. Scientific Data, 7:143(1), 1–8. https://doi.org/10/gh372d

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