Motor Rehabilitation

Gait impairments and motor disorders are frequently occurring phenomena across all ages in Western countries. These conditions not only constitute an enormous socio-economic burden but also have an impact on the lives of those affected by them. Various methods exist for evaluating and diagnosing movement disorders, as this information is critical to developing further treatment strategies. The Center for Digital Health Innovation combines human motion analysis with machine learning, visual analytics and mixed reality.

In this context, advanced methods stemming from the fields of human motion analysis and applied biomechanics, such as motion capturing techniques, are state-of-the-art in clinical practice. This technology allows us to collect three-dimensional (3D) kinematic and kinetic measurements that aim at gathering precise quantitative information about the mechanics of the musculoskeletal system.

Technical advances, such as machine learning, visual analytics or mixed reality, offer new treatment options in motor rehabilitation beyond the scope of existing approaches. However, to enable a broad application in clinical practice, further effort is required to transfer technological innovations originating at academic institutions to the open market.

Research with key focus on motor rehabilitation  

Our focus on motor rehabilitation  pursues innovation through:

  • establishing a cutting-edge research facility at the UAS St. Pölten
  • improving motor rehabilitation by incorporating methods from the fields of machine learning, visual analytics and mixed reality
  • working closely together  with industrial partners in order to promote technological transfer to the market
  • ensuring the transfer of knowledge between R&D and teaching,
  • establishing contact with international scientific networks and project activities
FH-Prof. Dr. Brian Horsak

Key Focus Coordinator

 

Our research aims at introducing new technology-driven approaches to support motor rehabilitation and promote a broad application in clinical practice by collaborating with industrial partners.

Key Focus Coordinator

  • Senior Researcher Institute of Health Sciences
  • Department of Health Sciences
P: +43/676/847 228 587

Projects

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

HIPstar

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

Evaluating technical measuring accuracy

Evaluating the technical measuring accuracy of a 3D monitoring system for the continuous tracking of lumbar spine movements

The Children's KNEEs Study

The aim of the Children’s KNEEs Study is to analyze altered biomechanical movement strategies in obese children during walking and stair climbing and to develop a specific training program for them.

Publications

Horst, F., Slijepcevic, D., Lapuschkin, S., Raberger, A.-M., Zeppelzauer, M., Samek, W., … Horsak, B. (2020). On the Understanding and Interpretation of Machine Learning Predictions in Clinical Gait Analysis Using Explainable Artificial Intelligence. Frontiers in Bioengineering and Biotechnology, Submitted.
Horsak, B., Slijepcevic, D., Raberger, A.-M., Schwab, C., & Zeppelzauer, M. (2020). GaitRec, a large-scale walking GRF dataset for a healthy cohort and patients with musculo-skeletal impairments. Scientific Data, Submitted.
Slijepcevic, D., Zeppelzauer, M., Raberger, A.-M., Breitender, C., Horsak, B., & Horsak, Brian. (2020). Input Representations and Classification Strategies for Automated Human Gait Analysis. Gait & Posture, 76, 198–203.
Horsak, B., Schwab, C., Baca, A., Greber-Platzer, S., Kreissl, A., Nehrer, S., … Wondrasch, B. (2019). Effects of a lower extremity exercise program on gait biomechanics and clinical outcomes in children and adolescents with obesity: A randomized controlled trial. Gait & Posture, ePub ahead of print. https://doi.org/10.1016/j.gaitpost.2019.02.032
Slijepcevic, D., Raberger, A.-M., Zeppelzauer, M., Dumphart, B., Breiteneder, C., & Horsak, B. (2019). On the usefulness of statistical parameter mapping for feature selection in automated gait classification. In Book of Abstracts of the 25th Conference of the European Society of Biomechanics (ESB) (p. 1). Vienna, Austria.
Wondrasch, B., Raberger, A.-M., & Endres, C. (2019). Motor Learning in Knee Osteoarthritis Therapy - A new Rehabilitation Approach. Presented at the International Society of Athroscopy, Knee Surgery and Orthopaedic Sports Medicine, Cancún, Mexiko.
Wondrasch, B. (2019). Prävention von Knorpelverletzungen und Arthrose. In S. Nehrer, V. Valderrabano, & M. Engelhardt (Eds.), Knorpel und Arthrose im Sport. Krems, Österreich.
Wondrasch, B. (2019). Rehabilitation und Back to sports nach Knorpelschaden. In S. Nehrer, V. Valderrabano, & M. Engelhardt (Eds.), Knorpel und Arthrose im Sport. Krems, Österreich.
Horsak, B. (2019). Reliabilität von Messergebnissen in der Gang- und Bewegungsanalyse – Erfahrungsbericht zu gängigen Maßzahlen. Invited talk presented at the GAMMA Workshop im Rahmen des 11. Kongress der Deutschen Gesellschaft für Biomechanik, Berlin.
Rind, A., Wagner, M., & Aigner, W. (2019). Towards a Structural Framework for Explicit Domain Knowledge in Visual Analytics. In Proc. IEEE Workshop on Visual Analytics in Healthcare (VAHC) (pp. 33–40). https://doi.org/10.1109/VAHC47919.2019.8945032