  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.
