By using Artificial Intelligence, machines and programmes learn from experience, react to new and unforeseeable situations, and are able to solve complex tasks and put information in wider contexts – similar to humans.
Active Machine Learning for automatic identification of handwriting in 12th century manuscripts
Leveraging machine learning and predictive analytics for early detection of plant stress for the benefit of sustainability in farming
Platform for digital makers, for interaction, efficient sharing of knowledge & experiences and nationwide coordination of activities
Comprehensive service programme to increase the transformation capacity and transformation speed of SMEs in Eastern Austria with regard to digital innovations
Establishing advanced analysis methods for modelling, classification and similarity retrieval of gait patterns to enable novel data-driven ways to access 3D gait databases
Using everyday low-cost robot sensors and smart speech recognition in assistance services for elderly people
The „Laboratory for Capturing Motion and Augmenting Environment in Motor Rehabilitation“ will enable innovative projects and excellent research in Austria
Exploration of music therapeutic processes and relationships in selected areas of neurological rehabilitation
Developing methods for the analysis of large amounts of data without compromising data protection.
Algorithms, Law and Society: Decision Makers between Algorithmic Guidance and Personal Responsibility
Investigating non-discriminatory and employee-friendly implementation of algorithmic decision support systems in the workplace
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., Dumphart, B., Slijepcevic, D., & Zeppelzauer, M. (2020). Explainable Artificial Intelligence (XAI) und ihre Anwendung auf Klassifikationsprobleme in der Ganganalyse. In Abstractband des 3. GAMMA Kongress. München, Deutschland.
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.
Horsak, B., Schwab, C., & Kranzl, A. (2019). Reliability of stair walking kinematics in young overweight and obese individuals. Poster presented at the 28th Annual Meeting of the European Society for Movement Analysis in Adults and Children (ESMAC), Amsterdam, Netherlands.
Michelberger, F., & Grossberger, H. (2019). Inspiring and Hiring the Next Generation - Strategies for a Common Approach. Presented at the 5th UIC World Congress on Rail Training, Rabat, Morocco.
Koch, D., Despotovic, M., Sascha, L., Sakeena, M., Döller, M., & Zeppelzauer, M. (2019). Real Estate Image Analysis - A Literature Review. Real Estate Economics Journal, to Appear, 27(2), 269–300.
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