@article{wagner_kavagait_2018, title = {{KAVAGait}: {Knowledge}-{Assisted} {Visual} {Analytics} for {Clinical} {Gait} {Analysis}}, volume = {25}, url = {https://doi.org/10.1109/TVCG.2017.2785271}, doi = {10.1109/TVCG.2017.2785271}, abstract = {In 2014, more than 10 million people in the US were affected by an ambulatory disability. Thus, gait rehabilitation is a crucial part of health care systems. The quantification of human locomotion enables clinicians to describe and analyze a patient’s gait performance in detail and allows them to base clinical decisions on objective data. These assessments generate a vast amount of complex data which need to be interpreted in a short time period. We conducted a design study in cooperation with gait analysis experts to develop a novel Knowledge-Assisted Visual Analytics solution for clinical Gait analysis (KAVAGait). KAVAGait allows the clinician to store and inspect complex data derived during clinical gait analysis. The system incorporates innovative and interactive visual interface concepts, which were developed based on the needs of clinicians. Additionally, an explicit knowledge store (EKS) allows externalization and storage of implicit knowledge from clinicians. It makes this information available for others, supporting the process of data inspection and clinical decision making. We validated our system by conducting expert reviews, a user study, and a case study. Results suggest that KAVAGait is able to support a clinician during clinical practice by visualizing complex gait data and providing knowledge of other clinicians.}, number = {3}, journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)}, author = {Wagner, Markus and Slijepcevic, Djordje and Horsak, Brian and Rind, Alexander and Zeppelzauer, Matthias and Aigner, Wolfgang}, year = {2018}, note = {Projekt: KAVA-Time Projekt: IntelliGait Projekt: CARMA Projekt: DHLab}, keywords = {Center for Digital Health Innovation, DHLab, Design Study, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Digital Technologies, Forschungsgruppe Media Computing, Healthcare, Human Gait Analysis, Human-Computer Interaction, Institut für Creative Media Technologies, Institut für Gesundheitswissenschaften, Machine Learning, Media Computing Group, SP IGW Clinical \& Healthcare Research, Visual analytics, Wiss. Beitrag, best, best-bhorsak, best-lbaigner, best-lbwagnerm, best-mzeppelzauer, information visualization, knowledge generation, peer-reviewed}, pages = {1528--1542} }