#Motor Rehabilitation

Das Forschungsgebiet Motor Rehabilitation an der FH St. Pölten entwickelt technologiegestützte Ansätze der Bewegungsrehabilitation und fördert deren breiten Einsatz in der klinischen Praxis durch die Kooperation mit Industriepartnerinnen und -partnern.

Projekte

ELSA - Evaluation of simple gait analysis devices

Evaluierung einfacher Messprinzipien zur Untersuchung des Behandlungserfolges bei der Rehabilitation nach Rekonstruktion des vorderen Kreuzbandes

HIPstar

Evaluierung der Genauigkeit verschiedener nicht-invasiver Methoden zur Bestimmung des Hüftgelenkszentrums für die klinische Ganganalyse bei übergewichtigen Kindern und Jugendlichen

Publikationen

Rind, A., Slijepcevic, D., Zeppelzauer, M., Unglaube, F., Kranzl, A., & Horsak, B. (2022). Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy. Proc. 2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX), 7–15. https://doi.org/10.1109/TREX57753.2022.00006
Slijepcevic, D., Horst, F., Lapuschkin, S., Horsak, B., Raberger, A.-M., Kranzl, A., Samek, W., Breitender, C., Schöllhorn, W., & Zeppelzauer, M. (2022). Explaining Machine Learning Models for Clinical Gait Analysis. ACM Transactions on Computing for Healthcare, 3(2), 14:1-14:27. https://doi.org/10/gnt2s9
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/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/https://doi.org/10.1016/j.gaitpost.2021.09.026
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/https://doi.org/10.1016/j.gaitpost.2021.09.120
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/https://doi.org/10.3389/fbioe.2021.780314
Horst, F., Slijepcevic, D., Simak, M., & Schöllhorn, W. I. (2021). Gutenberg Gait Database, a ground reaction force database of level overground walking in healthy individuals. Scientific Data, 8(1), 232. https://doi.org/https://doi.org/10.1038/s41597-021-01014-6
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/https://doi.org/10.1038/s41598-021-89763-7
Bernard, Jürgen, Hutter, M., Sedlmair, M., Zeppelzauer, Matthias, & Munzner, Tamara. (2021). A Taxonomy of Property Measures to Unify Active Learning and Human-centered Approaches to Data Labeling. ACM Transactions on Interactive Intelligent Systems (TiiS), 11(3–4), 1–42. https://doi.org/10/gnt2wf
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/https://doi.org/10.3390/children8040295

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