#Health Sciences

Under the heading of Health Sciences, the St. Pölten UAS combines expertise from the fields of dietetics, physiotherapy, healthcare and nursing, and digital healthcare in the research areas of Health Promotion & Healthy Ageing, Clinical & Healthcare Research and Education & Lifelong Learning for Health Professionals.

Projects

NextGen Nurse – Preparing Future Nursing Professionals Through XR-Enhanced Education

Tackling nursing education challenges by utilising a modern, digital, and scalable extended reality approach.

IMPROVE

Framework to IMPROVE the Integration of Patient Generated Health Data to Facilitate Value Based Healthcare.

MMOVE

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

SINN – Strengthening Inclusive and sustainable neighbourhoods

Fostering inclusive and healthy small and medium sized cities through community-driven research and activities.

CARING-AI

Culturally Adaptive and Responsive AI-Driven Social Assistive Robots for Inclusive Care

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.

E³UDRES² 2.0

Promoting Smart and Sustainable Regions as an Engaged and Entrepreneurial European University.

SIMNPACT

Simulation-based Learning Environments in Healthcare.

Institute of Health Sciences

XR2Resilience

An Extended Reality training program to improve the resilience of health care workers.

Publications

Höld, E., Rathmanner, T., & Heller, M. (2025). Teenage Body Image Perception, Body-shaping Behavior, and Body Composition With Respect to Use of “Fitspiration”: Exploratory Investigation Study. JMIR Formative Research, 9, e70964. https://doi.org/10.2196/70964

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

Höld, E., Größbacher, S., Kramml, P., Zeppelzauer, M., Prokop, B., Heller, M., & Rathmanner, T. (2025). Fit for #fitspiration: Human-Centred Design of a Blended Learning Course for Upper Secondary Schools. Technology, Knowledge and Learning. https://doi.org/10.1007/s10758-025-09871-5

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

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

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

Horst, F., Slijepcevic, D., Schöllhorn, W. I., Horsak, B., & Zeppelzauer, M. (2024). Explainable artificial intelligence for walking speed classification from vertical ground reaction forces. Gait & Posture, ESMAC Abstracts 2024, 113, 215–216. https://doi.org/10.1016/j.gaitpost.2024.07.232

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

News

10/15/2025