Title (eng): Video tutorial for KAVAGait
Wagner, M. (Markus)
Aigner, W. (Wolfang)
Zeppelzauer, M. (Matthias)
Rind, A. (Alexander)
Horsak, B. (Brian)
Slijepčević, D. (Djordje)
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.
However, 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 along the clinicians needs. Additionally, an explicit knowledge store (EKS) allows storage of implicit knowledge from clinicians and makes these 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 every day clinical practice in visualizing complex gait data and providing knowledge of other clinicians.
Object languages: English
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Classification: design study; interface design; knowledge generation; knowledge-assisted; visualization; visual analytics; gait analysis
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