Many people are affected by dysfunctions or abnormalities in their gait pattern, for example, due to functional deficits. In order to support attending therapists during their therapy, St. Pölten UAS is developing tools to facilitate the diagnosis of gait dysfunctions based on complex measurement data. The research project KAVAGait (Knowledge-Assisted Visual Analytics for Clinical Gait Analysis) makes it possible for therapists to store, search for, visualise and share data with colleagues. This should enable therapists to work more efficiently in the future and better network their knowledge about gait dysfunctions.
Sharing gait-analysis data
"The system includes innovative and interactive visual interfaces, which have been developed based on the needs of attending therapists. In addition, a database allows for the storage of the implicit knowledge of therapists. It can provide valuable information for other therapeutic practitioners and significantly assist the clinical decision-making process", explained Wolfgang Aigner, Head of the Institute of Creative\Media/Technologies at St. Pölten UAS.
KAVAGait is a comprehensive initiative of two research projects at St. Pölten UAS, "IntelliGait: Intelligent Gait Analysis” and "KAVA-Time: Knowledge-Assisted Visual Analytics Methods for Time-Oriented Data”. IntelliGait researches intelligent gait-pattern analyses for the detection of gait dysfunctions. KAVA-Time has developed methods for better analysis and visual processing of data, which can be applied to a variety of subject areas. Good collaboration between human and machine is important. "Visual analytics lets computers do what they do best such as finding clusters in large amounts of data. However, humans are better at recognising visual patterns and dealing with uncertainties and contradictions", said Aigner.
KAVAGait will be presented to an audience of experts at the end of October as part of the IEEE VIS conference in Berlin. The article on KAVAGait was published in the scientific journal “IEEE Transactions on Visualization and Computer Graphics”, a top journal for visual analytics and is available as an open-access publication: https://doi.org/10.1109/TVCG.2017.2785271
- Video about the publication
- Information on other interdisciplinary projects can be found on the website of the Center for Digital Health Innovation at St. Pölten UAS.
Project KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis
The project is part of the project "KAVA-Time: Knowledge-Assisted Visual Analytics Methods for Time-Oriented Data" and is funded by the FWF - Fond zur Förderung der wissenschaftlichen Forschung (The Austrian Science Fund) (P25489-N23).
The project is funded by the NFB - NÖ Forschungs- und Bildungsges.m.b.H. (Lower Austrian Research and Education Association) through the Life Science Call 2014. Partners are: the Department of Biomechanics, Kinesiology and Computer Science in Sport of the Department of Sports Science at the University of Vienna and AUVA - die Allgemeine Unfallversicherungsanstalt (General Accident Insurance Institution).