#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

MODEL-CP

A multidisciplinary approach to minimize the need for surgical interventions in children with cerebral palsy.

MMOVE

A method to use the smartphone to check whether rehabilitation exercises are performed correctly.

PROGRESS

Validation of a Cost-Effective Smartphone-Based 3D Gait and Motion Analysis System for Capturing Movement Kinematics in Children with Cerebral Palsy.

ACCESS

Assessing clinically relevant biomechanical biomarkers in the field to predict physical functioning and health in patients with knee osteoarthritis: a nation-wide citizen science approach.

VReeze

Developing an Open-Source Virtual Reality for improving treatment of Freezing of Gait.

Trust AI - A platform for interactive and trustworthy artificial intelligence.

Explainable and trustworthy AI solution in an easy-to-use platform for SMEs.

EyeQTrack - Quantitative Eye-Tracking Analytics for Adaptive XR Training & Rehabilitation in Healthcare

A self-adaptive eXtended reality environment for training and therapeutic purposes.

Applied Biomechanics in Rehabilitation Research

Endowed professorship of the province of Lower Austria to strengthen and expand the research focus on motor rehabilitation

ULTRASKEL3D

Ultrasound-Based Skeletal 3D scans for Personalized Musculoskeletal Modelling.

deepForce - Pushing the limits of rapid estimation of knee joint contact force estimation in clinical gait analysis by Machine and Deep Learning

Machine learning methods to enable more clinicians to use data obtained by gait analyses for diagnosis.

Publications

Slijepčević, D., Klausner, L. D., Eigner, O., Ladner, S., Kietreiber, T., Belinskaya, Y., Kovac, F., Priebe, T., Judmaier, P., Litschka, M., & Zeppelzauer, M. (2026). TrustAI: Designing and Implementing a Trustworthy and User-Centered AI Platform. Proceedings of the 36th International Conference on Database and Expert Systems Applications – DEXA 2025 Workshops, CCIS, volume 2615, 15–29. https://doi.org/10.1007/978-3-032-02003-1_2

Bibtex

Slijepčević, D., Ladner, S., Judmaier, P., Zeppelzauer, M., Kranzl, A., & Horsak, B. (2025). AI applications and data annotation practices in clinical gait analysis: Initial insights from a survey of ESMAC and GAMMA members. Gait & Posture, ESMAC 2025 Abstract, 121, 229–230. https://doi.org/10.1016/j.gaitpost.2025.07.246

Krondorfer, P., Slijepčević, D., Kranzl, A., Zeppelzauer, M., & Horsak, B. (2025). Predicting joint contact forces using a combination of kinematics, anthropometrics, and demographics with explainable artificial intelligence. Gait & Posture, ESMAC 2025 Abstract, 121, 126–128. https://doi.org/10.1016/j.gaitpost.2025.07.139

Satriani, N., Slijepcevic, D., Schedl, M., & Zeppelzauer, M. (2025). Explanatory Interactive Machine Learning for Bias Mitigation in Visual Gender Classification. IEEE International Conference on Content-Based Multimedia Indexing.

Bibtex

Engelmann, B., Dumphart, B., Nehrer, S., Neubauer, M., Nowak, C., Wondrasch, B., & Horsak, B. (2025). ACCESS: Markerlose Bewegungsanalyse für die alltägliche Praxis – Neue Perspektiven bei Kniearthrose? Abstractband: 18. Forschungsforum der österreichischen Fachhochschulen : 7. - 8. Mai 2025, 38–40. https://doi.org/10.34895/hcw.0027

Matt, M., Sedlakova, J., Bernard, J., Zeppelzauer, M., & Waldner, M. (2025). Scalable Class-Centric Visual Interactive Labeling. Computers & Graphics, 104240. https://doi.org/10.1016/j.cag.2025.104240

Dindorf, C., Horst, F., Slijepcevic, D., Dumphart, B., Dully, J., Zeppelzauer, M., Horsak, B., & Fröhlich, M. (2024). Machine Learning in Biomechanics: Key Applications and Limitations in Walking, Running and Sports Movements (pp. 91–148). https://doi.org/10.1007/978-3-031-76047-1_4

Bibtex

Dindorf, C., Horst, F., Slijepčević, D., Dumphart, B., Dully, J., Zeppelzauer, M., Horsak, B., & Fröhlich, M. (2024). From lab to field with machine learning – Bridging the gap for movement analysis in real-world environments: A commentary. Current Issues in Sport Science (CISS), 9(4), 014–014. https://doi.org/10.36950/2024.4ciss014

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, ESMAC Abstracts 2024, 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, ESMAC Abstracts 2024, 113, 214–215. https://doi.org/10.1016/j.gaitpost.2024.07.231

News

10/15/2025