#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

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.

O3DGA – The Use of Machine Learning as a Supportive Measure in Clinical Three-Dimensional Gait Analysis

Optimisation of time-consuming and error-prone processes in three-dimensional gait analysis.

VeRgonomiX III

Education and training of ergonomics in health and safety.

Publications

Horst, F., Hoitz, F., Slijepcevic, D., Schons, N., Beckmann, H., Nigg, B. M., & Schöllhorn, W. I. (2023). Identification of subject-specific responses to footwear during running. Scientific Reports, 13(1), 11284. https://doi.org/10.1038/s41598-023-38090-0

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

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

Vulpe-Grigorasi, A. (2023). Cognitive load assessment based on VR eye-tracking and biosensors. Proceedings of the 22nd International Conference on Mobile and Ubiquitous Multimedia, 589–591. https://doi.org/10.1145/3626705.3632618

Vulpe-Grigorasi, A. (2023). Multimodal machine learning for cognitive load based on eye tracking and biosensors. 2023 Symposium on Eye Tracking Research and Applications, 1–3. https://doi.org/10.1145/3588015.3589534

Slijepcevic, D., Horst, F., Simak, M., Lapuschkin, S., Raberger, A. M., Samek, W., Breiteneder, C., Schöllhorn, W. I., Zeppelzauer, M., & Horsak, B. (2022). Explaining machine learning models for age classification in human gait analysis. Gait & Posture, 97, S252–S253. https://doi.org/10.1016/j.gaitpost.2022.07.153

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/10.1145/3474121

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

Leung, V., Simone Hofbauer, Leonhartsberger, J., Kee, C., Liang, Y., & Schmied, R. (2022, 05). Influence of education systems on children’s visual behaviours as an environmental risk factor for myopia: a quantitative analysis with LIDAR-sensor tracking in classrooms. 18th International Myopia Conference, Rotterdam.

Bibtex

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

News

12/19/2024